agent-harness-construction

Design AI agent action spaces by creating stable, explicit tool names and schema-first inputs. This ensures robust interaction with multiple APIs and improves overall task completion rates by providing clear, deterministic output shapes.

Format AI agent observations by ensuring tool responses include status, summary, next actions, and artifacts. This structured observation formatting helps agents process tool outputs effectively and manage their context budget efficiently.

Handle API errors in AI agent development by defining an error recovery contract that provides root cause hints and retry instructions. This approach allows agents to gracefully handle failures and automatically adjust their next actions.

Use ReAct or Function-calling architecture patterns when designing new agents that interact with multiple APIs and require complex context budgeting. These patterns support stable tool definitions and efficient error recovery for higher task completion rates.

AI agents lose context when tool responses inline excessive data instead of managing the context budget efficiently. Prefer references over inlining in tool observations to maintain status, summary, and next actions within the context window.