agent-harness-construction

To improve AI agent planning, refine the action space by enforcing stable, explicit tool names and schema-first inputs. You can increase completion rates by structuring deterministic outputs and applying robust error recovery contracts across complex workflows.

Designing tool observations requires including status, summary, next_actions, and artifacts in every response. This observation design ensures predictable downstream processing and stable convergence for function-calling and ReAct patterns.

Implement error recovery by defining a contract for every error path that includes root-cause hints, safe retry instructions, and explicit stop conditions. This prevents infinite loops and ensures robust agent convergence during complex workflows.

Choosing between ReAct, function-calling, or hybrid flows depends on your workflow complexity. Architecture pattern guidance recommends tracking metrics like completion rate and retries to benchmark which approach yields higher task success for your specific use case.

Agents fail to converge due to bloated context windows and ambiguous action spaces. Apply context budgeting by minimizing system prompts, moving long guidance to on-demand skills, and keeping phase-boundary communications compact to maintain stable task execution.

Yes, you can apply this harness pattern to React-based data aggregation workflows. The quick start provides a minimal agent task that plans tool usage, demonstrating how schema-first inputs and deterministic outputs manage data processing effectively.