Interactive Explainer

Agent Harness Anatomy

The model is only half the agent. The harness — context delivery, tool interfaces, planning artifacts, memory, guardrails, and evaluation loops — is the scaffolding that determines whether an AI agent succeeds or fails on real tasks. Explore each system below, then simulate how harness quality changes outcomes.

Context deliveryTool interfacesPlanning artifactsMemoryGuardrailsEval loops

The Six Systems of a Harness

Click or tab through any component in the diagram. Requests flow down from the task; observations flow back up through the loop.

Real taskContext deliverywhat the model seesPlanning artifactsplans, todos, subgoalsMemory & statescratchpads, summariesTool interfacesactions on the worldModel corereasoning inside the loopGuardrailspermissions, budgets, checksEvaluation loopsdid it actually work?safe outputsverified result

Harness Quality Simulator

Same model, different harness. Set the maturity of each system, then run a simulated 12-step task and watch where the run breaks down. Scores use a simple compounding model: each step must survive every subsystem.

--task success

Adjust the sliders and press Run simulated task to see how far the agent gets.

No run yet. The trace of each simulated step will appear here.

Design Primitives

Recurring primitives from harness engineering practice — small ideas that compound across long tasks.

Fresh context per step

Rebuild the prompt from durable state instead of letting a raw transcript grow. Stale context is the quietest failure mode in long-horizon agents.

Legible tools

Few, well-named tools with strict schemas and rich error messages beat many overlapping ones. The error message is part of the interface.

Externalized plans

Plans, todo lists, and subgoal files live outside the model so progress survives context resets and can be inspected by humans.

Verification over trust

Never accept the agent's claim of success. Run tests, diff outputs, and check invariants after every meaningful action.

Budgets everywhere

Cap steps, tokens, retries, and spend. A harness that cannot stop an agent cannot be trusted to run one.

Recoverable failure

Design for retry: idempotent actions, checkpoints, and summaries so a failed step costs one step, not the whole run.

Harness Design Checklist

Score your own agent scaffold. Check what you have, then copy the checklist to share or file as an issue.

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