Cekura Self-Improving Agent

Purpose

Close the loop on agent prompt and tool-config quality. Ingest evaluation signal (scenario IDs to run, completed runs, a result batch, or production call logs), classify failures, diagnose where the prompt or tool config has gaps / conflicts / ambiguities, propose targeted edits, apply them, and re-run validation — iterating until the agent reaches 100% pass rate on the validation set or the iteration cap is reached.

Exit gate. The voice/channel/infra filter informs what to fix (the Optimization phase only proposes edits for prompt-following failures), not when to stop. Any remaining failure of any class keeps the loop alive. Only the iteration cap or a genuine 100% pass ends the loop.

Currently supported for VAPI and self-hosted (websocket). Retell support is intentionally disabled and will be re-enabled in a future revision.

Architecture — orchestrator over a sequence of focused sub-phases

This SKILL.md is a thin orchestrator. Optimization is split into five sub-phases living in phases/optimization/, with Setup, Overfitting Gate, and Eval as standalone phases on either side: