Mike Chambers distinguishes agents people use from agents developers build, then defines a harness as everything around the modelAn AI agent harness is the software framework that packages a model with tools, instructions, context management, execution controls, and user interaction.. For a coding assistant, that includes memory, skills, tools, model context protocol servers and engineering standards. For a production agent, the harness becomes a larger operational system.
The production checklist includes the agent loopAn agent loop repeatedly observes a task's state, chooses an action, checks the result and decides whether to continue or stop., scaling, payments, memory, identity, skills, runtime, context management, observabilityAI agent observability makes an agent's state, actions, tool use, failures, resource use, and outcomes visible enough to understand and operate it. and evaluations. Mike Chambers argues that these components should not be packed into one container when they have different scaling, security and lifecycle needs.
The live demonstration progresses from a local agent with tools to one with session memoryAI agent memory is stored information that an agent can retrieve and use across steps, sessions, or changing contexts., then to a cloud deployment with isolated multi-user runtimeAn AI agent runtime is the execution system that manages an agent's model calls, tools, state, permissions, and task lifecycle., separately managed memory, local development, infrastructure-as-code deployment, traces and debugging. The broader lesson is to keep the model replaceable while making the surrounding system composable and observable.
Watch on YouTube
