When to Build Your Own Agent Harness

Harrison Chase argues that an agent is more than a model: it combines a model, the context available to it, and a harness that orchestrates both. The harness decides what context reaches the model, when it arrives, how tools are invoked, and how observations return to the next step of the loop.

For many teams, the practical starting point is a general-purpose, off-the-shelf harness. It offers quicker time to value while models are operating on familiar tasks. As a use case moves further out of distribution, teams can introduce middleware, hooks, tool handling, summarization, memory, sandboxes, sub-agents, or more explicit cognitive architectures. The goal is not to replace the agent loop, but to customize it at the points that matter for a domain.

Chase pairs harness engineering with evaluation and observability. Private evaluations establish what good looks like; traces, feedback, decisions, and institutional context create the raw material for a continuous learning loop. Benchmark runners such as Harbor make it possible to compare models, harnesses, reasoning effort, accuracy, latency, and cost across sandboxed tasks.

Observability is essential because an agent failure may result from the model, but often from insufficient or poorly accumulated context. Teams should make trajectories, tool calls, and context assembly inspectable, collect feedback from product UX and online evaluators, curate trace data, then run experiments. The resulting flywheel is simple in outline: run the agent, collect traces, find patterns, and fix the system.

LangSmith Engine is offered as an example of automating that loop. It examines trace data, creates issue boards, links evidence, and proposes changes to prompts, context, and harness code. In the closing discussion, Chase recommends beginning with standard harnesses such as Deep Agents, Codex, or Claude Code, then moving toward custom designs when a domain needs greater specialization, predictability, or control.