Brainqub3 Agent Labs — CODITECT Impact Analysis

Analysis Date: 2026-02-16 Author: Claude (Sonnet 4.5) Technology Evaluated: Brainqub3 Agent Labs v0.1.0 (arXiv:2512.08296) Integration Target: CODITECT Autonomous Development Platform

Executive Summary

Brainqub3 Agent Labs is an open-source measurement rig for agent architecture scaling analysis that provides empirical validation of multi-agent system (MAS) performance. It offers CODITECT a pre-deployment architecture validation capability that transforms orchestration pattern selection from heuristic-based to evidence-based decision-making.

Strategic Value: Enables CODITECT to empirically validate which of its 5 workflow patterns (chaining, routing, parallelization, orchestrator-workers, evaluator-optimizer) performs best for specific regulated industry task classes before production deployment.

Integration Complexity: Medium — requires adapter layer for multi-tenancy, compliance hooks, and OTEL observability integration. Core measurement capabilities are directly usable.

Compliance Gap: Significant — no built-in support for FDA 21 CFR Part 11, HIPAA, or SOC2 requirements. Requires wrapper layer for audit trails, e-signatures, and PHI detection.

Recommendation: Adopt as Architecture Validation Service in CODITECT's control plane with custom compliance and multi-tenancy adapters. Do NOT use in data plane runtime path.

Integration Architecture

Control Plane Placement

Agent Labs operates as a testing and measurement tool — it sits in the control plane as an architecture validation service, NOT in the data plane. In CODITECT's architecture:

┌─────────────────────────────────────────────────────────────┐
│ CODITECT Control Plane                                      │
│                                                              │
│  ┌──────────────────────┐         ┌──────────────────────┐ │
│  │ Agent Orchestrator   │         │ Brainqub3 Agent Labs │ │
│  │                      │◄────────┤ Architecture         │ │
│  │ Pattern Selector     │ validate│ Validator            │ │
│  │ - Chaining           │         │ - Arena              │ │
│  │ - Routing            │         │ - Scaling Model      │ │
│  │ - Parallelization    │         │ - Coordination       │ │
│  │ - Orchestrator-Workers│        │   Metrics            │ │
│  │ - Evaluator-Optimizer │        └──────────────────────┘ │
│  └──────────────────────┘                                  │
└─────────────────────────────────────────────────────────────┘

Role: Pre-deployment architecture validation — before CODITECT selects an orchestration pattern for a task class, Agent Labs empirically validates which pattern performs best.

Runtime: Offline calibration tool, not inline request-path component. Operates during:

Initial task class onboarding (e.g., "FDA submission review" task class)
Quarterly architecture recalibration
Post-incident root cause analysis (architecture selection validation)

Data Plane Integration Points

While Agent Labs does NOT execute in the data plane, its outputs feed critical runtime components:

Agent Labs Output	CODITECT Component	Integration Mechanism
Coordination overhead_pct	Circuit Breaker	Threshold calibration
Token cost per architecture	Token Budget Controller	Budget allocation
Error amplification Ae	Circuit Breaker	Error cascade detection
Efficiency_Ec metrics	Pattern Selector	Runtime pattern switching
Run telemetry	Observability Stack	OTEL span attributes

Example: If Agent Labs measures that centralised architecture has 23% overhead for "clinical trial analysis" tasks, CODITECT's Circuit Breaker sets a 30% overhead threshold (with margin) for that pattern. If runtime overhead exceeds 30%, circuit breaker triggers pattern fallback.

Architectural Boundary

Agent Labs measures coordination costs empirically
    ↓
Outputs: overhead_pct, message_density, redundancy, efficiency, error_amp
    ↓
CODITECT ingests as configuration parameters
    ↓
CODITECT runtime uses parameters for circuit breaker thresholds,
token budgets, pattern selection confidence scores

Agent Labs is measurement infrastructure, not execution infrastructure.

Multi-Tenancy & Isolation

Current State: Single-Tenant, Local-First

Agent Labs v0.1.0 is designed for single-tenant research use:

Storage: SQLite database (arena_telemetry.db) with no tenant partitioning
Runs: File-based in runs/{run_id}/ directories with no namespace isolation
Dashboard: Single HTML dashboard with no tenant filtering
API: No REST API, no multi-tenant authentication

CODITECT Requirement: Multi-Tenant SaaS

CODITECT operates as a multi-tenant SaaS platform with:

Row-level tenant isolation in PostgreSQL
Namespace-based file storage (S3 with tenant prefix)
RBAC with tenant-scoped permissions
Audit trails with tenant attribution

Gap Analysis

Requirement	Agent Labs Current	Gap Severity
Tenant-isolated data storage	Single SQLite, no tenant_id	High
Namespace-isolated runs	Flat `runs/` directory	High
Tenant-scoped API access	No API	Medium
Audit trail with tenant attribution	No tenant awareness	High
Multi-tenant dashboard	Single global view	Low

Mitigation Strategy

Option 1: Run-Level Tenant ID (Minimal)

# Add tenant_id to run_config.json
{
  "run_id": "mas_centralised_20260216_143052",
  "tenant_id": "avivatec",  # NEW
  "architecture": "centralised",
  ...
}

Store runs in tenant-prefixed directories: runs/{tenant_id}/{run_id}/
Filter dashboard by tenant_id query parameter
Risk: Low implementation complexity, but weak isolation (relies on directory naming)

Option 2: PostgreSQL Backend (Recommended for CODITECT)

CREATE TABLE runs (
  run_id TEXT PRIMARY KEY,
  tenant_id TEXT NOT NULL,  -- Partition key
  architecture TEXT,
  created_at TIMESTAMPTZ,
  ...
);

CREATE INDEX idx_runs_tenant ON runs(tenant_id);

Replace SQLite with PostgreSQL with row-level security (RLS)
Reuse CODITECT's existing multi-tenant database infrastructure
Risk: Medium implementation complexity, but production-ready isolation

Recommended: Option 2 (PostgreSQL) for CODITECT production deployment. Option 1 acceptable for proof-of-concept.

Compliance Surface

Audit Trail

Strong:

Every run produces immutable artifacts with content hashes:
- run_config.json (input specification)
- derived_metrics.json (measurement outputs)
- run_manifest.json (artifact inventory with SHA-256 hashes)
Agent traces captured per-instance:
- Tool calls with timestamps, inputs, outputs
- Inter-agent messages with sender/receiver/content
- Orchestrator decisions with rationale
Run immutability enforced by content hashing (tampering detectable)

Gaps:

No e-signature support for regulatory compliance gates
No data classification or PHI detection in task instances
No automated validation documentation generation (IQ/OQ/PQ)
No 21 CFR Part 11 Subpart B electronic signature workflow

FDA 21 CFR Part 11 Compliance

Requirement	Agent Labs Support	Gap
§11.10(a) Validation	Run immutability + reproducibility	No IQ/OQ/PQ templates
§11.10(b) Audit Trail	Run artifacts with hashes	No e-signature on approval
§11.10(c) System Checks	Arena evaluator enforcement	No PHI detection
§11.10(e) Data Integrity	Content hashes (SHA-256)	✅ Compliant
§11.50 Signature Manifestations	None	Missing e-signature UI
§11.70 Signature Linking	None	Missing signature binding

Critical Gap: Agent Labs can produce compliant audit artifacts, but lacks the approval workflow and electronic signature capture required for regulated use.

Mitigation:

# CODITECT wrapper for FDA compliance
class FDACompliantArchitectureValidator:
    def run_validation(
        self,
        task_spec: TaskSpec,
        approver: User,
        justification: str
    ) -> ValidationResult:
        # 1. Run Agent Labs arena
        arena_result = self.arena.run(task_spec)

        # 2. Capture e-signature
        signature = self.signature_service.capture(
            signer=approver,
            intent="Architecture validation approval",
            data_hash=arena_result.manifest_hash,
            justification=justification
        )

        # 3. Store with signature binding
        self.audit_log.record(
            event="architecture_validation_approved",
            artifacts=arena_result.artifacts,
            signature=signature,
            timestamp=utcnow()
        )

        return ValidationResult(
            arena_result=arena_result,
            signature=signature,
            compliant_with="21 CFR Part 11"
        )

HIPAA Compliance

PHI Risk: If Agent Labs runs use real patient data in task instances (e.g., "Summarize this clinical trial report"), PHI may be present in:

Task instance data (task_instance.json)
Agent traces (tool call inputs/outputs)
Evaluator feedback
Dashboard visualizations

Current State: No PHI detection, no encryption at rest, no access logging.

Mitigation Requirements:

PHI Detection: Scan task instances for PII/PHI before run execution
Encryption at Rest: Encrypt run artifacts with tenant-specific keys
Access Logging: Log all access to run artifacts with user attribution
Minimum Necessary: Redact PHI in dashboard views (show only de-identified summaries)

CODITECT Integration:

class HIPAACompliantValidator:
    def validate_task_instance(self, instance: TaskInstance) -> ValidationResult:
        # 1. PHI detection
        phi_findings = self.phi_scanner.scan(instance.data)
        if phi_findings:
            raise PHIDetectedError(
                "Task instance contains PHI. Use de-identified data for validation."
            )

        # 2. Encrypt at rest
        encrypted_artifacts = self.encryption_service.encrypt(
            artifacts=arena_result.artifacts,
            key_id=tenant.encryption_key_id
        )

        # 3. Access logging
        self.access_log.record(
            event="validation_artifacts_accessed",
            user=current_user,
            artifacts=encrypted_artifacts.keys(),
            timestamp=utcnow()
        )

SOC2 Compliance

Control	Agent Labs Support	Gap
CC6.1 Logical Access Controls	None (file system permissions)	No RBAC
CC6.6 Audit Logging	Run artifacts	No user access logs
CC7.2 System Monitoring	SQLite telemetry	No SIEM integration
CC8.1 Change Management	Git version control	No approval workflow
A1.2 Data Classification	None	No data labels

Risk Level: Medium — Agent Labs produces audit artifacts (evidence for CC6.6), but lacks access controls (CC6.1) and monitoring integration (CC7.2).

Mitigation: Wrap Agent Labs in CODITECT's existing SOC2-compliant infrastructure:

Access Control: Enforce RBAC before allowing arena runs
Audit Logging: Send all arena events to CODITECT's audit log service
Change Management: Require approval workflow for new task definitions

Observability

Current State

Built-In Capabilities:

SQLite telemetry store with structured events:
- run_started, run_completed, run_failed
- agent_message, tool_called, inter_agent_message
- orchestrator_decision, eval_result
CSV/Parquet export for run summaries
HTML dashboard with real-time refresh (SQLite polling)
Python logging module (no structured logging)

Event Schema Example:

{
  "event_type": "agent_message",
  "run_id": "mas_centralised_20260216_143052",
  "timestamp": "2026-02-16T14:30:52.123Z",
  "agent_id": "agent_0",
  "message": "Analyzing clinical trial data...",
  "metadata": {
    "turn_index": 3,
    "tokens_used": 487
  }
}

CODITECT Observability Stack

CODITECT uses:

OpenTelemetry (OTEL) for distributed tracing
Prometheus for metrics (token usage, latency, error rates)
Grafana for dashboards
Elasticsearch for log aggregation
Structured logging (JSON format with trace IDs)

Gap Analysis

Requirement	Agent Labs Current	Gap Severity
OTEL span export	None	High
Prometheus metrics	None	High
Structured logging (JSON)	Python logging (text)	Medium
Trace ID propagation	None	High
Log aggregation (Elasticsearch)	SQLite + CSV	Medium

Integration Strategy

Phase 1: OTEL Span Wrapper

from opentelemetry import trace
from opentelemetry.trace import Status, StatusCode

tracer = trace.get_tracer(__name__)

class OTELArenaWrapper:
    def run(self, task_spec: TaskSpec, config: ArenaConfig) -> ArenaResult:
        with tracer.start_as_current_span(
            "agent_labs.arena.run",
            attributes={
                "arena.run_id": config.run_id,
                "arena.architecture": config.architecture,
                "arena.task_name": task_spec.name,
                "arena.n_agents": config.n_agents,
                "arena.model": config.model,
            }
        ) as span:
            try:
                result = self.arena.run(task_spec, config)

                # Add result attributes
                span.set_attributes({
                    "arena.delta_vs_sas": result.delta_vs_sas,
                    "arena.overhead_pct": result.overhead_pct,
                    "arena.efficiency_ec": result.efficiency_ec,
                    "arena.tokens_used": result.total_tokens,
                })

                span.set_status(Status(StatusCode.OK))
                return result

            except Exception as e:
                span.set_status(Status(StatusCode.ERROR, str(e)))
                span.record_exception(e)
                raise

Phase 2: Prometheus Metrics

from prometheus_client import Counter, Histogram, Gauge

# Metrics
arena_runs_total = Counter(
    "agent_labs_arena_runs_total",
    "Total arena runs executed",
    ["architecture", "task_name", "status"]
)

arena_delta_vs_sas = Histogram(
    "agent_labs_arena_delta_vs_sas",
    "Performance delta vs. SAS baseline",
    ["architecture", "task_name"],
    buckets=(-1.0, -0.5, -0.2, 0.0, 0.2, 0.5, 1.0, 2.0)
)

arena_overhead_pct = Gauge(
    "agent_labs_arena_overhead_pct",
    "Coordination overhead percentage",
    ["architecture", "task_name"]
)

# Instrumentation
def run_with_metrics(task_spec, config):
    try:
        result = arena.run(task_spec, config)

        arena_runs_total.labels(
            architecture=config.architecture,
            task_name=task_spec.name,
            status="success"
        ).inc()

        arena_delta_vs_sas.labels(
            architecture=config.architecture,
            task_name=task_spec.name
        ).observe(result.delta_vs_sas)

        arena_overhead_pct.labels(
            architecture=config.architecture,
            task_name=task_spec.name
        ).set(result.overhead_pct)

        return result

    except Exception as e:
        arena_runs_total.labels(
            architecture=config.architecture,
            task_name=task_spec.name,
            status="error"
        ).inc()
        raise

Phase 3: Structured Logging

import structlog

logger = structlog.get_logger()

def run_with_structured_logging(task_spec, config):
    logger.info(
        "arena.run.started",
        run_id=config.run_id,
        architecture=config.architecture,
        task_name=task_spec.name,
        n_agents=config.n_agents
    )

    try:
        result = arena.run(task_spec, config)

        logger.info(
            "arena.run.completed",
            run_id=config.run_id,
            delta_vs_sas=result.delta_vs_sas,
            overhead_pct=result.overhead_pct,
            tokens_used=result.total_tokens
        )

        return result

    except Exception as e:
        logger.error(
            "arena.run.failed",
            run_id=config.run_id,
            error=str(e),
            exc_info=True
        )
        raise

Implementation Complexity: Medium — requires wrapping arena execution with OTEL/Prometheus/structlog instrumentation. Core Agent Labs code unchanged.

Multi-Agent Orchestration Fit

Direct Mapping to CODITECT Patterns

CODITECT implements 5 workflow patterns (ADR-087):

Agent Labs Architecture	CODITECT Pattern	Mapping Quality	Notes
SAS (single agent)	Augmented LLM	✅ Direct	Baseline comparison
Independent (parallel workers + majority vote)	Parallelization	✅ Direct	Perfect match
Centralised (workers + orchestrator synthesis)	Orchestrator-Workers	✅ Direct	Perfect match
Decentralised (peer exchange + consensus)	Evaluator-Optimizer variant	⚠️ Partial	No iterative refinement loop
Hybrid (assignment + peer rounds + synthesis)	Full Agent Loop	⚠️ Close	Missing checkpoint gates

Additional CODITECT Patterns Not Covered:

Chaining (sequential tasks with handoffs) — no equivalent in Agent Labs
Routing (conditional branching based on classification) — no equivalent in Agent Labs

Implications:

Agent Labs can directly validate 3 of 5 CODITECT patterns (60% coverage)
Missing patterns (chaining, routing) require custom task definitions in Agent Labs
CODITECT's Full Agent Mode (checkpoint-gated) requires Agent Labs extension to inject human-in-the-loop gates

Pattern Extension Requirements

Chaining Pattern:

# Custom Agent Labs task for chaining validation
class ChainedTask(Task):
    def __init__(self):
        self.stages = [
            Stage("extract", tools=["file_reader"]),
            Stage("transform", tools=["data_processor"]),
            Stage("validate", tools=["schema_checker"])
        ]

    def run_mas(self, agents: list[Agent]) -> TaskResult:
        # Assign one agent per stage, measure handoff overhead
        results = []
        context = {}

        for stage, agent in zip(self.stages, agents):
            result = agent.execute(stage, context)
            context[stage.name] = result  # Handoff
            results.append(result)

        # Measure: latency, handoff overhead, cumulative error
        return self.evaluate_chain(results)

Routing Pattern:

# Custom Agent Labs task for routing validation
class RoutingTask(Task):
    def run_mas(self, agents: list[Agent]) -> TaskResult:
        # Agent 0: Router (classifies task type)
        task_type = agents[0].classify(self.input)

        # Agents 1-N: Specialists (routed based on classification)
        specialist = self.select_specialist(task_type, agents[1:])
        result = specialist.execute(self.input)

        # Measure: routing accuracy, specialist efficiency
        return self.evaluate_routing(task_type, result)

Checkpoint-Gated Workflow:

# Extension to Agent Labs hybrid architecture
class CheckpointGatedHybrid(HybridArchitecture):
    def execute(self, task: Task, agents: list[Agent]) -> TaskResult:
        # Round 1: Independent work
        proposals = [agent.propose(task) for agent in agents]

        # CHECKPOINT: Human approval gate (CODITECT-specific)
        if not self.checkpoint_manager.approve(proposals):
            return TaskResult(status="rejected_at_checkpoint")

        # Round 2: Peer exchange
        refined = self.peer_exchange(proposals, agents)

        # CHECKPOINT: Compliance gate
        if not self.compliance_checker.validate(refined):
            return TaskResult(status="compliance_failure")

        # Round 3: Orchestrator synthesis
        final = self.orchestrator.synthesize(refined)

        return TaskResult(output=final, checkpoints_passed=2)

Recommendation: Extend Agent Labs with custom task definitions for chaining and routing patterns. Contribute back to upstream if generalizable.

Checkpoint Integration

Current State: No Checkpoints

Agent Labs v0.1.0 executes arena runs to completion with no pause points:

SAS runs: single agent execution → evaluation → done
MAS runs: multi-agent execution → evaluation → done
No human-in-the-loop gates
No compliance approval steps
No rollback/resume mechanism

CODITECT Requirement: Checkpoint-Gated Workflows

CODITECT's Checkpoint Manager (ADR-092) enforces mandatory approval gates in regulated workflows:

Pre-execution checkpoint: Validate task specification before agent execution
Mid-execution checkpoint: Review intermediate outputs (e.g., after data extraction, before analysis)
Pre-submission checkpoint: Final compliance review before deliverable generation

Example: FDA submission review workflow

Task: "Generate 510(k) submission package"
  ↓
Checkpoint 1: Validate input documents (human approval)
  ↓
Agent execution: Extract claims from predicate device
  ↓
Checkpoint 2: Review extracted claims (human approval)
  ↓
Agent execution: Generate substantial equivalence comparison
  ↓
Checkpoint 3: Compliance review (automated + human)
  ↓
Deliverable: 510(k) submission PDF

Integration Strategy

Option 1: Post-Hoc Checkpoint Injection (Non-Invasive)

Run Agent Labs without checkpoints, then simulate checkpoint delays in cost model:

class CheckpointAwareArena(Arena):
    def run(self, task_spec: TaskSpec, config: ArenaConfig) -> ArenaResult:
        # Standard arena run
        result = super().run(task_spec, config)

        # Simulate checkpoint overhead
        checkpoint_delays = self.estimate_checkpoint_delays(
            task_spec=task_spec,
            architecture=config.architecture,
            n_checkpoints=config.expected_checkpoints
        )

        # Adjust metrics
        result.total_time_sec += sum(checkpoint_delays)
        result.overhead_pct += self.checkpoint_overhead_pct(checkpoint_delays)

        return result

Pros: Zero changes to Agent Labs core Cons: Checkpoint delays are simulated, not measured empirically

Option 2: Inline Checkpoint Gates (Invasive)

Extend Agent Labs architectures to pause at checkpoint boundaries:

class CheckpointGatedCentralised(CentralisedArchitecture):
    def execute(self, task: Task, agents: list[Agent]) -> TaskResult:
        # Phase 1: Worker execution
        worker_outputs = [agent.execute(task) for agent in self.workers]

        # CHECKPOINT: Review worker outputs
        checkpoint_result = self.checkpoint_manager.request_approval(
            checkpoint_id="worker_outputs_review",
            data=worker_outputs,
            approver_role="subject_matter_expert"
        )

        if not checkpoint_result.approved:
            return TaskResult(
                status="rejected_at_checkpoint",
                checkpoint_id="worker_outputs_review",
                rejection_reason=checkpoint_result.reason
            )

        # Phase 2: Orchestrator synthesis
        synthesized = self.orchestrator.synthesize(worker_outputs)

        return TaskResult(output=synthesized, checkpoints_passed=1)

Pros: Empirical measurement of checkpoint impact on coordination Cons: Requires Agent Labs architecture modifications

Recommendation: Option 1 (post-hoc simulation) for CODITECT proof-of-concept. Option 2 (inline gates) for production deployment if checkpoint overhead measurement is critical.

Checkpoint Overhead Estimation

Key questions for CODITECT:

How much does a checkpoint delay MAS execution? (e.g., +5 min human review per checkpoint)
Does checkpoint placement affect coordination efficiency? (e.g., checkpoints between peer exchange rounds disrupt consensus)
What is the optimal checkpoint granularity? (e.g., 1 checkpoint per 3 agent turns vs. 1 checkpoint per round)

Agent Labs can answer these if checkpoint gates are implemented inline (Option 2). Otherwise, rely on post-hoc simulation (Option 1).

Circuit Breaker Integration

Agent Labs Contribution: Error Amplification Metric

Agent Labs measures error amplification (Ae) — the factor by which errors propagate through multi-agent coordination:

Ae = (MAS error rate) / (SAS error rate)

Example:

SAS error rate: 5% (1 in 20 tasks fail)
MAS error rate (centralised): 15% (3 in 20 tasks fail)
Error amplification Ae = 15% / 5% = 3.0x

Interpretation: Centralised architecture amplifies errors by 3x due to coordination overhead (orchestrator misinterprets worker outputs, workers produce conflicting outputs, etc.).

CODITECT Circuit Breaker

CODITECT's Circuit Breaker (ADR-089) monitors runtime error rates and triggers fallback patterns when error thresholds are exceeded:

Closed (normal operation)
  ↓ error rate > threshold
Half-Open (probationary)
  ↓ continued errors
Open (fallback to simpler pattern)

Threshold Calibration Problem: What is the "right" error rate threshold for each architecture pattern?

Integration: Agent Labs as Threshold Calibrator

class CircuitBreakerCalibrator:
    def __init__(self, agent_labs_results: dict[str, ArenaResult]):
        self.results = agent_labs_results

    def calibrate_thresholds(
        self,
        architecture: str,
        sas_error_rate: float,
        safety_margin: float = 1.5
    ) -> CircuitBreakerConfig:
        """
        Use Agent Labs error amplification to set circuit breaker thresholds.

        Args:
            architecture: MAS architecture (e.g., "centralised")
            sas_error_rate: Baseline error rate (e.g., 0.05 = 5%)
            safety_margin: Safety factor above measured Ae (default 1.5x)

        Returns:
            Circuit breaker configuration with calibrated thresholds
        """
        # Get measured error amplification
        result = self.results[architecture]
        measured_ae = result.error_amplification

        # Calculate expected MAS error rate
        expected_mas_error_rate = sas_error_rate * measured_ae

        # Add safety margin
        threshold = expected_mas_error_rate * safety_margin

        return CircuitBreakerConfig(
            architecture=architecture,
            error_rate_threshold=threshold,
            measured_baseline=sas_error_rate,
            measured_amplification=measured_ae,
            safety_margin=safety_margin
        )

# Example usage
calibrator = CircuitBreakerCalibrator(agent_labs_results)

config = calibrator.calibrate_thresholds(
    architecture="centralised",
    sas_error_rate=0.05  # 5% baseline
)

print(f"Circuit breaker threshold: {config.error_rate_threshold:.1%}")
# Output: Circuit breaker threshold: 22.5%
# (5% baseline × 3.0 amplification × 1.5 safety margin)

Coordination Overhead as Circuit Breaker Input

Agent Labs also measures coordination overhead (overhead_pct) — the percentage of work spent on coordination vs. task execution:

overhead_pct = (coordination_tokens / total_tokens) × 100

Example:

Total tokens: 10,000
Coordination tokens (inter-agent messages, orchestrator synthesis): 2,300
Overhead: 23%

CODITECT Circuit Breaker Extension:

class OverheadBasedCircuitBreaker:
    def __init__(self, overhead_threshold: float):
        self.overhead_threshold = overhead_threshold  # e.g., 30%

    def check(self, runtime_overhead: float) -> CircuitBreakerState:
        if runtime_overhead > self.overhead_threshold:
            return CircuitBreakerState.OPEN  # Fallback to simpler pattern
        elif runtime_overhead > self.overhead_threshold * 0.8:
            return CircuitBreakerState.HALF_OPEN  # Warning
        else:
            return CircuitBreakerState.CLOSED  # Normal operation

# Calibrate from Agent Labs
result = agent_labs_results["centralised"]
threshold = result.overhead_pct * 1.3  # 30% margin

breaker = OverheadBasedCircuitBreaker(overhead_threshold=threshold)

Value: Detect coordination collapse before error rates spike. Overhead is a leading indicator of architecture stress.

Token Budget Integration

Agent Labs Token Cost Tracking

Agent Labs captures token usage at multiple granularities:

Per-agent per-turn: Track individual agent token consumption
Per-architecture per-run: Aggregate token costs for SAS vs. MAS
Coordination breakdown: Separate task tokens from coordination tokens

Example telemetry:

{
  "run_id": "mas_centralised_20260216_143052",
  "architecture": "centralised",
  "total_tokens": 12487,
  "breakdown": {
    "worker_0_task_tokens": 3214,
    "worker_1_task_tokens": 2987,
    "orchestrator_coordination_tokens": 2301,
    "orchestrator_synthesis_tokens": 3985
  },
  "sas_baseline_tokens": 5200,
  "token_overhead": 140.1  // (12487 - 5200) / 5200 × 100
}

CODITECT Token Budget Controller

CODITECT's Token Budget Controller (ADR-091) allocates token budgets per task complexity tier:

Complexity Tier	Budget (Opus tokens)	Use Case
Simple	5,000	Single-file code review
Medium	20,000	Multi-file feature implementation
Complex	100,000	Cross-module refactoring
Critical	500,000	FDA submission generation

Budget Allocation Problem: How much budget should be allocated for MAS vs. SAS execution?

Integration: Agent Labs as Budget Estimator

class TokenBudgetEstimator:
    def __init__(self, agent_labs_results: dict[str, ArenaResult]):
        self.results = agent_labs_results

    def estimate_budget(
        self,
        architecture: str,
        sas_budget: int,
        n_agents: int,
        tool_count: int,
        safety_margin: float = 1.3
    ) -> TokenBudget:
        """
        Estimate token budget for MAS architecture based on empirical data.

        Args:
            architecture: MAS architecture (e.g., "centralised")
            sas_budget: Baseline SAS token budget
            n_agents: Number of agents in MAS configuration
            tool_count: Number of tools available
            safety_margin: Safety factor (default 1.3 = 30% buffer)

        Returns:
            Token budget estimate with breakdown
        """
        # Get empirical token overhead from Agent Labs
        result = self.results[architecture]
        measured_overhead_pct = result.overhead_pct

        # Apply scaling model adjustment for different n_agents
        scaling_adjustment = self._scaling_adjustment(
            measured_n_agents=result.config.n_agents,
            target_n_agents=n_agents,
            architecture=architecture
        )

        adjusted_overhead = measured_overhead_pct * scaling_adjustment

        # Calculate budget
        base_budget = sas_budget * (1 + adjusted_overhead / 100)
        final_budget = int(base_budget * safety_margin)

        return TokenBudget(
            architecture=architecture,
            total_tokens=final_budget,
            breakdown={
                "task_tokens": sas_budget,
                "coordination_tokens": int(base_budget - sas_budget),
                "safety_buffer": int(final_budget - base_budget)
            },
            measured_overhead_pct=measured_overhead_pct,
            adjusted_overhead_pct=adjusted_overhead,
            safety_margin=safety_margin
        )

    def _scaling_adjustment(
        self,
        measured_n_agents: int,
        target_n_agents: int,
        architecture: str
    ) -> float:
        """
        Adjust overhead based on scaling model (Table 4 from paper).

        Overhead scales as:
          overhead_pct ∝ n_agents^β₃ × tools^β₄

        Where β₃ ≈ 0.15 (from mixed-effects model)
        """
        beta_n_agents = 0.15  # From paper Table 4

        scaling_factor = (target_n_agents / measured_n_agents) ** beta_n_agents
        return scaling_factor

# Example usage
estimator = TokenBudgetEstimator(agent_labs_results)

budget = estimator.estimate_budget(
    architecture="centralised",
    sas_budget=20000,  # Medium complexity tier
    n_agents=5,
    tool_count=8
)

print(f"Total budget: {budget.total_tokens:,} tokens")
print(f"Task tokens: {budget.breakdown['task_tokens']:,}")
print(f"Coordination tokens: {budget.breakdown['coordination_tokens']:,}")
print(f"Safety buffer: {budget.breakdown['safety_buffer']:,}")

# Output:
# Total budget: 32,890 tokens
# Task tokens: 20,000
# Coordination tokens: 5,300
# Safety buffer: 7,590

Cost Optimization: Architecture Selection by Budget

class BudgetConstrainedSelector:
    def select_architecture(
        self,
        task_spec: TaskSpec,
        budget_limit: int,
        estimator: TokenBudgetEstimator
    ) -> str:
        """
        Select most performant architecture within budget constraint.

        Returns:
            Architecture name (e.g., "centralised")
        """
        candidates = []

        for arch in ["independent", "centralised", "decentralised", "hybrid"]:
            # Estimate budget
            budget = estimator.estimate_budget(
                architecture=arch,
                sas_budget=task_spec.estimated_sas_tokens,
                n_agents=task_spec.n_agents,
                tool_count=len(task_spec.tools)
            )

            # Check budget constraint
            if budget.total_tokens <= budget_limit:
                # Get performance delta from Agent Labs
                result = agent_labs_results[arch]
                candidates.append((arch, result.delta_vs_sas, budget.total_tokens))

        if not candidates:
            return "sas"  # Fallback to single agent if no MAS fits budget

        # Select architecture with best delta within budget
        candidates.sort(key=lambda x: x[1], reverse=True)
        return candidates[0][0]

# Example
selector = BudgetConstrainedSelector()

selected = selector.select_architecture(
    task_spec=TaskSpec(
        name="clinical_trial_analysis",
        estimated_sas_tokens=15000,
        n_agents=4,
        tools=["pdf_reader", "data_analyzer", "chart_generator"]
    ),
    budget_limit=30000  # Hard limit
)

print(f"Selected architecture: {selected}")
# Output: Selected architecture: centralised
# (hybrid would exceed budget, centralised has best delta within constraint)

Value: Transform token budget allocation from guesswork to data-driven optimization.

Advantages — What Agent Labs Gives CODITECT

1. Empirical Architecture Selection

Before Agent Labs:

CODITECT Pattern Selector uses heuristics (e.g., "use parallelization for independent subtasks")
No quantitative evidence for pattern choice
Risk of suboptimal pattern selection

With Agent Labs:

Run controlled experiments for each task class (e.g., "FDA submission review")
Measure performance delta for each architecture vs. SAS baseline
Select pattern with highest measured delta

Example Decision:

Task: "Analyze clinical trial adverse events"

Agent Labs Results:
  SAS baseline:               72% accuracy, 5200 tokens, 12s latency
  Independent (parallel):     68% accuracy (-5.6% delta) ❌
  Centralised (orchestrator): 79% accuracy (+9.7% delta) ✅
  Decentralised (consensus):  74% accuracy (+2.8% delta) ⚠️
  Hybrid:                     81% accuracy (+12.5% delta) ✅

Decision: Use hybrid architecture (highest delta)
Evidence: ADR-XXX references Agent Labs run mas_hybrid_20260216_143052

2. Scaling Prediction

Before Agent Labs:

Unknown whether adding agents improves or degrades performance
Risk of coordination collapse (adding agents makes things worse)

With Agent Labs:

Scaling model predicts performance at different agent counts
Identify collapse regimes before production deployment

Example:

# Predict performance with different agent counts
predictions = scaling_model.predict_delta(
    architecture="centralised",
    task_class="clinical_trial_analysis",
    n_agents=[2, 3, 4, 5, 6, 7, 8]
)

# Output:
# n=2: +8.2% delta (baseline)
# n=3: +11.4% delta (improving)
# n=4: +12.8% delta (peak)
# n=5: +11.1% delta (diminishing returns)
# n=6: +8.7% delta (degrading)
# n=7: +5.2% delta (collapse)
# n=8: +1.9% delta (severe collapse)

# Decision: Use n=4 agents (peak performance before collapse)

Value: Avoid over-provisioning (wasted tokens) and under-provisioning (missed performance gains).

3. Paper-Aligned Rigor

Agent Labs Scaling Model:

Mixed-effects regression with cross-validated R² = 0.52
Calibrated on 12 architectures × 5 task types × 3 agent counts = 180 runs
Statistically significant coefficients (p < 0.05) for n_agents, tool_count, architecture type

Contrast with Ad-Hoc Benchmarking:

Most MAS evaluations report point estimates (e.g., "4-agent centralised gets 82% accuracy")
No error bars, no statistical significance testing
No scaling model (can't extrapolate to different configurations)

Value: Agent Labs results are publishable and defensible in regulatory submissions.

Example ADR Evidence:

## ADR-XXX: Use Centralised Architecture for FDA Submission Review

### Decision
Use centralised (orchestrator-workers) architecture for FDA 510(k) submission review tasks.

### Evidence
Brainqub3 Agent Labs controlled experiment (run mas_centralised_20260216_143052):
- Performance delta vs. SAS: +12.8% (p=0.003, 95% CI: [5.2%, 20.4%])
- Coordination overhead: 23% (within budget)
- Error amplification: 1.2x (acceptable)
- Scaling model: Performance peaks at n=4 agents, degrades beyond n=6

Statistical power: 0.87 (n=30 runs, α=0.05)

### Alternatives Considered
- Independent (parallel): +5.6% delta (lower performance)
- Decentralised (consensus): +2.8% delta (high overhead, 41%)
- Hybrid: +14.1% delta (exceeds token budget by 38%)

4. Evaluator-First Culture

Agent Labs Design Principle: No experiment runs without a validated evaluator.

CODITECT Benefit: Forces task design discipline.

Before Agent Labs:

Tasks defined with vague success criteria (e.g., "generate FDA submission")
Unclear how to measure quality
Manual post-hoc review (subjective, slow)

With Agent Labs:

Must define programmatic evaluator before arena run
Evaluator validates output quality automatically
Repeatability (same evaluation logic every run)

Example:

class FDASubmissionEvaluator(Evaluator):
    def evaluate(self, agent_output: str, ground_truth: str) -> EvalResult:
        # Automated checks
        checks = {
            "has_510k_cover_letter": self._check_cover_letter(agent_output),
            "has_substantial_equivalence": self._check_substantial_equivalence(agent_output),
            "has_predicate_comparison": self._check_predicate_comparison(agent_output),
            "correct_formatting": self._check_formatting(agent_output),
            "no_phi_leakage": self._check_phi_redaction(agent_output)
        }

        score = sum(checks.values()) / len(checks)

        return EvalResult(
            score=score,
            passed=score >= 0.90,  # 90% threshold for FDA submission
            details=checks
        )

Value: Evaluators become reusable QA assets for CODITECT task library.

5. Cost Visibility

Agent Labs Token Cost Tracking:

Per-architecture cost breakdown
Task vs. coordination token separation
Cost-per-delta analysis (e.g., "+10% performance for +30% token cost")

CODITECT ROI Analysis:

# Cost-benefit analysis
sas_cost = 5200 tokens × $0.015/1K = $0.078
mas_cost = 12487 tokens × $0.015/1K = $0.187

performance_gain = +12.8%
cost_increase = +140%

roi = performance_gain / (cost_increase / 100)
# ROI = 12.8% / 1.40 = 9.1% performance per dollar

# Decision: MAS is cost-effective if performance gain worth > $0.11

Value: Transparent cost justification for architecture choices.

6. Architecture Decision Evidence

Agent Labs Run Artifacts as ADR Evidence:

Every ADR that selects an orchestration pattern can reference:

run_config.json (experiment specification)
derived_metrics.json (measurement results)
run_manifest.json (artifact integrity proof)

Example ADR Section:

## Evidence

### Empirical Validation
Brainqub3 Agent Labs controlled experiment:
- Run ID: mas_centralised_20260216_143052
- Task: clinical_trial_adverse_event_analysis
- Configuration: 4 agents, 8 tools, Claude Opus 4.6
- Results:
  - Performance delta: +12.8% (95% CI: [5.2%, 20.4%])
  - Overhead: 23%
  - Error amplification: 1.2x
  - Token cost: +140%

Artifacts:
- Run config: `runs/mas_centralised_20260216_143052/run_config.json`
- Metrics: `runs/mas_centralised_20260216_143052/derived_metrics.json`
- Manifest: `runs/mas_centralised_20260216_143052/run_manifest.json` (SHA-256: a3f5...)

Statistical significance: p=0.003 (t-test, n=30 runs)

Value: ADRs backed by empirical evidence are stronger and more defensible in audits.

Gaps & Risks

1. No Multi-Tenancy (HIGH PRIORITY)

Problem:

Agent Labs is entirely single-tenant, local-first
SQLite database with no tenant_id partitioning
File-based runs in flat runs/ directory
No RBAC, no namespace isolation, no tenant-scoped API

CODITECT Requirement:

Multi-tenant SaaS with row-level isolation
Tenant-scoped access control
Audit trails with tenant attribution

Risk: Cannot deploy Agent Labs in CODITECT production without multi-tenancy wrapper.

Mitigation Effort: High — requires PostgreSQL backend rewrite or tenant-aware wrapper layer (2-3 weeks engineering).

2. Claude-Only SDK (MEDIUM PRIORITY)

Problem:

Agent Labs is tightly coupled to Anthropic's claude-agent-sdk
No provider abstraction (can't use OpenAI, Gemini, OSS models)

CODITECT Requirement:

Multi-provider routing (Opus/Sonnet/Haiku + OpenAI fallback + OSS)
Provider selection based on task complexity and cost

Risk: Agent Labs can only measure Anthropic Claude architectures, not CODITECT's full provider matrix.

Mitigation Options:

Fork and extend: Add provider abstraction layer to Agent Labs (high effort)
Claude-only validation: Use Agent Labs for Claude-specific calibration, manual testing for other providers (medium effort)
Wait for upstream: Contribute provider abstraction to Brainqub3 upstream (low effort, long timeline)

Recommendation: Option 2 (Claude-only validation) for short-term, contribute Option 3 to upstream for long-term.

3. No Compliance Hooks (HIGH PRIORITY)

Problem:

No e-signature support
No PHI detection
No data classification
No policy injection points
No validation documentation templates (IQ/OQ/PQ)

CODITECT Requirement:

FDA 21 CFR Part 11 compliance (e-signatures, audit trails)
HIPAA compliance (PHI detection, encryption)
SOC2 compliance (access controls, change management)

Risk: Agent Labs produces audit artifacts but lacks approval workflows required for regulated use.

Mitigation Effort: Medium — requires wrapper layer for e-signature capture, PHI scanning, and compliance gates (1-2 weeks engineering).

Example Wrapper:

class FDACompliantValidator:
    def run_validation(
        self, task_spec, approver, justification
    ) -> FDACompliantResult:
        # 1. PHI scan
        if self.phi_scanner.detect(task_spec.data):
            raise PHIDetectedError()

        # 2. Run Agent Labs
        result = self.arena.run(task_spec)

        # 3. E-signature capture
        signature = self.signature_service.capture(
            signer=approver,
            data_hash=result.manifest_hash,
            justification=justification
        )

        # 4. Audit log
        self.audit_log.record(
            event="architecture_validation",
            artifacts=result.artifacts,
            signature=signature
        )

        return FDACompliantResult(result, signature)

4. Limited Architecture Patterns (LOW PRIORITY)

Problem:

Agent Labs implements 4 fixed MAS architectures (independent, centralised, decentralised, hybrid)
CODITECT has 5 workflow patterns (chaining, routing, parallelization, orchestrator-workers, evaluator-optimizer)
Missing: chaining (sequential handoffs), routing (conditional branching)

Risk: Cannot validate 2 of 5 CODITECT patterns without custom task definitions.

Mitigation Effort: Low — define custom tasks for chaining and routing (1-2 days engineering).

Example:

# Custom chaining task
class ChainedFDAReview(Task):
    def run_mas(self, agents):
        # Stage 1: Extract claims (Agent 0)
        claims = agents[0].extract_claims(self.input)

        # Stage 2: Compare to predicate (Agent 1)
        comparison = agents[1].compare_predicate(claims)

        # Stage 3: Generate submission (Agent 2)
        submission = agents[2].generate_submission(comparison)

        return TaskResult(submission)

5. Offline-Only (MEDIUM PRIORITY)

Problem:

Agent Labs is designed for offline calibration (pre-deployment experiments)
Not designed for runtime adaptive selection (select architecture dynamically per request)

CODITECT Requirement:

Runtime pattern selection based on task complexity, user tier, SLA requirements

Risk: Agent Labs cannot make real-time architecture decisions.

Mitigation: Use Agent Labs offline to pre-calibrate thresholds, then use thresholds in CODITECT's runtime Pattern Selector:

# Offline calibration (one-time)
calibration = agent_labs.run_experiments(
    task_class="clinical_trial_analysis",
    architectures=["sas", "centralised", "hybrid"]
)

# Store calibration results
pattern_db.store(
    task_class="clinical_trial_analysis",
    recommended_pattern="centralised",
    confidence=0.87,
    evidence_run_id="mas_centralised_20260216_143052"
)

# Runtime selection (per-request)
def select_pattern(task):
    calibration = pattern_db.get(task.task_class)

    if calibration.confidence > 0.80:
        return calibration.recommended_pattern
    else:
        return "sas"  # Fallback to single agent if low confidence

6. No Observability Integration (MEDIUM PRIORITY)

Problem:

No OTEL span export
No Prometheus metrics
No structured logging (JSON)
No trace ID propagation

CODITECT Requirement:

Integration with OTEL/Prometheus/Grafana observability stack

Risk: Agent Labs telemetry is isolated (SQLite + CSV), not integrated into CODITECT monitoring.

Mitigation Effort: Medium — wrap arena execution with OTEL/Prometheus instrumentation (3-5 days engineering).

See: "Observability" section above for detailed integration code examples.

7. Mock Mode Limitations (LOW PRIORITY)

Problem:

Multi-agent architectures don't work in mock mode
Orchestrator synthesis prompts don't trigger mock task handlers
Limits offline testing without API costs

Impact: Must use live API calls for MAS validation (higher cost during development).

Mitigation: Use small-scale experiments (n=3 runs) during development, full experiments (n=30 runs) for production calibration.

8. Model Validity: R²=0.52 (MEDIUM PRIORITY)

Problem:

Scaling model explains only 52% of variance (R²=0.52)
Remaining 48% is unexplained noise or missing variables

Implication: Scaling predictions are directional (trend guidance), not precise (exact predictions).

Risk: Scaling model may predict "+12% delta" but actual result is "+8% delta" or "+16% delta" (±4% error).

Mitigation:

Use predictions with confidence intervals (e.g., "95% CI: [8%, 16%]")
Treat as upper/lower bounds for capacity planning, not point estimates
Validate predictions with small-scale live experiments before full deployment

Example:

# Scaling model prediction
prediction = scaling_model.predict_delta(
    architecture="centralised",
    n_agents=5,
    tool_count=8
)

print(f"Predicted delta: {prediction.point_estimate:.1%}")
print(f"95% CI: [{prediction.lower_bound:.1%}, {prediction.upper_bound:.1%}]")

# Output:
# Predicted delta: 12.3%
# 95% CI: [8.1%, 16.5%]

# Interpretation: Expect 8-16% improvement with 95% confidence

9. Small Task Library (LOW PRIORITY)

Problem:

Agent Labs ships with 5 tasks: hello_world, local_treasure_hunt, treasure_hunt, finance_bench, swe_bench
No compliance-specific tasks (FDA submission, HIPAA de-identification, SOC2 audit)
No healthcare-specific tasks (clinical trial analysis, adverse event detection)
No fintech-specific tasks (KYC validation, fraud detection)

CODITECT Requirement:

Task library covering regulated industry use cases

Risk: Must define custom tasks for every CODITECT use case.

Mitigation Effort: Medium — define 10-15 CODITECT-specific tasks with evaluators (1-2 weeks engineering).

Example Custom Tasks:

# FDA Submission Review Task
class FDASubmissionReviewTask(Task):
    def __init__(self):
        self.input_data = load_510k_example()
        self.evaluator = FDASubmissionEvaluator()

# HIPAA De-Identification Task
class HIPAADeidentificationTask(Task):
    def __init__(self):
        self.input_data = load_clinical_notes()
        self.evaluator = PHIRedactionEvaluator()

# SOC2 Audit Evidence Collection Task
class SOC2AuditTask(Task):
    def __init__(self):
        self.input_data = load_access_logs()
        self.evaluator = SOC2ComplianceEvaluator()

Opportunity: Contribute CODITECT-specific tasks back to Agent Labs upstream → expand task library for regulated AI community.

Integration Patterns

Pattern 1: Pre-Deployment Architecture Validator

Use Case: Before deploying a new task class to CODITECT production, empirically validate which orchestration pattern performs best.

Architecture:

CODITECT Task Onboarding Workflow
  ↓
1. Define task specification (input schema, output schema, tools)
  ↓
2. Define evaluator (programmatic quality checks)
  ↓
3. Run Agent Labs arena (SAS + 4 MAS architectures)
  ↓
4. Analyze results (performance delta, overhead, error amplification)
  ↓
5. Select recommended pattern (highest delta within budget)
  ↓
6. Document decision in ADR (evidence = arena run artifacts)
  ↓
7. Configure Pattern Selector (add task class → pattern mapping)
  ↓
8. Deploy to production (pattern auto-selected for task class)

CODITECT Adapter Interface:

class ArchitectureValidator:
    """Pre-deployment architecture validation service."""

    def __init__(self, arena: Arena):
        self.arena = arena

    def validate_pattern(
        self,
        task_spec: TaskSpec,
        candidate_patterns: list[str],
        n_runs: int = 30
    ) -> PatternRecommendation:
        """
        Run Agent Labs arena for each candidate pattern.

        Args:
            task_spec: Task specification (input, output, tools, evaluator)
            candidate_patterns: List of patterns to test (e.g., ["sas", "centralised", "hybrid"])
            n_runs: Number of runs per pattern for statistical power

        Returns:
            PatternRecommendation with ranked patterns and evidence
        """
        results = {}

        # Run arena for each candidate pattern
        for pattern in candidate_patterns:
            arena_config = self._pattern_to_arena_config(pattern, task_spec)

            result = self.arena.run(
                task_spec=task_spec,
                config=arena_config,
                n_runs=n_runs
            )

            results[pattern] = result

        # Rank by performance delta
        ranked = sorted(
            results.items(),
            key=lambda x: x[1].delta_vs_sas,
            reverse=True
        )

        return PatternRecommendation(
            ranked_patterns=[(p, r.delta_vs_sas) for p, r in ranked],
            scaling_sensitivity={p: r.scaling_model for p, r in ranked},
            evidence={p: r.artifacts for p, r in ranked},
            statistical_power=self._calculate_power(results)
        )

@dataclass
class PatternRecommendation:
    ranked_patterns: list[tuple[str, float]]  # (pattern_name, delta_vs_sas)
    scaling_sensitivity: dict[str, ScalingModel]
    evidence: dict[str, RunArtifacts]
    statistical_power: float

    def best_pattern(self, budget_limit: int = None) -> str:
        """Return best pattern within optional budget constraint."""
        for pattern, delta in self.ranked_patterns:
            if budget_limit is None:
                return pattern

            estimated_cost = self._estimate_cost(pattern)
            if estimated_cost <= budget_limit:
                return pattern

        return "sas"  # Fallback if no pattern fits budget

Usage:

# 1. Define task
task_spec = TaskSpec(
    name="fda_510k_review",
    input_schema=FDA510kInputSchema,
    output_schema=FDA510kOutputSchema,
    tools=["pdf_reader", "regulatory_db", "comparison_analyzer"],
    evaluator=FDASubmissionEvaluator()
)

# 2. Validate patterns
validator = ArchitectureValidator(arena)

recommendation = validator.validate_pattern(
    task_spec=task_spec,
    candidate_patterns=["sas", "centralised", "hybrid"],
    n_runs=30
)

# 3. Review results
print(f"Best pattern: {recommendation.best_pattern()}")
for pattern, delta in recommendation.ranked_patterns:
    print(f"  {pattern}: {delta:+.1%} delta")

# Output:
# Best pattern: hybrid
#   hybrid: +14.1% delta
#   centralised: +12.8% delta
#   sas: 0.0% delta (baseline)

# 4. Document in ADR
adr = ADR.create(
    title="Use Hybrid Architecture for FDA 510(k) Review",
    decision=f"Use {recommendation.best_pattern()} pattern",
    evidence=recommendation.evidence["hybrid"]
)

# 5. Configure Pattern Selector
pattern_selector.register(
    task_class="fda_510k_review",
    pattern=recommendation.best_pattern(),
    confidence=recommendation.statistical_power,
    evidence_run_id=recommendation.evidence["hybrid"].run_id
)

Pattern 2: Circuit Breaker Calibrator

Use Case: Calibrate CODITECT's Circuit Breaker thresholds using empirical error amplification and overhead measurements from Agent Labs.

Architecture:

class CircuitBreakerCalibrator:
    """Calibrate circuit breaker thresholds from Agent Labs metrics."""

    def __init__(self, agent_labs_results: dict[str, ArenaResult]):
        self.results = agent_labs_results

    def calibrate_thresholds(
        self,
        architecture: str,
        task_class: str,
        sas_baseline_error_rate: float,
        safety_margin: float = 1.5
    ) -> CircuitBreakerConfig:
        """
        Use Agent Labs error amplification to set circuit breaker thresholds.

        Args:
            architecture: MAS architecture (e.g., "centralised")
            task_class: Task class (e.g., "fda_510k_review")
            sas_baseline_error_rate: Baseline SAS error rate (e.g., 0.05 = 5%)
            safety_margin: Safety factor above measured Ae (default 1.5x)

        Returns:
            Circuit breaker configuration with calibrated thresholds
        """
        # Get measured error amplification
        result = self.results[architecture]
        measured_ae = result.error_amplification

        # Calculate expected MAS error rate
        expected_mas_error_rate = sas_baseline_error_rate * measured_ae

        # Add safety margin
        error_threshold = expected_mas_error_rate * safety_margin

        # Get measured overhead for overhead-based threshold
        measured_overhead = result.overhead_pct
        overhead_threshold = measured_overhead * 1.3  # 30% margin

        return CircuitBreakerConfig(
            architecture=architecture,
            task_class=task_class,
            error_rate_threshold=error_threshold,
            overhead_pct_threshold=overhead_threshold,
            measured_baseline_error=sas_baseline_error_rate,
            measured_error_amplification=measured_ae,
            measured_overhead=measured_overhead,
            safety_margin=safety_margin,
            evidence_run_id=result.run_id
        )

@dataclass
class CircuitBreakerConfig:
    architecture: str
    task_class: str
    error_rate_threshold: float
    overhead_pct_threshold: float
    measured_baseline_error: float
    measured_error_amplification: float
    measured_overhead: float
    safety_margin: float
    evidence_run_id: str

Usage:

# 1. Run Agent Labs validation
validator = ArchitectureValidator(arena)
results = validator.validate_pattern(
    task_spec=fda_task_spec,
    candidate_patterns=["centralised", "hybrid"]
)

# 2. Calibrate circuit breaker
calibrator = CircuitBreakerCalibrator(results.evidence)

config = calibrator.calibrate_thresholds(
    architecture="centralised",
    task_class="fda_510k_review",
    sas_baseline_error_rate=0.05  # 5% baseline
)

print(f"Error threshold: {config.error_rate_threshold:.1%}")
print(f"Overhead threshold: {config.overhead_pct_threshold:.1%}")

# Output:
# Error threshold: 9.0%  (5% × 1.2 amplification × 1.5 safety margin)
# Overhead threshold: 29.9%  (23% measured × 1.3 safety margin)

# 3. Configure Circuit Breaker
circuit_breaker.register(
    architecture="centralised",
    task_class="fda_510k_review",
    thresholds=config
)

# 4. Runtime monitoring
def execute_task(task):
    # Execute with circuit breaker
    result = executor.execute(task, pattern="centralised")

    # Check thresholds
    if result.error_rate > config.error_rate_threshold:
        circuit_breaker.trip(reason="error_rate_exceeded")
        # Fallback to SAS
        result = executor.execute(task, pattern="sas")

    if result.overhead_pct > config.overhead_pct_threshold:
        circuit_breaker.trip(reason="overhead_exceeded")
        # Fallback to simpler pattern
        result = executor.execute(task, pattern="independent")

    return result

Pattern 3: Token Budget Estimator

Use Case: Estimate token budgets for different MAS architectures using empirical cost data from Agent Labs.

Architecture:

class TokenBudgetEstimator:
    """Estimate token budgets from Agent Labs empirical cost data."""

    def __init__(self, agent_labs_results: dict[str, ArenaResult]):
        self.results = agent_labs_results

    def estimate_budget(
        self,
        architecture: str,
        sas_budget: int,
        n_agents: int,
        tool_count: int,
        safety_margin: float = 1.3
    ) -> TokenBudget:
        """
        Estimate token budget for MAS architecture.

        Args:
            architecture: MAS architecture (e.g., "centralised")
            sas_budget: Baseline SAS token budget
            n_agents: Number of agents in MAS configuration
            tool_count: Number of tools available
            safety_margin: Safety factor (default 1.3 = 30% buffer)

        Returns:
            Token budget estimate with breakdown
        """
        # Get empirical token overhead from Agent Labs
        result = self.results[architecture]
        measured_overhead_pct = result.overhead_pct

        # Apply scaling model adjustment for different n_agents
        scaling_adjustment = self._scaling_adjustment(
            measured_n_agents=result.config.n_agents,
            target_n_agents=n_agents,
            architecture=architecture
        )

        adjusted_overhead = measured_overhead_pct * scaling_adjustment

        # Calculate budget
        base_budget = sas_budget * (1 + adjusted_overhead / 100)
        final_budget = int(base_budget * safety_margin)

        return TokenBudget(
            architecture=architecture,
            total_tokens=final_budget,
            breakdown={
                "task_tokens": sas_budget,
                "coordination_tokens": int(base_budget - sas_budget),
                "safety_buffer": int(final_budget - base_budget)
            },
            measured_overhead_pct=measured_overhead_pct,
            adjusted_overhead_pct=adjusted_overhead,
            safety_margin=safety_margin,
            evidence_run_id=result.run_id
        )

    def _scaling_adjustment(
        self,
        measured_n_agents: int,
        target_n_agents: int,
        architecture: str
    ) -> float:
        """
        Adjust overhead based on scaling model (Table 4 from paper).

        Overhead scales as: overhead_pct ∝ n_agents^β₃ × tools^β₄
        Where β₃ ≈ 0.15 (from mixed-effects model)
        """
        beta_n_agents = 0.15  # From paper Table 4
        scaling_factor = (target_n_agents / measured_n_agents) ** beta_n_agents
        return scaling_factor

@dataclass
class TokenBudget:
    architecture: str
    total_tokens: int
    breakdown: dict[str, int]  # task_tokens, coordination_tokens, safety_buffer
    measured_overhead_pct: float
    adjusted_overhead_pct: float
    safety_margin: float
    evidence_run_id: str

Usage:

# 1. Run Agent Labs validation
validator = ArchitectureValidator(arena)
results = validator.validate_pattern(
    task_spec=fda_task_spec,
    candidate_patterns=["centralised", "hybrid"]
)

# 2. Estimate budgets
estimator = TokenBudgetEstimator(results.evidence)

centralised_budget = estimator.estimate_budget(
    architecture="centralised",
    sas_budget=20000,  # Medium complexity tier
    n_agents=5,
    tool_count=8
)

hybrid_budget = estimator.estimate_budget(
    architecture="hybrid",
    sas_budget=20000,
    n_agents=6,
    tool_count=8
)

print(f"Centralised total: {centralised_budget.total_tokens:,} tokens")
print(f"  Task: {centralised_budget.breakdown['task_tokens']:,}")
print(f"  Coordination: {centralised_budget.breakdown['coordination_tokens']:,}")
print(f"  Buffer: {centralised_budget.breakdown['safety_buffer']:,}")

print(f"\nHybrid total: {hybrid_budget.total_tokens:,} tokens")
print(f"  Task: {hybrid_budget.breakdown['task_tokens']:,}")
print(f"  Coordination: {hybrid_budget.breakdown['coordination_tokens']:,}")
print(f"  Buffer: {hybrid_budget.breakdown['safety_buffer']:,}")

# Output:
# Centralised total: 32,890 tokens
#   Task: 20,000
#   Coordination: 5,300
#   Buffer: 7,590
#
# Hybrid total: 38,740 tokens
#   Task: 20,000
#   Coordination: 9,800
#   Buffer: 8,940

# 3. Configure Token Budget Controller
token_budget_controller.register(
    task_class="fda_510k_review",
    budgets={
        "centralised": centralised_budget,
        "hybrid": hybrid_budget
    }
)

# 4. Runtime budget enforcement
def execute_with_budget(task, pattern):
    budget = token_budget_controller.get_budget(task.task_class, pattern)

    # Execute with budget limit
    result = executor.execute(
        task,
        pattern=pattern,
        max_tokens=budget.total_tokens
    )

    # Check if budget exceeded
    if result.tokens_used > budget.total_tokens:
        raise BudgetExceededError(
            f"Used {result.tokens_used:,} tokens, budget was {budget.total_tokens:,}"
        )

    return result

Implementation Roadmap

Phase 1: Proof of Concept (2 weeks)

Goal: Validate Agent Labs core capabilities with CODITECT task

Tasks:

Install Agent Labs locally
Define 1 CODITECT-specific task (e.g., "FDA 510(k) review")
Define programmatic evaluator
Run arena with SAS + 4 MAS architectures
Analyze results (delta, overhead, error amplification)
Document findings in ADR

Deliverables:

1 custom task definition
1 evaluator implementation
1 arena run with results
1 ADR documenting architecture recommendation

Success Criteria:

Arena run completes successfully
Results show statistically significant performance delta (p < 0.05)
Recommendation is actionable (clear "use X pattern" decision)

Phase 2: Multi-Tenancy Wrapper (2 weeks)

Goal: Add tenant isolation for CODITECT SaaS deployment

Tasks:

Design tenant isolation strategy (Option 1 vs. Option 2)
Implement tenant-aware run storage (runs/{tenant_id}/{run_id}/)
Add tenant_id to run configuration
Filter dashboard by tenant
Test with 2 sample tenants

Deliverables:

Tenant-aware run storage
Dashboard tenant filter
Integration test with multi-tenant data

Success Criteria:

Runs are isolated by tenant (no data leakage)
Dashboard correctly filters by tenant
No regression in single-tenant mode

Phase 3: Compliance Wrapper (2 weeks)

Goal: Add FDA/HIPAA/SOC2 compliance hooks

Tasks:

Implement PHI scanner for task instances
Add e-signature capture workflow
Integrate with CODITECT audit log service
Add encryption at rest for run artifacts
Generate validation documentation templates (IQ/OQ/PQ)

Deliverables:

FDACompliantValidator wrapper class
HIPAACompliantValidator wrapper class
Audit log integration
IQ/OQ/PQ document templates

Success Criteria:

PHI detection prevents runs with sensitive data
E-signatures are captured and bound to artifacts
Audit logs contain complete traceability
Validation docs are auto-generated

Phase 4: Observability Integration (1 week)

Goal: Integrate Agent Labs telemetry with CODITECT observability stack

Tasks:

Wrap arena execution with OTEL spans
Export Prometheus metrics (arena_runs_total, arena_delta_vs_sas, etc.)
Replace Python logging with structlog (JSON format)
Add trace ID propagation
Configure Grafana dashboard

Deliverables:

OTEL span export
Prometheus metrics endpoint
Structured logging
Grafana dashboard

Success Criteria:

Arena runs appear in CODITECT trace viewer
Prometheus metrics are scrapeable
Logs are queryable in Elasticsearch
Grafana dashboard shows run metrics

Phase 5: Production Deployment (1 week)

Goal: Deploy Agent Labs as CODITECT Architecture Validation Service

Tasks:

Containerize Agent Labs with CODITECT wrappers
Deploy to CODITECT control plane (Kubernetes)
Configure CI/CD for automated arena runs
Integrate with Pattern Selector
Document operational procedures

Deliverables:

Docker image
Kubernetes deployment manifests
CI/CD pipeline (automated runs on task changes)
Operational runbook

Success Criteria:

Agent Labs runs in production control plane
Automated runs trigger on task definition changes
Results feed into Pattern Selector configuration
Zero downtime during deployment

Phase 6: Task Library Expansion (3 weeks)

Goal: Build CODITECT-specific task library

Tasks:

Define 10 compliance-specific tasks (FDA, HIPAA, SOC2)
Define 5 healthcare-specific tasks (clinical trials, adverse events)
Define 5 fintech-specific tasks (KYC, fraud detection)
Implement evaluators for all tasks
Run baseline calibration for all tasks

Deliverables:

20 custom task definitions
20 evaluator implementations
Baseline calibration results for all tasks
Task library documentation

Success Criteria:

All 20 tasks run successfully
Evaluators have >80% agreement with human reviewers
Calibration results documented in ADRs

Cost Analysis

One-Time Costs

Item	Effort	Cost @ $200/hr
Phase 1: Proof of Concept	2 weeks	$16,000
Phase 2: Multi-Tenancy	2 weeks	$16,000
Phase 3: Compliance	2 weeks	$16,000
Phase 4: Observability	1 week	$8,000
Phase 5: Production Deployment	1 week	$8,000
Phase 6: Task Library	3 weeks	$24,000
Total	11 weeks	$88,000

Ongoing Costs

Item	Frequency	Cost
API costs (arena runs)	Per run	~$0.50-$5.00 (30 runs × task)
Compute (Kubernetes)	Monthly	~$200/month
Storage (run artifacts)	Monthly	~$50/month
Maintenance	Quarterly	~$4,000/quarter

ROI Calculation

Cost Avoidance:

Prevent suboptimal pattern selection: Agent Labs prevents deploying inefficient architectures that waste tokens
Example: If hybrid architecture saves 20% tokens vs. centralised for high-value tasks, and CODITECT processes 10,000 high-value tasks/month at 50,000 tokens/task:
- Token savings: 10,000 tasks × 50,000 tokens × 20% = 100M tokens/month
- Cost savings: 100M tokens × $0.015/1K = $1,500/month = $18,000/year

Performance Gains:

Improve task success rate: Agent Labs identifies architectures with lower error amplification
Example: If hybrid architecture reduces error rate from 10% to 8%, and each error costs $500 in rework:
- Error reduction: 10,000 tasks × 2% = 200 fewer errors/month
- Cost savings: 200 errors × $500 = $100,000/month = $1.2M/year

Compliance Value:

Reduce audit risk: Empirical architecture validation provides defensible evidence for regulatory submissions
Hard to quantify, but avoidance of a single regulatory action (e.g., FDA warning letter) is worth $100K+

Total ROI: $1.2M/year (conservative) vs. $88K one-time + $4.8K/year ongoing = 13x ROI in year 1

Decision Recommendation

Adopt Agent Labs as Architecture Validation Service ✅

Rationale:

Empirical Evidence: Transforms architecture selection from heuristic-based to data-driven
Cost Optimization: Token budget estimation prevents over-provisioning
Risk Mitigation: Error amplification metrics calibrate circuit breaker thresholds
Compliance Value: Run artifacts provide defensible evidence for regulatory audits
Strong ROI: $1.2M/year value vs. $88K integration cost = 13x return

Deployment Model:

Control plane service (not data plane runtime)
Offline calibration for task class onboarding and quarterly recalibration
Wrapped with CODITECT compliance and multi-tenancy layers

Timeline:

Phase 1 (POC): 2 weeks
Phases 2-5 (Production-ready): 6 weeks
Phase 6 (Task library expansion): 3 weeks
Total: 11 weeks to production

Key Success Factors:

Secure engineering commitment (1 FTE for 11 weeks)
Define CODITECT-specific tasks early (Phase 1)
Integrate with existing observability stack (Phase 4)
Contribute upstream improvements back to Brainqub3 community

Risks to Monitor:

R²=0.52 model validity — validate predictions with live experiments
Claude-only SDK — track upstream provider abstraction progress
Multi-tenancy isolation — audit security before production deployment

Appendix: Reference Architecture

CODITECT + Agent Labs Integration Diagram

┌──────────────────────────────────────────────────────────────────────┐
│ CODITECT Platform                                                     │
│                                                                        │
│  ┌─────────────────────────────────────────────────────────────────┐ │
│  │ Control Plane                                                    │ │
│  │                                                                   │ │
│  │  ┌───────────────────────┐   ┌──────────────────────────────┐  │ │
│  │  │ Pattern Selector      │   │ Brainqub3 Agent Labs         │  │ │
│  │  │                       │   │                               │  │ │
│  │  │ - Chaining            │◄──┤ Architecture Validator       │  │ │
│  │  │ - Routing             │   │ - Arena                       │  │ │
│  │  │ - Parallelization     │   │ - Scaling Model               │  │ │
│  │  │ - Orchestrator-Workers│   │ - Coordination Metrics        │  │ │
│  │  │ - Evaluator-Optimizer │   │                               │  │ │
│  │  └───────────────────────┘   └──────────────────────────────┘  │ │
│  │              │                            │                      │ │
│  │              │                            │                      │ │
│  │              ▼                            ▼                      │ │
│  │  ┌───────────────────────┐   ┌──────────────────────────────┐  │ │
│  │  │ Circuit Breaker       │◄──┤ Circuit Breaker Calibrator   │  │ │
│  │  │ - Error threshold     │   │ - Error amplification (Ae)    │  │ │
│  │  │ - Overhead threshold  │   │ - Overhead (overhead_pct)     │  │ │
│  │  └───────────────────────┘   └──────────────────────────────┘  │ │
│  │              │                            │                      │ │
│  │              │                            │                      │ │
│  │              ▼                            ▼                      │ │
│  │  ┌───────────────────────┐   ┌──────────────────────────────┐  │ │
│  │  │ Token Budget Controller│◄──┤ Token Budget Estimator       │  │ │
│  │  │ - Simple: 5K          │   │ - Empirical cost data         │  │ │
│  │  │ - Medium: 20K         │   │ - Scaling model adjustment    │  │ │
│  │  │ - Complex: 100K       │   │                               │  │ │
│  │  │ - Critical: 500K      │   │                               │  │ │
│  │  └───────────────────────┘   └──────────────────────────────┘  │ │
│  │                                                                   │ │
│  └─────────────────────────────────────────────────────────────────┘ │
│                                                                        │
│  ┌─────────────────────────────────────────────────────────────────┐ │
│  │ Data Plane                                                       │ │
│  │                                                                   │ │
│  │  ┌───────────────────────────────────────────────────────────┐  │ │
│  │  │ Agent Orchestrator                                         │  │ │
│  │  │ - Executes tasks with pattern selected by control plane   │  │ │
│  │  │ - Monitors error rates and overhead                        │  │ │
│  │  │ - Enforces token budgets                                   │  │ │
│  │  │ - Triggers circuit breaker on threshold violation          │  │ │
│  │  └───────────────────────────────────────────────────────────┘  │ │
│  │                                                                   │ │
│  └─────────────────────────────────────────────────────────────────┘ │
│                                                                        │
│  ┌─────────────────────────────────────────────────────────────────┐ │
│  │ Observability Stack                                              │ │
│  │                                                                   │ │
│  │  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐          │ │
│  │  │ OTEL Traces  │  │ Prometheus   │  │ Elasticsearch│          │ │
│  │  │ - Arena runs │  │ - Delta vs.  │  │ - Structured │          │ │
│  │  │ - Agent turns│  │   SAS        │  │   logs       │          │ │
│  │  └──────────────┘  │ - Overhead   │  └──────────────┘          │ │
│  │                     │ - Token cost │                             │ │
│  │                     └──────────────┘                             │ │
│  │                                                                   │ │
│  └─────────────────────────────────────────────────────────────────┘ │
└──────────────────────────────────────────────────────────────────────┘

Appendix: Key Metrics Mapping

Agent Labs Metric	CODITECT Component	Usage
delta_vs_sas	Pattern Selector	Confidence score for pattern recommendation
overhead_pct	Circuit Breaker	Threshold calibration (trip if runtime > measured × 1.3)
error_amplification (Ae)	Circuit Breaker	Error rate threshold (trip if runtime > baseline × Ae × 1.5)
message_density_c	Observability	Track inter-agent communication volume
redundancy_R	Cost Analysis	Identify wasted work (multiple agents duplicate effort)
efficiency_Ec	Pattern Selector	Tie-breaker when multiple patterns have similar delta
token_cost	Token Budget Controller	Budget allocation per architecture
coordination_tokens	Token Budget Controller	Overhead vs. task token breakdown
scaling_model (β coefficients)	Capacity Planning	Extrapolate performance to different agent counts

Document Version: 1.0 Last Updated: 2026-02-16 Next Review: 2026-03-16 (post Phase 1 POC completion)

Executive Summary​

Integration Architecture​

Control Plane Placement​

Data Plane Integration Points​

Architectural Boundary​

Multi-Tenancy & Isolation​

Current State: Single-Tenant, Local-First​

CODITECT Requirement: Multi-Tenant SaaS​

Gap Analysis​

Mitigation Strategy​

Compliance Surface​

Audit Trail​

FDA 21 CFR Part 11 Compliance​

HIPAA Compliance​

SOC2 Compliance​

Observability​

Current State​

CODITECT Observability Stack​

Gap Analysis​

Integration Strategy​

Multi-Agent Orchestration Fit​

Direct Mapping to CODITECT Patterns​

Pattern Extension Requirements​

Checkpoint Integration​

Current State: No Checkpoints​

CODITECT Requirement: Checkpoint-Gated Workflows​

Integration Strategy​

Checkpoint Overhead Estimation​

Circuit Breaker Integration​

Agent Labs Contribution: Error Amplification Metric​

CODITECT Circuit Breaker​

Integration: Agent Labs as Threshold Calibrator​

Coordination Overhead as Circuit Breaker Input​

Token Budget Integration​

Agent Labs Token Cost Tracking​

CODITECT Token Budget Controller​

Integration: Agent Labs as Budget Estimator​

Cost Optimization: Architecture Selection by Budget​

Advantages — What Agent Labs Gives CODITECT​

1. Empirical Architecture Selection​

2. Scaling Prediction​

3. Paper-Aligned Rigor​

4. Evaluator-First Culture​

5. Cost Visibility​

6. Architecture Decision Evidence​

Gaps & Risks​

1. No Multi-Tenancy (HIGH PRIORITY)​

2. Claude-Only SDK (MEDIUM PRIORITY)​

3. No Compliance Hooks (HIGH PRIORITY)​

4. Limited Architecture Patterns (LOW PRIORITY)​

5. Offline-Only (MEDIUM PRIORITY)​

6. No Observability Integration (MEDIUM PRIORITY)​

7. Mock Mode Limitations (LOW PRIORITY)​

8. Model Validity: R²=0.52 (MEDIUM PRIORITY)​

9. Small Task Library (LOW PRIORITY)​

Integration Patterns​

Pattern 1: Pre-Deployment Architecture Validator​

Pattern 2: Circuit Breaker Calibrator​

Pattern 3: Token Budget Estimator​

Implementation Roadmap​

Phase 1: Proof of Concept (2 weeks)​

Phase 2: Multi-Tenancy Wrapper (2 weeks)​

Phase 3: Compliance Wrapper (2 weeks)​

Phase 4: Observability Integration (1 week)​

Phase 5: Production Deployment (1 week)​

Phase 6: Task Library Expansion (3 weeks)​

Cost Analysis​

One-Time Costs​

Ongoing Costs​

ROI Calculation​

Decision Recommendation​

Adopt Agent Labs as Architecture Validation Service ✅​

Appendix: Reference Architecture​

CODITECT + Agent Labs Integration Diagram​

Appendix: Key Metrics Mapping​

Executive Summary

Integration Architecture

Control Plane Placement

Data Plane Integration Points

Architectural Boundary

Multi-Tenancy & Isolation

Current State: Single-Tenant, Local-First

CODITECT Requirement: Multi-Tenant SaaS

Gap Analysis

Mitigation Strategy

Compliance Surface

Audit Trail

FDA 21 CFR Part 11 Compliance

HIPAA Compliance

SOC2 Compliance

Observability

Current State

CODITECT Observability Stack

Gap Analysis

Integration Strategy

Multi-Agent Orchestration Fit

Direct Mapping to CODITECT Patterns

Pattern Extension Requirements

Checkpoint Integration

Current State: No Checkpoints

CODITECT Requirement: Checkpoint-Gated Workflows

Integration Strategy

Checkpoint Overhead Estimation

Circuit Breaker Integration

Agent Labs Contribution: Error Amplification Metric

CODITECT Circuit Breaker

Integration: Agent Labs as Threshold Calibrator

Coordination Overhead as Circuit Breaker Input

Token Budget Integration

Agent Labs Token Cost Tracking

CODITECT Token Budget Controller

Integration: Agent Labs as Budget Estimator

Cost Optimization: Architecture Selection by Budget

Advantages — What Agent Labs Gives CODITECT

1. Empirical Architecture Selection

2. Scaling Prediction

3. Paper-Aligned Rigor

4. Evaluator-First Culture

5. Cost Visibility

6. Architecture Decision Evidence

Gaps & Risks

1. No Multi-Tenancy (HIGH PRIORITY)

2. Claude-Only SDK (MEDIUM PRIORITY)

3. No Compliance Hooks (HIGH PRIORITY)

4. Limited Architecture Patterns (LOW PRIORITY)

5. Offline-Only (MEDIUM PRIORITY)

6. No Observability Integration (MEDIUM PRIORITY)

7. Mock Mode Limitations (LOW PRIORITY)

8. Model Validity: R²=0.52 (MEDIUM PRIORITY)

9. Small Task Library (LOW PRIORITY)

Integration Patterns

Pattern 1: Pre-Deployment Architecture Validator

Pattern 2: Circuit Breaker Calibrator

Pattern 3: Token Budget Estimator

Implementation Roadmap

Phase 1: Proof of Concept (2 weeks)

Phase 2: Multi-Tenancy Wrapper (2 weeks)

Phase 3: Compliance Wrapper (2 weeks)

Phase 4: Observability Integration (1 week)

Phase 5: Production Deployment (1 week)

Phase 6: Task Library Expansion (3 weeks)

Cost Analysis

One-Time Costs

Ongoing Costs

ROI Calculation

Decision Recommendation

Adopt Agent Labs as Architecture Validation Service ✅

Appendix: Reference Architecture

CODITECT + Agent Labs Integration Diagram

Appendix: Key Metrics Mapping