agent-harness-construction

AI agent task completion often fails due to poorly designed action spaces, uninformative observation formatting, and inadequate error recovery mechanisms. Optimizing tool definitions with explicit schemas and structured outputs directly addresses these low completion rates.

Designing an action space for autonomous agents requires creating stable, explicit, schema-first tool definitions. You must apply appropriate granularity to separate micro-tools for high-risk actions like ticket escalation from standard medium-risk query lookups.

The best way to format observations is using structured tool outputs that include status, summaries, and actionable next steps. This deterministic output shape prevents wasted operational resources and reduces failed task attempts in hybrid agent systems.

Implement error recovery contracts by defining standardized templates that provide root cause hints and safe retry logic. This ensures agents can handle structured deterministic workflows without getting stuck during execution.

This optimization approach works for both structured deterministic workflows and exploratory tasks. It satisfies production-grade requirements by defining schema-first tool inputs and context budgeting best practices for hybrid ReAct plus function-calling architectures.

Apply context budgeting by standardizing observation formats and defining deterministic output shapes. This reduces wasted operational resources and ensures your autonomous agent operates efficiently within token limits during complex workflows.