Agent Harness Engineering vs. Loop Engineering vs. Graph Engineering
Author(s): ML Point
Originally published on Towards AI.
A practical guide to the three architecture layers people keep mixing together
The confusion is understandable. All three ideas sit around the same model, all three influence reliability, and all three can contain “loops.” But they are not synonyms. They describe different engineering decisions, and the distinction matters the moment an agent leaves a demo notebook and starts touching files, APIs, customers, or production code.
The article explains that reliable agent systems are built from three distinct (often overlapping) layers: harness engineering (the surrounding machinery that provides context, tools, permissions, persistence, control, safety, and observability), loop engineering (the repeated observe/act/verify cycles with explicit triggers, evidence-based stopping rules, and stacked or event-driven/improvement loops rather than “keep trying”), and graph engineering (explicitly modeling workflow topology as nodes and edges to control allowed next steps, branching, concurrency, state transitions, and recovery paths). It argues that mixing these up leads to expensive failures—such as drawing graphs before understanding behavior, letting the same model self-grade without safeguards, creating unbounded retry loops, stuffing the harness with too many tools or overly broad permissions, or blaming the model for orchestration problems that belong to the wrong layer. Finally, it offers a production checklist and a memory aid: harness makes the model operate, loops make execution iterative and verifiable/resumable, and graphs make complex control flow inspectable and controllable—when designed together with clear responsibility at each layer.
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Note: Article content contains the views of the contributing authors and not Towards AI.
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