Free Lesson
Build a Self-Repairing Context Loop for AI Data Agents
Part of Build Your AI Product Analyst
45 min
Nov 4, 2026 1:00 PM
What you'll learn
Detect when an answer is drifting
Spot the signal that an agent's number is off: the same question returning different results across runs.
Have the agent fix its own definitions
Let the agent trace the bad answer back to a stale metric definition and rewrite it in the repo.
Re-run and watch the variance collapse
Run the same question again against the repaired context and see the answers converge on one number.
Why this only works with repo-based context
Definitions live in files the agent can read and edit, so a fix persists instead of vanishing when the chat ends.
Why this topic matters
Most AI analysis quietly rots: a metric gets redefined, the agent keeps answering from stale context, and nobody notices until the number is wrong. The fix is not more prompting. It is a loop where the agent detects the drift, repairs its own definitions in the repo, and re-runs until variance collapses. We build that loop live, so you watch the agent catch itself and land on one honest answer.
