agent-harness-construction

To design an AI agent action space, define explicit, stable tool names and narrow inputs to ensure deterministic planning and robust execution across multi-tool workflows.

Observation formatting structures tool responses to include status, summary, next_actions, and artifacts, enabling agents to reliably process outputs and progress toward task completion.

Error recovery contracts define safe retry paths and stop conditions for tool calling, allowing agents to handle failures gracefully and maintain reliable execution without infinite loops.

Multi-tool agents fail to converge when action spaces lack explicit definitions or observation formatting is poor, causing unpredictable tool invocation and preventing measurable benchmark progress.

Yes, you can optimize context budgeting by minimizing prompt size and using reference materials, which reduces token overhead and improves deterministic tool invocation during agent planning.

Yes, multi-tool agents require explicit action space design and benchmarking to verify deterministic tool calls, robust error handling, and measurable task completion rates across varied environments.