AI coding context loss
AI coding assistants can read a repository and still lose the decisions that matter: which auth guard passed review, which webhook pattern is approved and which deployment fix solved the last incident. Moresq Corpus makes that implementation memory searchable and accountable.
When an agent forgets a prior decision, it may regenerate a plausible alternative with different assumptions. The cost appears later as review time, inconsistent behavior, duplicated bugs and another round of debugging.
A retrieval-first workflow searches compact implementation cards before writing commodity code. The agent receives the relevant pattern, provenance and constraints, then adapts only names, paths and project-specific glue.
A useful match includes source, review state, license status, quality signals and revocation information. That makes reuse inspectable for security-sensitive helpers, persistence code and deployment workflows.
Start with a short intent query. Search is cheap and returns compact metadata before the agent spends tokens on a full retrieval.
Open the strongest match only when provenance, license, review state and project fit justify reuse.
Adapt the known pattern, record the outcome and turn misses or corrections into better future retrieval signals.
Corpus is not a promise that every retrieved snippet is correct, a replacement for code review, or a silent training dump of private chats. It is a governed reuse layer: the process verifies provenance and review evidence, while the developer remains responsible for fit, testing and the final change.
AI coding assistants can read a repository and still lose the decisions that matter: which auth guard passed review, which webhook pattern is approved and which deployment fix solved the last incident. Moresq Corpus makes that implementation memory searchable and accountable.
Moresq Corpus lets coding agents search reusable implementation cards, retrieve relevant snippets with proof receipts, and learn from misses, applies and corrections.
No. Retrieval can reduce repeated work and expose provenance, but developers still need to check project fit, security implications and test results before shipping.
Related guides
Agent repo memory for Claude Code, Codex, Cursor and MCP clients: retrieve proven implementation patterns before regenerating code.
Verified code snippets with source metadata, review status, proof receipts and revocation for AI coding agents.
A retrieval-first coding workflow where agents search known patterns, retrieve compact context and generate only the project-specific adaptation.
An MCP corpus for AI coding agents: search reusable implementation cards, retrieve snippets and attach proof receipts.