A 7-File Governance Layer to Prevent LLM Session Drift

A developer on r/LocalLLaMA shared a solution to prevent LLM coding assistants like Claude from silently undoing architectural decisions across sessions. Instead of treating LLM sessions as conversations, they now treat them as stateless processes that need a protocol.
The Core Problem
Every LLM session starts with zero memory. You re-explain, it re-interprets, and it drifts confidently. The developer noted: "You won't even notice until you are deep inside the project maybe three files deep or four files or who knows even on the last part of project."
The 7-File Governance Layer
The fix isn't a better prompt but a governance layer that any model can read and immediately operate within. The system uses seven files, each owning a specific concern with no overlap:
active_context.md- Session controller, defines what's in scope right nowcontracts.md- Behavioral law, data schemas, enum values, required behavioragent_core.md- Execution discipline, how to operate, validate, reportagent_project.md- Project intent, why this system exists, expected outcomesdecisions.md- ADR log, non-obvious choices and why they were acceptedbuild_plan.md- Module roadmap, implementation order and deliverablesstate.md- Living journal, what's done, what changed, what remains
Key Design Decisions
The developer explained two critical separations:
Separating contracts.md from agent_core.md: "When a behavioral conflict appeared, the model had no way to know which layer to defer to. Was this a schema rule or an execution preference? When they're separate, the hierarchy is unambiguous, contracts always win."
Including decisions.md: "I almost skipped it ('I'll just remember'). Three weeks later I couldn't reconstruct why we'd chosen Postgres over SQLite for a specific module. The ADR log exists precisely because 'I'll remember' is not a protocol."
The Operational Loop
Every session follows this order, no exceptions:
- Read
active_context.md→ extract what's in scope - Re-ground against
contracts.md→ behavioral rules locked - Confirm operating constraints from
agent_core.md+agent_project.md - Check
decisions.md→ don't reverse accepted choices - Execute only what
active_context.mdauthorizes, perbuild_plan.md - Validate with tests — don't declare done without evidence
- Update
state.mdwith factual outcomes - If a new non-trivial decision was made, log it in
decisions.md
Workflow Impact
The active_context.md scope lock proved particularly valuable: "Before this, I would start a session to fix a bug and then end up refactoring an unrelated module because 'it was right there.' Felt productive.........and it Was."
📖 Read the full source: r/LocalLLaMA
👀 See Also

No-Code Persistent Memory System for Claude Using Notion and MCP
A radiologist built a 'Cognitive Hub' in Notion that Claude reads and writes to through MCP, creating a structured knowledge base with a routing table to load only relevant information per conversation. The system has grown to 70+ pages after a month of daily use.

Running Two Claude Code Agents on the Same Repo with Git Worktrees
A Reddit user details how to run multiple Claude Code agents in parallel on the same codebase using git worktrees, avoiding file conflicts and enabling independent agent sessions.

Building a voice-controlled multi-agent system on top of Claude Code
A developer built a wake-word-activated voice loop for Claude Code that spawns sub-agents, parallelizes work, and auto-QAs results. Full technical breakdown including speaker verification and PID watcher.

AgentLens: Observability Tool for Multi-Agent AI Workflows
AgentLens provides unified tracing across Ollama, vLLM, Anthropic, and OpenAI, with cost tracking, an MCP server for querying stats from Claude Code, and a CLI for inline checks. It's self-hosted and runs locally via Docker.