Production AI agents depend more on their harness than on the model alone

A model demo becomes a production agent only after engineers add the surrounding systems that keep it available, observable and within defined limits. An InfoQ architecture guide calls that layer the agent harness: memory, tool access, model routing, retries, guardrails, cost controls and diagnostic traces.

The guide divides the work into development and operations. Development extends what the model can do and defines acceptable behavior. Operations handles real traffic, failures, scaling and monitoring, making much of the job familiar DevOps work rather than a new form of model research.

Teams can use a managed harness such as AWS AgentCore or assemble a self-managed stack using components such as LangChain, a gateway and Kubernetes. Both approaches can provide similar core capabilities. The practical choice is whether speed and reduced maintenance matter more than portability, infrastructure control and the ability to tune each component.

The recommendation is to begin with the smallest harness that meets a real product requirement. Building elaborate routing and governance before usage demonstrates a need adds cost without improving outcomes. Evaluation criteria and hard safety boundaries must come from the product team; neither the underlying model nor a managed platform can define them automatically.