ADR-010: Continuous Feedback Loop for AI Refinement

Status: Accepted Version: 1.0 Date: 2025-10-01 Supersedes: N/A Related ADRs: N/A Related PRDs: PRD-014, PRD-015

Context

The need for AI agents, particularly the artifact recommendation algorithm, to continuously learn and improve based on user interactions and outcomes.

Decision

Implement a continuous feedback loop that captures user interactions with cognitive artifacts and feedback on AI recommendations.

Rationale

This feedback is crucial for refining the recommendation algorithm, improving the relevance and utility of generated artifacts, and adapting AI agent behavior over time. It ensures that the SEA™ evolves with user needs and optimizes for cognitive amplification.

Alternatives Considered

One-Off Model Training

Rejected - Leads to static AI models that do not adapt to changing user needs or contexts.

Implicit Feedback Only

Rejected - May not provide sufficient signal for targeted model improvements.

Constraints

• MUST capture user interactions with cognitive artifacts
• MUST capture feedback on AI recommendations
• MUST use feedback to refine recommendation algorithms
• MUST support both implicit and explicit feedback mechanisms
• MUST NOT rely solely on one-off model training

Quality Attributes

• Adaptive and continuously improving AI
• Enhanced user satisfaction
• Optimized cognitive amplification
• Data-driven refinement of the SEA™

Bounded Contexts Impacted

• Cognitive Extension Layer
• AI Agent Runtime
• Feedback Processing
• Knowledge Layer

Consequences

Positive

• Adaptive and continuously improving AI
• Enhanced user satisfaction
• Optimized cognitive amplification
• Data-driven refinement of the SEA™

Negative

• Requires robust data collection and processing infrastructure
• Careful design of feedback mechanisms to avoid bias
• Ethical considerations around data usage

Additional Notes

✅ MVP