Research Prompts: Consequence-Aware Autonomous Execution
Deep Dive Research Agenda for Coditect Product Development
Category 1: Theoretical Foundations
1.1 Causal Inference for Real-Time Systems
Prompt 1.1.1 — Streaming Causal Discovery
Research Question: How can causal discovery algorithms be adapted for streaming data
in autonomous code execution systems where the causal graph evolves as new actions
are taken?
Search Terms: "online causal discovery" "streaming causal inference" "dynamic causal
graphs" "incremental causal learning" "temporal causal models"
Expected Output: Survey of algorithms supporting incremental causal graph updates,
computational complexity analysis, comparison of PC-based vs. score-based approaches
for real-time applications.
Coditect Application: Foundation for Causation Tracker that updates attribution
confidence in real-time as consequences are observed.
Prompt 1.1.2 — Counterfactual Reasoning for Action Impact
Research Question: What counterfactual reasoning frameworks can predict "what would
have happened if a different action had been taken" to inform consequence-aware
plan adaptation?
Search Terms: "counterfactual prediction" "structural causal models software"
"potential outcomes framework" "causal effect estimation" "interventional queries"
Expected Output: Comparison of Pearl's do-calculus vs. Rubin's potential outcomes
for software systems. Implementation patterns for counterfactual simulation.
Coditect Application: Enable Adaptation Engine to evaluate alternative action
sequences and select optimal remediation paths.
Prompt 1.1.3 — Causal Reinforcement Learning
Research Question: How can causal inference principles be integrated with
reinforcement learning to create agents that understand causal relationships
rather than just correlations?
Search Terms: "causal reinforcement learning" "causal RL agents" "intervention
policies" "causal model-based RL" "causal curiosity"
Expected Output: Survey of causal RL approaches including causal world models,
causal exploration, and causal transfer learning.
Coditect Application: Train CACA agents that learn causal structure of codebases
to predict consequence propagation.
1.2 Multi-Temporal Modeling
Prompt 1.2.1 — Temporal Abstraction Hierarchies
Research Question: How can temporal abstraction hierarchies enable efficient
modeling of consequences across different timescales (milliseconds to weeks)?
Search Terms: "hierarchical temporal abstraction" "multi-scale time series"
"temporal hierarchy learning" "options framework" "multi-resolution prediction"
Expected Output: Frameworks for representing and reasoning about events at
multiple temporal granularities. Methods for propagating information between scales.
Coditect Application: Design Consequence Mesh temporal layers with efficient
information flow between immediate, short-term, and projected observers.
Prompt 1.2.2 — Technical Debt Trajectory Prediction
Research Question: What machine learning models best predict technical debt
accumulation trajectories from code changes and architectural decisions?
Search Terms: "technical debt prediction models" "code quality trajectory"
"architectural erosion prediction" "maintainability forecasting" "defect prediction"
Expected Output: Comparison of time-series models, graph neural networks, and
transformer-based approaches for TD prediction. Feature engineering best practices.
Coditect Application: Projected Consequence Observer for long-term impact assessment.
Prompt 1.2.3 — Ripple Effect Simulation
Research Question: How can graph-based propagation models simulate the ripple
effects of code changes through dependency networks?
Search Terms: "change impact analysis" "dependency graph propagation" "code change
ripple effect" "semantic impact propagation" "transitive dependency analysis"
Expected Output: Algorithms for computing change propagation probabilities through
software dependency graphs. Integration with static analysis tools.
Coditect Application: Enable immediate consequence observation to flag cross-module
impact before full test execution.
Category 2: Architectural Patterns
2.1 Observer Architecture
Prompt 2.1.1 — Event Sourcing for Consequence Tracking
Research Question: How can event sourcing patterns be adapted for consequence
tracking where every action and its observed consequences are captured as
immutable events?
Search Terms: "event sourcing patterns" "CQRS consequence" "event-driven
observability" "audit trail architecture" "immutable event logs"
Expected Output: Event sourcing schema designs for action-consequence pairs.
Query patterns for causal chain reconstruction. FoundationDB implementation guidance.
Coditect Application: Design FoundationDB schema for Causation Tracker with
efficient temporal queries.
Prompt 2.1.2 — Reactive Streams for Consequence Propagation
Research Question: What reactive programming patterns enable non-blocking
consequence propagation through the CACA architecture?
Search Terms: "reactive streams patterns" "backpressure handling" "reactive
event processing" "async consequence handling" "reactive architecture"
Expected Output: Pattern catalog for reactive consequence observation.
Backpressure strategies for high-velocity execution. Akka/RxJava/Project Reactor
comparisons.
Coditect Application: Implement non-blocking Consequence Mesh with appropriate
backpressure handling.
Prompt 2.1.3 — Parallel Observer Coordination
Research Question: How should multiple parallel observers (compilation, tests,
security scans) coordinate to synthesize impact signals without creating
bottlenecks?
Search Terms: "parallel observer pattern" "signal aggregation" "consensus
observation" "observer coordination" "distributed event aggregation"
Expected Output: Coordination protocols for parallel observers. Signal synthesis
algorithms. Conflict resolution strategies when observers disagree.
Coditect Application: Design Impact Synthesizer component architecture.
2.2 Adaptation Mechanisms
Prompt 2.2.1 — Dynamic Plan Mutation Strategies
Research Question: What formal methods ensure plan mutation preserves task
completion guarantees while adapting to observed consequences?
Search Terms: "plan repair" "dynamic replanning" "plan adaptation" "execution
monitoring replanning" "temporal plan networks"
Expected Output: Survey of plan repair algorithms from AI planning literature.
Methods for maintaining plan validity during mutation. Proof techniques for
adaptation correctness.
Coditect Application: Formal foundation for Adaptation Engine plan mutation
with correctness guarantees.
Prompt 2.2.2 — Graceful Degradation Patterns
Research Question: What patterns enable autonomous systems to gracefully degrade
functionality when consequence severity exceeds thresholds?
Search Terms: "graceful degradation patterns" "adaptive system resilience"
"controlled degradation" "failure mode adaptation" "resilience patterns"
Expected Output: Catalog of graceful degradation patterns. Decision frameworks
for degradation level selection. Recovery path planning.
Coditect Application: Define CACA stopping conditions and partial completion
strategies.
Prompt 2.2.3 — Rollback Architecture
Research Question: How should rollback capabilities be architected for
consequence-aware systems that need to undo problematic actions?
Search Terms: "transactional rollback patterns" "saga pattern" "compensating
actions" "undo architecture" "reversible computation"
Expected Output: Comparison of saga patterns, compensation-based rollback, and
checkpoint-based recovery. Implementation complexity analysis.
Coditect Application: Implement Adaptation Engine rollback capabilities with
FoundationDB transaction support.
Category 3: Multi-Agent Coordination
3.1 Agent Communication
Prompt 3.1.1 — Consequence Sharing Protocols
Research Question: What communication protocols enable agents to share observed
consequences efficiently without overwhelming bandwidth or creating circular
dependencies?
Search Terms: "multi-agent communication protocols" "distributed observation
sharing" "agent coordination protocols" "information sharing MAS" "gossip protocols"
Expected Output: Protocol designs for consequence sharing. Bandwidth optimization
techniques. Circular dependency prevention mechanisms.
Coditect Application: Design inter-agent communication for ConsequenceObserverAgents.
Prompt 3.1.2 — Distributed Causation Consensus
Research Question: How can multiple agents reach consensus on causation attribution
when they have partial observations?
Search Terms: "distributed consensus" "multi-agent belief aggregation" "causal
reasoning consensus" "distributed inference" "agent opinion pooling"
Expected Output: Consensus protocols for causal attribution. Handling conflicting
causal hypotheses. Confidence aggregation methods.
Coditect Application: Multi-agent Causation Tracker consensus mechanism.
Prompt 3.1.3 — Hierarchical Consequence Escalation
Research Question: What hierarchical patterns enable consequence severity escalation
from worker agents to orchestrator with appropriate filtering?
Search Terms: "hierarchical multi-agent" "escalation patterns" "agent hierarchy"
"supervisor-worker MAS" "exception handling agents"
Expected Output: Escalation decision frameworks. Signal filtering at hierarchy
levels. Orchestrator intervention triggers.
Coditect Application: Orchestrator-Workers pattern with consequence escalation.
3.2 Coordination Under Uncertainty
Prompt 3.2.1 — Partial Observability in Consequence Assessment
Research Question: How should agents reason about consequences when they have only
partial observation of system state?
Search Terms: "POMDP multi-agent" "partial observability" "belief state estimation"
"observation uncertainty" "state estimation agents"
Expected Output: Dec-POMDP formulations for consequence-aware execution. Belief
update algorithms. Information-gathering action selection.
Coditect Application: Handle incomplete test results and async consequence observation.
Prompt 3.2.2 — Coordination with Communication Failures
Research Question: How can consequence-aware coordination remain robust when
agent communication is unreliable or delayed?
Search Terms: "fault-tolerant coordination" "communication failure MAS"
"asynchronous coordination" "network partition tolerance" "eventual consistency MAS"
Expected Output: Fault-tolerant coordination protocols. Local consequence assessment
fallbacks. Eventual consistency strategies for shared state.
Coditect Application: Design resilient Consequence Mesh for distributed environments.
Category 4: Implementation Research
4.1 Token Economics
Prompt 4.1.1 — Observation Overhead Optimization
Research Question: What techniques minimize the token overhead of continuous
consequence observation in LLM-based autonomous systems?
Search Terms: "LLM token optimization" "efficient observation" "context window
management" "prompt compression" "selective attention"
Expected Output: Techniques for summarizing consequence signals without losing
critical information. Context window management strategies. Trade-offs between
observation fidelity and cost.
Coditect Application: Optimize CACA for Coditect's token economics (15x multiplier
challenge).
Prompt 4.1.2 — Selective Consequence Monitoring
Research Question: What criteria should determine which actions receive intensive
consequence monitoring vs. lightweight observation?
Search Terms: "adaptive monitoring" "risk-based observation" "selective tracing"
"monitoring overhead reduction" "sampling strategies"
Expected Output: Risk models for monitoring intensity selection. Adaptive sampling
algorithms. Cost-benefit frameworks for observation depth.
Coditect Application: Implement tiered observation strategy in Consequence Mesh.
Prompt 4.1.3 — Model Routing for Consequence Assessment
Research Question: How should consequence assessment tasks be routed across
different model tiers (Haiku/Sonnet/Opus) for optimal cost-quality balance?
Search Terms: "LLM routing" "model selection" "task complexity estimation"
"cascade models" "model switching"
Expected Output: Complexity estimation algorithms for consequence assessment tasks.
Routing decision frameworks. Quality-cost Pareto optimization.
Coditect Application: Extend Coditect's model routing strategy to CACA components.
4.2 Performance & Scalability
Prompt 4.2.1 — Real-Time Constraint Satisfaction
Research Question: How can consequence observation meet real-time constraints
for immediate feedback without blocking execution?
Search Terms: "real-time AI systems" "latency constraints" "async observation"
"deadline scheduling" "real-time monitoring"
Expected Output: Latency budgets for consequence assessment. Async processing
patterns. Deadline-aware scheduling algorithms.
Coditect Application: Define latency SLAs for Consequence Mesh temporal layers.
Prompt 4.2.2 — Scaling Causation Graphs
Research Question: How do causation tracking systems scale as action history grows
to millions of entries?
Search Terms: "graph database scaling" "temporal graph scaling" "causal graph
compression" "historical data management" "time-series graph"
Expected Output: FoundationDB scaling patterns for causation graphs. Archival
strategies. Query optimization for temporal causal queries.
Coditect Application: FoundationDB schema design for production-scale Causation Tracker.
Prompt 4.2.3 — Distributed Consequence Assessment
Research Question: How should consequence assessment be distributed across compute
nodes for large-scale autonomous development?
Search Terms: "distributed AI systems" "parallel assessment" "sharding strategies"
"distributed observation" "compute placement"
Expected Output: Distribution strategies for consequence observation. Data locality
optimization. Consistency models for distributed state.
Coditect Application: Cloud-native CACA deployment architecture.
Category 5: Compliance & Trust
5.1 Regulatory Alignment
Prompt 5.1.1 — FDA 21 CFR Part 11 for Autonomous Systems
Research Question: How should consequence-aware autonomous systems be designed to
meet FDA 21 CFR Part 11 requirements for electronic records and signatures?
Search Terms: "FDA Part 11 compliance" "audit trail requirements" "electronic
records validation" "CFR11 software design" "regulatory compliance AI"
Expected Output: Part 11 requirement mapping to CACA components. Audit trail
specifications. Validation documentation templates.
Coditect Application: Ensure CACA meets FDA compliance for healthcare customers.
Prompt 5.1.2 — HIPAA Technical Safeguards for Consequence Data
Research Question: What technical safeguards are required when consequence
observation data includes or derives from PHI?
Search Terms: "HIPAA technical safeguards" "PHI protection AI" "healthcare
data compliance" "HIPAA audit logs" "de-identification"
Expected Output: PHI detection in consequence data. De-identification requirements.
Access control specifications for consequence history.
Coditect Application: HIPAA-compliant Causation Tracker design.
Prompt 5.1.3 — SOC 2 for Autonomous Operations
Research Question: How should SOC 2 control mapping address autonomous
consequence-aware systems that make decisions without human intervention?
Search Terms: "SOC 2 autonomous systems" "control automation" "autonomous
audit" "SOC 2 AI controls" "trust services criteria AI"
Expected Output: SOC 2 trust services criteria mapping for CACA. Evidence
collection automation. Audit-ready documentation patterns.
Coditect Application: SOC 2 compliance framework for Coditect enterprise customers.
5.2 Explainability & Trust
Prompt 5.2.1 — Explainable Consequence Attribution
Research Question: How can consequence attribution decisions be explained to
human reviewers in an understandable and actionable format?
Search Terms: "explainable AI attribution" "XAI causal" "interpretable
causation" "explanation generation" "human-interpretable AI"
Expected Output: Explanation generation techniques for causal chains.
Visualization approaches for consequence propagation. Natural language
explanation templates.
Coditect Application: Human-readable consequence reports for checkpoint decisions.
Prompt 5.2.2 — Trust Calibration for Consequence Predictions
Research Question: How should consequence prediction confidence be calibrated
to align with actual prediction accuracy?
Search Terms: "confidence calibration" "prediction uncertainty" "calibrated
probabilities" "uncertainty quantification" "reliability diagrams"
Expected Output: Calibration techniques for consequence predictions.
Uncertainty communication strategies. Trust threshold determination methods.
Coditect Application: Reliable confidence scores for Adaptation Engine decisions.
Prompt 5.2.3 — Consequence Audit Trail Standards
Research Question: What audit trail standards enable forensic analysis of
autonomous decisions and their consequences?
Search Terms: "AI audit trail" "decision logging standards" "forensic AI
analysis" "accountability logging" "provenance tracking"
Expected Output: Audit trail schema specifications. Query capabilities for
forensic analysis. Retention and archival requirements.
Coditect Application: Compliance-ready audit trails for CACA operations.
Category 6: Validation & Evaluation
6.1 Benchmarking
Prompt 6.1.1 — Consequence Prediction Benchmarks
Research Question: What benchmarks exist for evaluating consequence prediction
accuracy in software development contexts?
Search Terms: "software impact prediction benchmark" "code change benchmark"
"defect prediction datasets" "regression benchmark" "SE evaluation datasets"
Expected Output: Survey of available benchmarks. Dataset characteristics.
Evaluation metric standards (precision, recall, lead time).
Coditect Application: Establish evaluation methodology for CACA consequence
prediction components.
Prompt 6.1.2 — Adaptation Quality Metrics
Research Question: How should the quality of plan adaptations be measured
when ground truth optimal adaptations are unknown?
Search Terms: "plan quality metrics" "adaptation evaluation" "replanning
benchmark" "dynamic planning metrics" "execution quality"
Expected Output: Proxy metrics for adaptation quality. Comparative evaluation
approaches. Human judgment correlation methods.
Coditect Application: Define KPIs for Adaptation Engine effectiveness.
Prompt 6.1.3 — End-to-End CACA Evaluation
Research Question: What evaluation frameworks assess the overall effectiveness
of consequence-aware autonomous execution systems?
Search Terms: "autonomous system evaluation" "end-to-end AI evaluation"
"system-level metrics" "holistic AI assessment" "integration testing AI"
Expected Output: Multi-dimensional evaluation frameworks. Simulation
environments for testing. A/B testing methodologies for production.
Coditect Application: Comprehensive CACA validation framework.
6.2 Safety Validation
Prompt 6.2.1 — Adversarial Consequence Testing
Research Question: How can consequence-aware systems be stress-tested against
adversarial inputs that attempt to exploit consequence assessment gaps?
Search Terms: "adversarial AI testing" "robustness testing" "red team AI"
"adversarial examples" "fault injection"
Expected Output: Adversarial testing methodologies for consequence assessment.
Failure mode catalogs. Red team exercise frameworks.
Coditect Application: Security validation for CACA in production.
Prompt 6.2.2 — Consequence Cascade Failure Analysis
Research Question: What techniques identify potential cascade failures where
consequence assessment errors propagate through the system?
Search Terms: "cascade failure analysis" "error propagation" "failure mode
effects" "systematic failure" "fault tree analysis"
Expected Output: Cascade failure modeling techniques. Critical path
identification. Circuit breaker placement strategies.
Coditect Application: Reliability engineering for CACA architecture.
Appendix: Research Execution Guidelines
Search Strategy
- Start with primary academic databases (arXiv, ACM DL, IEEE Xplore)
- Expand to domain-specific venues (ICSE, FSE, NeurIPS, ICML)
- Include industry research (Google AI, Microsoft Research, Meta AI)
- Check GitHub for open-source implementations
Quality Filters
- Prefer peer-reviewed publications
- Weight recent research (2022-2026) more heavily
- Validate claims against multiple sources
- Note replication studies and critiques
Synthesis Process
- Summarize key findings per prompt
- Identify contradictions between sources
- Extract implementation patterns
- Map to Coditect architecture components
Research agenda compiled: February 2026 Total prompts: 35 Estimated research hours: 80-120