AI agent orchestration explained

Once you have more than one AI agent, the question becomes: how do they work together without turning into chaos? That's orchestration.

What orchestration is (and isn't)

It is the coordination layer: one agent delegating focused sub-tasks to others, results coming back, the whole effort tracked as one durable unit. It isn't a workflow engine running a fixed plan — the coordination is emergent, decided at runtime toward a goal. (agent vs workflow)

The core primitives

• Delegation — an agent hands a sub-task to another agent (or a human) and continues or waits for the result.
• Durable jobs — work that survives across many steps, agents, and even days, with full history — so a multi-agent effort isn't lost if it spans a week.
• Hand-offs — passing context along, so the next agent isn't starting cold.
• Shared state — the facts everyone needs live in one source of truth, not in each agent's head.

The traps (and fixes)

• Lost context — a delegated agent answers wrong because it doesn't know what a sibling found. Thread the facts into the hand-off. (multi-agent systems)
• Racing on shared data — fix with deterministic operations.
• Runaway delegation — a leaf worker spawning more workers. Cap delegation depth and scope what each agent may start.

Why "emergent" beats "predefined"

Real processes have branches you didn't anticipate. A predefined plan breaks on the first one; orchestration lets the owning agent decide — delegate, ask a human, or handle it — while the platform keeps the durable record. You get flexibility without losing traceability.

The mental model

Think of it as a job tree: a root objective, agents and humans as participants, work fanning out and results flowing back, all on one record. That's how a sales agent pulls in a solutions agent mid-deal, or a manager coordinates several people at once — without you wiring the steps.

See the enterprise AI agents guide, or book a demo to watch a multi-agent workflow run.