AI Agent Orchestration

As soon as a system has more than one agent or a handful of tools, the hard part shifts from "prompt the model" to coordination: ordering, data passing, concurrency, and failure handling. Real orchestration has to cover:

• Multi-step and multi-agent workflows that pass state between stages.
• Tool execution — calling APIs, databases and services as first-class steps.
• Parallel execution of independent branches, then joining their results.
• Retries on individual steps without restarting the whole run.
• Human approvals inserted at the right points.
• Observability across the whole run, not per isolated call.

Steps with shared, durable state

Each model call, tool call or agent is a step. The orchestrator runs steps and checkpoints their outputs into one shared run state, so later steps can build on earlier ones and the whole run can resume after a failure.

Sequential and parallel

Independent work — enriching three sources, calling two APIs — should run in parallel and join, while dependent work runs in order. Orchestration expresses both without you hand-rolling concurrency and error handling each time.

Failure handling as part of the graph

Retries, backoff and approvals aren't bolted on — they're properties of steps in the workflow. A failed tool call retries; a risky step waits for a human; the rest of the run is unaffected.

• Visual canvas. Compose multi-step, multi-agent workflows and see the whole graph at once.
• Tools as steps. Call your own APIs and databases as nodes, with keys and secrets encrypted at rest.
• Durable underneath every step. Each node checkpoints, so a coordinated run is also a recoverable one.
• Approvals and retries in the graph. Insert human gates and per-node retry/backoff where they belong.
• One run history. The whole orchestration — every agent, tool and branch — is one replayable timeline.

What is AI agent orchestration?

Coordinating the steps of an AI workflow — model calls, tool calls, branches, parallel work and human approvals — so they run in the right order, handle failures, and share state. Orchestration is the layer above individual agents that decides what runs when and what happens on failure.

How is agent orchestration different from a job queue?

A job queue moves discrete tasks between workers. Orchestration adds workflow-level structure: ordered and parallel steps, branching, shared run state, retries, approvals and a durable history of the whole run. You can build orchestration on a queue, but the queue alone doesn't give you resumable, stateful workflows.

How do you orchestrate multiple agents reliably?

Model each agent and tool as a step in a durable workflow. The orchestration layer runs steps (sequentially or in parallel), checkpoints their outputs, retries failures, pauses for approvals, and keeps one shared, inspectable history — so a multi-agent run is coordinated and recoverable rather than a tangle of calls.