Autonomy
Agent orchestration
Also called: multi-agent system, workflow orchestration
Definition
Agent orchestration is the layer that decides which model, tool, or human handles each step of a task, and what happens when a step fails.
Most production value comes from orchestration rather than from any single model. Routing simple work to a cheap model, hard work to an expensive one, and ambiguous work to a person is what makes unit economics and quality hold at the same time, rather than trading one for the other. It is also where retries, timeouts, and audit trails live, which is why orchestration design deserves as much scrutiny as model selection during a build.
Routing is the margin
A workflow that sends every case to the strongest model overpays for the eighty percent that were easy, since a case that a cheap model would have resolved correctly gains nothing from being handled by a more expensive one. Tiered routing with a confidence threshold usually cuts cost substantially with no measurable quality loss, because the expensive model is reserved for the genuinely hard cases where its extra capability actually changes the outcome, rather than being applied uniformly regardless of case difficulty.
Failure paths are the design
What happens on a timeout, a refusal, or a low-confidence answer determines whether the system is trustworthy, arguably more than what happens on the happy path where everything works as expected. Silent failure into a default answer is the worst option and the most common one, because it looks like the system is working from the outside while quietly producing wrong or low-quality outputs that nobody is positioned to catch until the downstream damage surfaces.
Multi-agent systems add a coordination layer to govern
When multiple agents hand work between each other, the orchestration layer also has to manage what each agent is allowed to pass to the next, and what happens if one agent's output is malformed or wrong by the time it reaches another. This coordination layer is a new source of failure that a single-agent workflow does not have, and it needs its own logging and testing discipline, separate from testing each individual agent's behavior in isolation, because bugs at the handoff are common and easy to miss.
In practice
- Define an explicit escalation path for every automated step
- Route by difficulty and stakes, not by whichever model is newest
- Persist the full trace of each run for audit and debugging
- Test the handoffs between agents specifically, not just each agent alone
