agent-harness-construction

AI agent action spaces improve task completion rates by using stable tool names and schema-first inputs to ensure predictable behavior. Standardizing observation formatting and error recovery contracts further increases success rates.

Agents fail on ambiguous tool outputs because they lack standardized observation formatting and clear error recovery contracts. Defining contracts with status, summary, next_actions, and retry hints resolves these interaction failures and improves debugging.

The best way to format agent observations is standardizing tool responses with status, summary, next_actions, and artifacts fields. This structured formatting provides clear hints, retry instructions, and stop conditions for robust error recovery.

Context budgeting for ReAct and Function-calling patterns is managed by optimizing prompt and reference handling to stay within token limits. This architecture pattern ensures efficient context distribution alongside well-defined action spaces and deterministic tool outputs.

Standardizing tool definitions works with agent architecture patterns by recommending ReAct and Function-calling implementations. Schema-first inputs and deterministic outputs integrate with these patterns to provide stable tool names for predictable agent behavior.

Error recovery contracts for AI agent tools are needed when agents struggle with ambiguous outputs or poor error handling. Defining clear contracts with hints, retry instructions, and stop conditions ensures stable tool usage and higher task completion rates.