Error Recovery and Retry Policy for the Agent Loop

PROJECT CONTEXT:
My agent handles happy paths but degenerates on failure: it retries the same broken call, loses track after errors, or gives up silently. The autonomous-agent literature is a catalog of loop-and-drift failures with known fixes.

GOAL:
Implement a layered recovery policy: transient retries with backoff, model-visible error feedback, loop detection, and honest escalation.

REQUIREMENTS:
- Error taxonomy: transient (retry), corrective (inform model), fatal (escalate)
- Automatic retry with exponential backoff for transient failures only
- Corrective errors returned to the model with cause and suggested next step
- Loop detection on repeated near-identical failing actions
- An escalation path that reports honestly what failed and what was tried

CONSTRAINTS:
- Never auto-retry non-idempotent operations without a guard
- Every retry and escalation must appear in the transcript

DEFINITION OF DONE:
- A transient network failure is retried and succeeds without model involvement
- A corrective error (bad args) leads the model to a fixed second call
- Three near-identical failures trigger the loop breaker with a strategy-change message
- A fatal failure produces an honest escalation summary, not silence or fabrication

COMMON FAILURES TO AVOID:
- Retrying everything including operations that mutate state twice
- Hiding errors from the model so it reasons from a false world
- No loop breaker: the agent burns its budget on one broken idea
- Silent give-up: the run ends with neither success nor an honest report