
RAG Retrieval Is Not Grounding Evidence
Why finding the right document in a RAG pipeline does not prove the final answer is supported, and how to trace the evidence boundary that failed.
An archive of decisions, failures, changes, and shipped work. The record stays useful by keeping the sequence—and the limits—visible.

Why finding the right document in a RAG pipeline does not prove the final answer is supported, and how to trace the evidence boundary that failed.

Why third-party agent skills and plugins need an explicit admission decision before versioning, regression tests, and runtime containment can do their jobs.

A source-grounded comparison of frontier-AI pacing proposals by what they constrain, what triggers restraint, who verifies the condition, and what allows progress or deployment to continue.

Why testing MCP tools requires a separate semantic behavior check after protocol and schema validity, so accepted requests are tested for the effects their parameters promise.

Why AI confidence only becomes decision-useful when uncertainty is meaningful enough to change what the system or user may do next.

Why useful agent autonomy should be judged against the delegated job: what counted as done, which intervention was expected, what side effects were allowed, and what evidence proved completion.

Why parallel-agent workflows need explicit rules for evidence, conflict, verification, and authority before many provisional branch results can become one trusted answer.

What recent frontier-AI control failures establish, what they only suggest, and why rejecting false countdown precision is not the same as dismissing catastrophic risk.

An analysis of reviewable AI edits, exact proposal state, approval granularity, and the boundary between generating a change and making it authoritative.