agentic-engineering

An autonomous AI agent loop safely executes tools by centralizing execution in an isolation layer that never raises exceptions, returning errors as observations for the next iteration. This prevents crashes during multi-step tool execution.

Structured output enforcement validates LLM responses against a specified Pydantic model and applies correction-on-parse-failure instead of crashing. This ensures the agent loop iterates until a strictly validated output is produced.

State checkpointing and observability across multi-agent iterations are managed by saving an agent checkpoint after each loop iteration and emitting OpenTelemetry spans. This provides per-iteration tracing and reliable state persistence.

An agent loop crashes or hangs when tool execution raises unhandled exceptions or lacks max-iteration termination. Provider-agnostic tool execution with non-raising error handling and max-iteration limits stops the agent reliably at success or terminal error.

Yes, you can use Pydantic for structured output validation in multi-agent orchestration by enforcing strict parsing of the final result. The agent applies correction-on-validation-failure to ensure the output matches the specified model.

Agentic-engineering designs recursive Plan-Act-Observe loops that call Claude repeatedly with tool selection, execution, and observation across multiple LLM calls. It implements context window management, state checkpointing, and structured output validation.