Decision logs do not make an agent self-improving.

Video caption

Decision logs do not make an agent self-improving.

If you tune on every logged case, the holdout disappears.

Baseline, untouched holdout, accepted-work metric.

My rule: Log to replay; replay to adjudicate; promote with rollback.

#EricFieldNotes

Full written post / accessibility read

Collecting every route decision feels like learning. The Avid Jev build records useful decision receipts, but does not claim a measured coding-quality gain or self-training. A log gives you cases to study; it does not tell you what policy to promote.

Imagine changing the router after inspecting all recent failures. Replaying the same cases may look excellent while new tasks get worse. If human rescue and retries are omitted, apparent cost savings may simply move work to an owner queue.

Freeze a baseline policy and a versioned candidate. Reserve untouched tasks by decision class; label outcomes independently. Include latency, retries, review time and regressions. Have an owner inspect slices before promotion, with a rollback route already tested.

Use receipts to find counterexamples, not to declare the system self-improving. Promote only after a task-matched comparison shows accepted-work gain without unacceptable regressions. Do this because a decision trail is a record of behavior, not a proof that behavior improved.

#EricFieldNotes

Four-beat scene transcript

1. Decision logs do not make an agent self-improving.

Collecting every route decision feels like learning. The Avid Jev build records useful decision receipts, but does not claim a measured coding-quality gain or self-training. A log gives you cases to study; it does not tell you what policy to promote.

Visual: A receipt records a choice, not a better outcome.

2. The easy replay flatters the new policy.

Imagine changing the router after inspecting all recent failures. Replaying the same cases may look excellent while new tasks get worse. If human rescue and retries are omitted, apparent cost savings may simply move work to an owner queue.

Visual: If you tune on every logged case, the holdout disappears.

3. Make policy changes an experiment.

Freeze a baseline policy and a versioned candidate. Reserve untouched tasks by decision class; label outcomes independently. Include latency, retries, review time and regressions. Have an owner inspect slices before promotion, with a rollback route already tested.

Visual: Baseline, untouched holdout, accepted-work metric.

4. Let evidence earn adaptation.

Use receipts to find counterexamples, not to declare the system self-improving. Promote only after a task-matched comparison shows accepted-work gain without unacceptable regressions. Do this because a decision trail is a record of behavior, not a proof that behavior improved.

Visual: Log to replay; replay to adjudicate; promote with rollback.

Research and claim limits

• Avid: How to Build Agentic Harness using Jev (S109)
• TypeSafe AI: Workflow evals (S101)

The examples identified as illustrative or simulated are design probes, not reported incidents. Vendor specifications do not establish workload performance.