Multi-agent orchestration
Also called: agent team orchestration, multi-agent workflow engine
Coordinating several specialized AI agents through a defined workflow, with explicit handoffs, state, and control over the sequence.
What multi-agent orchestration means.
Multi-agent orchestration is the coordination of multiple specialized agents toward one outcome, with the sequence, the handoffs between them, and the shared state managed explicitly rather than emerging from conversation. Each agent holds a narrow role, and the orchestration layer decides which runs when, what context it receives, what it may do, and how partial results combine. The alternative, letting agents negotiate among themselves, produces runs that are hard to reproduce and harder to audit, because no artifact describes what was supposed to happen.
Why it matters.
One model in a loop degrades as a task gets longer: context fills, focus drifts, and there is no boundary at which to place a control. Splitting work across bounded roles keeps each step reviewable and creates natural points for policy, approval, and logging. It also means a failure is localized to a step rather than diffused across one long trace.
How it works in practice.
Declared, not improvised
A mission is defined ahead of the run, so the same inputs produce a comparable run and a reviewer can see the intended path.
Bounded roles
Each agent has a defined job and a defined permission set, which is what allows narrow authorization instead of one broadly privileged agent.
Explicit handoffs
What passes between agents is specified, so context does not leak and a step cannot silently inherit permissions from another.
Governed at each step
Because steps are discrete, policy evaluation, approval gates, and audit entries attach to specific transitions rather than to an opaque whole.
What people get wrong.
That more agents is better. Splitting a task that one agent handles well adds handoff overhead and failure modes. The useful decomposition follows real role boundaries, the same ones a team of people would divide along.
Common questions.
How many agents does a workflow need?
As many as there are genuinely distinct roles, and no more. Decomposition earns its cost when roles need different permissions, different models, or different review, not because more agents sounds more sophisticated.
What is the difference from a single agent with many tools?
A single agent with many tools holds the union of every permission its tools require, for the whole run. Bounded roles let each step carry only what that step needs, which is what makes least privilege achievable.
How do you debug a multi-agent run?
Through the recorded trace. Because handoffs are explicit and every step is logged with its reasoning and policy decision, a run can be replayed to the step where it diverged rather than reconstructed from output.
Standards referenced.
Trunnion is engineered to align with these control families. Alignment by design is not a claim of current certification or authorization.
Read next.
- AI control planeThe shared layer that composes, authorizes, routes, and records agent work, separate from the applications built on top of it.
- Agent governanceThe control layer that decides what an AI agent may do, who approves consequential actions, and how every step is proven afterward.
- Tool authorizationChecking an agent's permission to invoke a specific capability, separately from its permission to read the underlying data.
Get started
See multi-agent orchestration running on your workflow.
Governance, approval, and provable audit built into the control plane, from cloud to air-gap. Request a demo against a workflow you actually run.