The term "harness" designates everything surrounding an AI model within an agent. Birgitta Böckeler proposes a conceptual framework specific to coding agent users, distinguishing the internal harness (built by the agent's creator) from the external harness (that users build for their own context).
The model is organized around two complementary mechanisms. Guides (feedforward controls) anticipate the agent's behavior and steer it before it acts: coding conventions, specifications, skills, MCP servers. Sensors (feedback controls) observe after the action and allow the agent to self-correct: linters, tests, static analysis, AI code reviews. Each mechanism can be computational (deterministic, fast, reliable) or inferential (semantic, more costly, non-deterministic). Both are necessary: without feedback, the agent repeats its mistakes; without feedforward, it encodes rules without knowing whether they work.
The article applies the shift left principle: distributing controls as early as possible in the development cycle. Fast controls (linters, unit tests) run before the commit, while costlier ones (mutation testing, architectural review) run in the integration pipeline. Continuous sensors monitor code drift and production metrics.
Three regulation categories are identified. The maintainability harness is the most mature, relying on existing tooling (linters, complexity analyzers, test coverage). The architectural fitness harness reuses the concept of fitness functions for non-functional characteristics. The behavior harness remains the major challenge: how to verify that the application does what it is supposed to do? AI-generated test suites are not yet sufficiently reliable.
The concept of harnessability underscores that not all codebases are equally amenable to harnessing. Typed languages, abstract frameworks, and modular architectures offer more purchase. Ashby's law of cybernetics justifies harness templates: by reducing the variety of possible topologies (dashboard, CRUD, event processor), a complete harness becomes more attainable.
The author concludes that human experience remains an irreplaceable "implicit harness." The goal is not to eliminate the human but to direct their attention to what matters most. Harness engineering is a continuous engineering practice, not a one-time configuration, and many questions remain open regarding harness consistency, evaluation, and orchestration.