EPOCH: An Agentic Protocol for Multi-Round System Optimization

TL;DR

EPOCH is a structured protocol that guides multi-round autonomous system optimization, ensuring reproducibility and stability across diverse tasks and environments.

Contribution

It introduces a unified, multi-phase protocol with role-constrained stages for systematic autonomous system self-improvement.

Findings

Effective in heterogeneous environments

Enhances reproducibility and traceability

Facilitates production-oriented workflows

Abstract

Autonomous agents are increasingly used to improve prompts, code, and machine learning systems through iterative execution and feedback. Yet existing approaches are usually designed as task-specific optimization loops rather than as a unified protocol for establishing baselines and managing tracked multi-round self-improvement. We introduce EPOCH, an engineering protocol for multi-round system optimization in heterogeneous environments. EPOCH organizes optimization into two phases: baseline construction and iterative self-improvement. It further structures each round through role-constrained stages that separate planning, implementation, and evaluation, and standardizes execution through canonical command interfaces and round-level tracking. This design enables coordinated optimization across prompts, model configurations, code, and rule-based components while preserving stability, reproducibility, traceability, and integrity of evaluation. Empirical studies in various tasks illustrate the practicality of EPOCH for production-oriented autonomous improvement workflows.