EP-Harness Turns AI Coding Agents Into Managed Team Members
Most teams still treat AI coding as a solo activity. EP-Harness shows what happens when agent work gets the same platform discipline as human work—reviewable prompts, structured context, execution logs, and closed-loop feedback—which is the missing layer between a clever demo and a production R&D system.
Local AI coding tools leave four gaps when a team scales up: prompts go unreviewed, experience stays siloed, agent activity lacks visibility, and the delivery chain never closes. EP-Harness addresses all four by treating agents as managed team members with identities, tasks, runtimes, and audit trails. It is a secondary development of the open-source Multica platform, further wired into Dewu's internal systems—Feishu, code repos, deployment pipelines, and log platforms—so agents operate inside the company's actual R&D process rather than a generic chat window. A unified `Backend.Execute` contract lets Claude, Codex, OpenCode, and ACP backends plug into the same execution lifecycle. The platform also introduces Loop Engineering for automated, recorded, and decision-gated work like upstream release analysis and production log inspection. In production, automated governance cut a recurring error from 2,400-plus occurrences every four hours down to single digits, and multi-agent collaboration now spans proposal, development, review, and archival stages with full decision traceability.
Prompt quality stops being an individual skill problem the moment a platform makes prompts reviewable, versionable, and reusable—exactly the shift that turned code from personal craft into team engineering.
The four-layer model (prompt → context → harness → loop) is a useful maturity ladder: most tools stop at layer two, which is why enterprise adoption stalls.
Wiring agents into internal systems like Feishu, proprietary deployment pipelines, and log platforms is the real integration work; the model itself is the easy part.
Loop Engineering's value is not automation but auditability—every cycle records a judgment and a next step, which is what makes it governable rather than runaway automation.