Brainqub3 Agent Labs - Scaling Integration Assessment

Date: 2026-02-16 Author: Claude (Opus 4.6) Task ID: H.0 (ad-hoc analysis) Project: PILOT (coditect-rollout-master)

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1. Executive Summary

Recommendation: Conditional Go - Adopt brainqub3/agent-labs as an offline architecture validation tool within the CODITECT control plane. Do NOT replace CODITECT's orchestration engine.

Dimension	Score	Notes
Strategic Alignment	70%	4 MAS patterns map to CODITECT's 5 workflow patterns
Technical Quality	High	Clean Python, evaluator-first, immutable runs, paper-aligned model
Integration Effort	Low	MIT license, ~2,500 LOC, CLI-driven, file-based storage
Risk Level	Low	Read-only submodule, no production dependencies
Gap Severity	Medium	No multi-tenancy, Claude-only, no compliance hooks

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2. Repository Overview

Property	Value
Repository	brainqub3/agent-labs
Paper	arXiv:2512.08296 - "Towards a Science of Scaling Agent Systems"
License	MIT
Language	Python 3.11+
Package Manager	uv
LOC	~2,500 (core)
Agent SDK	Claude Agent SDK (Anthropic)
Submodule Path	submodules/labs/agent-labs/
Push Policy	NEVER push (third-party, read-only)

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3. Architecture Deep-Dive

3.1 Core Modules (Verified Against Source Code)

Module	File	LOC	Purpose
Orchestrators	brainqub3/arena/orchestrators.py	231	5 orchestration patterns (SAS, Independent, Centralised, Decentralised, Hybrid)
Runner	brainqub3/arena/runner.py	1,088	Experiment execution engine with AgentBackend
Paper Model	brainqub3/scaling/paper_model.py	142	Mixed-effects scaling model (R²=0.52)
Immutability	brainqub3/telemetry/immutability.py	117	SHA-256 per-file + Merkle tree hash for runs
Coordination	brainqub3/metrics/coordination.py	31	4 coordination metric functions
Redundancy	brainqub3/metrics/redundancy.py	80	TF-IDF cosine similarity for work overlap
CLI	brainqub3/cli.py	1,089	Full CLI: doctor, run, dashboard, metrics, scenario
Dashboard	brainqub3/dashboard/webapp.py	~500	Custom HTML webapp (Python ThreadingHTTPServer)

3.2 Orchestration Patterns

Pattern	Class	Peer Exchange	Aggregation	Paper Default Overhead
SAS	SASOrchestrator	None	Direct output	0%
Independent	IndependentOrchestrator	None	_majority_vote() (Counter.most_common)	58%
Centralised	CentralisedOrchestrator	None	Orchestrator synthesis	285%
Decentralised	DecentralisedOrchestrator	N refine rounds	Consensus vote	263%
Hybrid	HybridOrchestrator	Assignment + peer rounds	Orchestrator synthesis	515%

Key implementation detail: build_orchestrator(architecture: str) factory maps string → class. OrchestratorContext dataclass provides execute_turn, record_inter_agent, record_orchestrator callbacks.

3.3 Scaling Model

Formula: P_hat = clip(beta0 + sum(beta_i * z_i) + sum(beta_ij * z_i * z_j), 0, 1)

Transforms (verified in paper_model.py:56-79):

• I_centered = intelligence_index - 56.9 (I_mean_center from coefficients)
• log1p(T) for tool count
• log1p(n_agents) for agent count
• log1p(overhead_pct) for overhead
• log1p(error_amp_Ae) for error amplification

Interaction terms (9, verified in paper_model.py:105-114):

• P_SA x log1p_n_agents, Ec x T, overhead_pct x T, Ae x T, R x n_agents
• I x Ec, Ae x P_SA, c x I, I x log1p_T

3.4 Coordination Metrics (Verified Formulas)

Metric	Code Formula	Source
overhead_pct	((turns_mas - turns_sas) / turns_sas) * 100	coordination.py:7
message_density	inter_agent_messages / (inter_agent_messages + turns_total)	coordination.py:15-16
coordination_efficiency	success_rate / (turns_total / turns_sas)	coordination.py:22
error_amplification	(1 - success_mas) / (1 - success_sas)	coordination.py:30
redundancy_R	TF-IDF cosine similarity mean across worker outputs	redundancy.py:53-79

Important: Overhead is turns-based (not time-based). Message density uses the ratio of inter-agent messages to total message count (not messages/n_agents).

3.5 Run Immutability

• SHA-256 per-file hashing (_sha256() in immutability.py:21-29)
• Merkle-like tree hash from concatenated path:hash pairs
• finalize_run() writes manifest, raises RuntimeError if already finalized
• verify_run_manifest() checks file existence + hash match, returns (bool, list[str])

3.6 Key CLI Commands (Verified in cli.py)

uv run brainqub3 doctor                           # Environment health check
uv run brainqub3 run sas --task X --model Y        # Single-agent baseline
uv run brainqub3 run mas --task X --arch Z --model Y  # Multi-agent experiment
uv run brainqub3 run elasticity --task X --arch Z  # Scaling grid sweep
uv run brainqub3 dashboard                         # Launch HTML dashboard on :8765
uv run brainqub3 task init my_task                 # Create new task scaffold
uv run brainqub3 metrics compute --run-id X        # Compute coordination metrics
uv run brainqub3 model predict --scenario X        # Run scaling prediction

3.7 Evaluator-First Enforcement

ExperimentRunner._run() (runner.py:800) calls self._run_eval_tests(task) before any experiment execution. This matches CODITECT's ground truth validation pattern.

run_mas() auto-generates SAS baseline by calling self.run_sas() first (runner.py:1059).

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4. CODITECT Alignment Analysis

4.1 Strong Alignment (70%)

CODITECT Capability	Agent Labs Equivalent	Alignment
5 workflow patterns (MoE)	4 MAS patterns + SAS baseline	Direct map for 4/5
Ground truth validation	Evaluator-first with test enforcement	Exact match
Circuit Breaker / Token Budget	Coordination metrics (overhead, efficiency)	Input signal
Compliance audit trail	SHA-256 immutable run manifests	Compatible
776 agent benchmarking	Arena runtime + controlled experiments	Direct use
Scaling predictions	Paper model with elasticity calibration	Unique value-add

4.2 Gaps (30%)

Gap	Impact	Mitigation
No multi-tenancy	Cannot share experiments across CODITECT tenants	Phase 2: adapter layer with tenant isolation
Claude-only SDK	No multi-model provider abstraction	Phase 2: provider interface extraction
No compliance hooks	FDA 21 CFR Part 11, HIPAA, SOC2 not enforced	Phase 2: wrapper with compliance middleware
Offline calibration only	No runtime adaptive selection	Phase 3: streaming predictions to Pattern Selector
4 patterns (vs CODITECT 5+)	Missing Router/Pipeline patterns	Phase 2: custom orchestrator implementations
No observability	No OTEL, Prometheus integration	Phase 2: instrument with CODITECT telemetry
Local-first storage	Single-machine, no cloud persistence	Phase 3: GCS/S3 adapter for run storage

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5. Integration Phases

Phase 1: Immediate (0-2 weeks)

• Use as-is for offline validation experiments
• Author CODITECT-specific tasks (agent benchmarks, skill evaluation)
• Read-only submodule at submodules/labs/agent-labs/
• No production dependencies

Phase 2: Adapter Layer (4-6 weeks)

• Build scripts/scaling-analysis/ wrapper package
• Add multi-tenant experiment isolation
• Extract provider interface for multi-model support
• Add OTEL instrumentation to run lifecycle
• Implement compliance middleware (audit events)
• Add 5th orchestrator pattern (Router/Pipeline)

Phase 3: Runtime Integration (8-12 weeks, deferred)

• Scaling model predictions feed Pattern Selector in real-time
• Streaming elasticity calibration from production workloads
• GCS/S3 backend for experiment data
• Dashboard integration with CODITECT admin UI

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6. Artifact Inventory

20 analysis artifacts were generated and code-verified:

Category	Count	Location
Core documents	4	artifacts/ (executive-summary, coditect-impact, quick-start, glossary)
Architecture docs	4	artifacts/ (sdd, tdd, c4-architecture, mermaid-diagrams)
ADRs	5	artifacts/adrs/ (ADR-001 through ADR-005)
React dashboards	6	artifacts/dashboards/ (6 JSX components)
Index	1	artifacts/README.md

Total: 20 files, ~17,500 lines Location: analyze-new-artifacts/coditect-scaling-agent-systems/artifacts/

Discrepancies Found and Fixed

4 factual discrepancies between generated artifacts and actual source code were identified during deep-dive review and corrected:

Discrepancy	Affected Files	Fix Applied
"Streamlit dashboard" (actual: custom HTML ThreadingHTTPServer)	sdd, tdd, c4, glossary, mermaid, ADR-001, ADR-002, JSX	All 30+ refs corrected
run-experiment CLI command (actual: run sas/run mas)	mermaid-diagrams	2 refs corrected
Overhead formula "coordination_time/total_time" (actual: turns-based)	mermaid-diagrams	1 ref corrected
Message density "messages/n_agents" (actual: inter_agent/(inter_agent+turns))	tdd, mermaid, c4	4 refs corrected

Mermaid Diagram Validation

All 10 Mermaid diagrams rendered successfully via Mermaid Chart MCP:

#	Diagram	Type	Status
1	System Context	graph TB	PASS
2	Arena Run Lifecycle	sequenceDiagram	PASS
3	Orchestration Patterns	graph LR	PASS
4	Scaling Model Data Flow	flowchart TD	PASS
5	Elasticity Estimation	flowchart TD	PASS
6	Coordination Metrics	flowchart LR	PASS
7	CODITECT Integration	graph TB	PASS
8	Data Storage	graph TD	PASS
9	Experiment Design	flowchart TD	PASS
10	Component Diagram	graph TB	PASS

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7. Risk Assessment

Risk	Probability	Impact	Mitigation
Upstream breaking changes	Medium	Low	Pin submodule to specific commit
Claude SDK version drift	Medium	Medium	Version constraint in adapter
Scaling model R²=0.52 limitations	High	Low	Use for ranking, not absolute predictions
License contamination	Low	Low	MIT license, permissive
Dependency conflicts with CODITECT	Low	Medium	Isolated venv per submodule

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8. Conclusion

Brainqub3 Agent Labs provides a rigorous, paper-aligned measurement rig that fills a gap in CODITECT's architecture validation capabilities. The 70% alignment with existing patterns, combined with low integration effort and zero production risk, makes it a clear Conditional Go for Phase 1 adoption.

The 30% gap (multi-tenancy, multi-model, compliance) is addressable through the adapter layer in Phase 2, and does not block immediate use for offline experiments.

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Analysis Location: internal/analysis/agent-labs-scaling/agent-labs-scaling-integration-assessment-2026-02-16.md Artifacts Location: analyze-new-artifacts/coditect-scaling-agent-systems/artifacts/ Submodule Location: submodules/labs/agent-labs/ (read-only, NEVER push)