EPOCH: An Agentic Protocol for Multi-Round System Optimization

TL;DR

Empirical studies in various tasks illustrate the practicality of EPOCH for production-oriented autonomous improvement workflows and enables coordinated optimization across prompts, model configurations, code, and rule-based components while preserving stability, reproducibility, traceability, and integrity of evaluation.

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.

Figures (3)

• Figure 1: Architecture of EPOCH. EPOCH contains two phrases. Phrase 1 use seed planner and baseline executor to generate the evaluation metric compute script, baseline script, and baseline metric. Phrase 2 is a multi-round process, which improve the final optimization results through the orchestrator, investigator, executor, and reviewer. It generates the final script and evaluation metric at the end.
• Figure 2: Example EPOCH task and run artifact directory structure.
• Figure 3: Compact generic EPOCH task specification.