Closing the Loop: Automating AI Context from Audit Violations in CORE

Closing the Loop: From Audit Violation to AI Fix in One Command

CORE implements a constitutional governance system featuring 85 rules that audit codebases for architectural and safety violations. The system now automatically generates specific context-building commands to provide AI assistants with the exact context needed to resolve failures.

Why This Matters

The primary bottleneck in AI-assisted development is the manual selection of context, where developers must identify relevant classes and dependencies for a prompt. CORE solves this by treating the gap between audit findings and AI context as a mechanical mapping function, reducing a 10-minute manual task to a single command execution. This technical reality addresses the high cost of human-in-the-loop context assembly by leveraging AST force-adds and DB graph traversal to ensure the AI sees exactly what is necessary to resolve a specific violation.

Key Insights

• CORE utilizes a constitutional governance system with 85 rules to audit architectural boundaries and AI safety patterns (2026).
• The ‘core-admin context build’ command simulates the CoderAgent pipeline using Qdrant vector search and DB graph traversal for precise item inclusion.
• Audit formatters now map rule IDs like ‘ai.prompt.model_required’ directly to task types such as ‘code_modification’ to generate actionable hints.
• Context packages include semantic scores (e.g., 0.74) and source origins to allow developers to verify the AI’s view before code generation.
• The system is self-governing, capable of auditing its own newly written code for modularity debt and suggesting the next fix command immediately.

Working Examples

Command to build a context package for an AI assistant based on a specific symbol and task.

core-admin context build \
--file src/will/agents/coder_agent.py \
--symbol CoderAgent \
--task code_modification \
--output var/context_for_claude.md

Audit hint output showing the mechanical mapping from a violation to a fix command.

ERROR ai.prompt.model_required
Line 158: direct call to 'make_request_async()' detected.
core-admin context build \
--file src/will/agents/coder_agent.py \
--task code_modification \
--output var/context_for_claude.md

Practical Applications

• CORE governance system: Automatically detecting modularity debt in CLI resources and providing the direct context command for Claude to refactor the code.
• Manual context assembly pitfall: Developers manually constructing prompts often miss deep dependencies or provide insufficient context, leading to failed AI code generations.

References:

• https://dev.to/dariusz_newecki_e35b0924c/closing-the-loop-from-audit-violation-to-ai-fix-in-one-command-3aj8
• https://github.com/DariuszNewecki/CORE