agent-harness-construction

To design an AI agent action space, define explicit, stable tool names and structured input schemas. This action-space design improves planning predictability by ensuring the agent selects tools deterministically across multi-step decision tasks.

Standardize tool responses and define explicit error-recovery contracts to reduce AI agent failures. Standardized observation schemas and recovery contracts allow autonomous workflows to handle exceptions gracefully without excessive retries.

Select planning patterns like ReAct, function-calling, or hybrid approaches for multi-step agent orchestration. These architectures guide robust behavior across data collection and system orchestration tasks by structuring how actions are sequenced.

This approach suits enterprise-grade data-collection workflows requiring deterministic tool interfaces and clear observation schemas. It applies to building end-to-end agent workflows in software engineering and research contexts where robust, safe operation is critical.

Build an end-to-end agent workflow by providing a concrete blueprint with a sample tool set and a minimal plan. This demonstrates planning, tool-calling, and error-recovery using structured interfaces and deterministic output formats.

You must provide structured tool interfaces, deterministic output formats, clear observation schemas, and explicit error-recovery contracts. These prerequisites ensure robust, safe operation when the agent autonomously coordinates a sequence of tools.