agent-harness-construction

To improve AI agent task completion rates, refine the tool's input schema and the agent's understanding of its output using schema-first definitions and observation formatting. This ensures deterministic outputs and clear execution paths.

The best way to design action spaces for LLM agents is creating stable, schema-first tool definitions. This approach guides ReAct and function-calling architecture patterns to ensure deterministic outputs and improved planning.

Tool responses for AI agents should be formatted to include status, summary, next actions, and artifacts. This observation formatting ensures the agent clearly understands the execution state and necessary subsequent steps.

Define error recovery contracts for AI agents by establishing clear rules for handling errors with root cause hints and safe retry instructions. This guides the agent in autonomously resolving execution failures.

You need to optimize an AI agent's action space and observation formatting when the agent struggles to plan, execute tools, or recover from errors, ultimately aiming to achieve higher task completion rates.

Yes, schema-first tool design works with ReAct and function-calling architecture patterns. It guides these architectures by providing stable tool definitions and deterministic outputs to improve agent planning and execution.