Agent Orchestration
The coordination layer that manages the lifecycle, routing, scheduling, and communication of multiple AI agents, ensuring tasks flow through the right agents in the right order with appropriate resource allocation.
Orchestration sits above individual agent loops. The orchestrator receives a goal, selects which agent or agents to activate, supplies the correct context and tools, monitors progress, and handles exceptions like timeouts, budget overruns, and failed tool calls. It also enforces lane discipline—ensuring each agent stays within its defined scope.
Orchestration patterns include sequential pipelines (output of agent A becomes input of agent B), fan-out/fan-in (many agents work in parallel then merge results), and event-driven routing (agents subscribe to event streams). Paperclip uses an event-driven pattern where tasks carry a status field that triggers the next agent in the chain.
Failure handling is the hardest part of orchestration. Robust orchestrators implement retry logic with exponential backoff, escalation paths to human reviewers, and dead-letter queues for tasks that exhaust retries. Observability tools like Langfuse are essential for diagnosing failures in multi-hop agent chains.