meta-iterate

Automated multi-round optimization for an AI agent is orchestrated by coordinating iterative test, prompt change, review, and re-test loops. This preserves reproducible plan files and enforces backup synchronization to ensure safe, resumable optimization cycles.

Managing interrupted test runs during agent optimization requires generating timestamped iterate plans that persist state. The system automatically recovers interrupted runs, enforces backups, and synchronizes status to safely resume multi-iteration workflows.

Implementing baseline, sampling, and full verification strategies for prompt engineering involves applying multi-phase testing. The system runs warmup samples, creates a baseline, iterates on sampled cases, and validates prompt changes on the full testcase set before applying them.

Using subagents to review and engineer prompts during automated testing cycles is fully supported. The orchestration system invokes meta-prompt-engineer and meta-reviewer subagents to apply changes and write learnings and changelogs across iterations.

Preventing regressions when iterating on agent performance requires operational guardrails that enforce strict stop and rollback rules. The system manages plan persistence and status synchronization to avoid quality drops across testcases.

Iterative agent optimization scales to large testcase sets by using a multi-phase strategy. It handles warmup, baseline creation, sampled iterations, and full verification across testcases to improve output quality safely at scale.