agent-harness-construction

Low AI agent task completion rates are often caused by poorly designed action spaces, vague tool definitions, unformatted observations, and missing error recovery contracts that force frequent retries. Redesigning these tool definitions and observation formats can significantly improve first-pass success rates.

To design tool definitions for better AI agent task success, build granular, schema-first tool inputs categorized as micro, medium, or macro tools. Match these tools to the specific operation risk and round-trip overhead to reduce ambiguity and execution failures.

Context budget optimization for production-grade agent systems manages the token limits and informational payload passed to the model. Standardizing observation output formatting with status, summary, and next actions ensures agents process tool results efficiently without exceeding context limits.

Add explicit error recovery paths to a customer support agent by standardizing tool observations to include status, summary, and next actions. This provides the agent with structured fallback instructions, reducing failed tasks and unnecessary retries during ticket routing or refund requests.

Schema-first tool design works effectively for DevOps automation agents and other structured task scenarios. By defining granular tool inputs and standardized observation formatting, agents can reliably execute operations with reduced round-trip overhead and clear error recovery mechanisms.