agent-harness-construction

AI agent completion rates improve when you enforce stable, explicit tool names and narrow inputs to increase determinism, standardize observations with status and next_actions, and document clear error-recovery contracts to minimize retries.

The best way to format observations for AI agents is to standardize responses with structured fields including status, summary, next_actions, and artifacts, which accelerates closed-loop iteration and improves overall task convergence.

To design an action space that ensures deterministic outputs, enforce explicit tool naming and apply an input schema-first design approach with narrow inputs, reducing ambiguity and stabilizing the agent's planning and execution behavior.

An error-recovery contract works by providing clear, documented instructions for handling tool call failures, allowing the agent to understand its options and recover gracefully, which significantly minimizes unnecessary retries and context bloat.

Yes, you can apply ReAct, function-calling, or hybrid patterns to guide architecture and measure agent performance by tracking completion rates, retries, and cost, ensuring the chosen pattern fits your specific automation context.

AI agents get stuck in retry loops when lacking compact context budgeting and clear recovery contracts, causing repeated failures; optimizing observation formatting and documenting error-recovery protocols prevents these loops and improves convergence.