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Bring Your Own Harness to AC2 | Applied Compute

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A production agent is a complex engineered system around LLMs. It includes deliberate management of context, tools, sandboxing, authentication, memory, and access to proprietary systems. Connecting that system to a post-training stack often requires porting the agent loop into a new runtime or building custom infrastructure to capture training trajectories. This work is expensive and can introduce train-test mismatch, where the policy is optimized in a lab that simulates the harness, and then struggles when faced with the nuances of production. But what if instead of requiring users to adopt a new agent framework or re-instrument their harness with our tracing primitives, we could just replace the LLM response endpoint? The harness can stay where it already runs, and the trainer has the same interface as inference. Behind the scenes, the trainer captures all the tokens and generation metadata needed for gradient steps, plus rich tracing and observability data inferred from the completi

A production agent is a complex engineered system around LLMs. It includes deliberate management of context, tools, sandboxing, authentication, memory, and access to proprietary systems. Connecting that system to a post-training stack often requires porting the agent loop into a new runtime or building custom infrastructure to capture training trajectories. This work is expensive and can introduce train-test mismatch, where the policy is optimized in a lab that simulates the harness, and then struggles when faced with the nuances of production. But what if instead of requiring users to…

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