Reading
Agency is a meta-harness framework that runs other AI coding agents through automated multi-step workflows. It uses a layered architecture to decouple domain logic from the backend and supports various workflow modes such as analysis, repair, planning, and monitoring.
Section 01
Agency is a meta-harness framework that runs other AI coding agents through automated multi-step workflows. It uses a layered architecture to decouple domain logic from the backend and supports various workflow modes such as analysis, repair, planning, and monitoring.
Section 02
With the rapid improvement of AI Coding Agents' capabilities, how to effectively "harness" these agents has become a new challenge in the field of software engineering. Simple prompt engineering can no longer meet the needs of complex software development, and the industry has begun to call for a more systematic approach—Harness Engineering.
Agency was born in this context. It is a meta-harness framework that can run other AI coding agents and grant them more "autonomy" through automated multi-step workflows. Although the project is still in the research phase, its architectural design and engineering philosophy have shown a deep understanding of Harness Engineering.
Section 03
Harness Engineering is an emerging paradigm in AI-assisted software development. Unlike traditional "prompt engineering" which focuses on single model calls, Harness Engineering focuses on:
The Agency's README references a series of important articles on Harness Engineering, including authoritative sources such as OpenAI, Anthropic, and Martin Fowler, showing the project's in-depth thinking in this field.
Section 04
Agency provides five core functional modules, each corresponding to a specific scenario in software development:
Section 05
The agent scans the codebase, identifies potential issues, and submits the found issues to the issue tracker. This function is similar to automated code review, but executed by AI agents, which can discover deeper design and architectural issues.
Section 06
Supports two modes: issue-based repair (obtained from the tracker) and specification-based repair (obtained from files or prompts). The Fix function runs an adversarial cycle of implementation → review → feedback processing, and finally submits a Pull Request.
Section 07
Continuous loop mode: Fix ready issues, perform analysis when the queue is idle, then sleep. This is Agency's most "autonomous" feature—agents can continuously monitor the codebase status, proactively discover issues, and fix them.
Section 08
Interactive session: The agent explores the codebase, discusses options with the user, and generates structured plan files into the .agency/plans/ directory. This function emphasizes human-machine collaboration and finds a balance between automation and human decision-making.
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