Agent Orchestration
Our orchestration layer coordinates model-driven and deterministic components when a workload requires more than one specialized responsibility.
Not the default
A multi-agent design is not the default. We use orchestration when decomposing a workload creates clearer permissions, narrower contexts, more reliable evaluation, or useful independent verification compared with one general-purpose execution path.
Orchestration responsibilities
The orchestration layer can manage:
- Task assignment
- Context boundaries
- Dependencies
- Tool access
- Intermediate state
- Verification
- Retries
- Escalation
A conceptual workflow might separate financial analysis, customer evidence, operating context, and independent review.
- Finance path
- Customer path
- Operations path
- Review path
The purpose is not to simulate an organization chart with AI agents. It is to create explicit computational boundaries where those boundaries improve the system.
State and dependency control
Orchestration becomes important when one step depends on the verified output of another. A downstream component should know whether an upstream task succeeded, failed, timed out, produced uncertain output, or completed only partially.
Without explicit state, multi-step systems can silently continue after an invalid intermediate result.
Tradeoff
Every additional agent creates another prompt, context, permission surface, evaluation target, runtime dependency, and failure mode.
More agents can therefore make a system worse.
Where one deterministic workflow plus one reasoning component is sufficient, that architecture is usually easier to test and operate. We use orchestration where decomposition produces a measurable advantage — not because multi-agent architecture is inherently more advanced.
Verification topology
Orchestration can also separate generation from review. A component that produces an analysis does not always need to be the component that verifies it. Independent review paths can reduce correlated failure where the consequence justifies additional inference and latency.
However, orchestration does not create independence automatically. Two agents using the same model, context, and assumptions may reproduce the same error. Where verification matters, the review path can differ in model family, context, prompt, tools, deterministic checks, or task framing.
The orchestration layer therefore manages not only who does the work, but also how evidence and responsibility move between execution stages without losing traceability.
Cost and latency
Decomposition also changes economics. Parallel execution can reduce wall-clock latency while increasing total inference. Sequential dependencies can do the opposite. Verification paths add cost deliberately in exchange for reliability.
We therefore evaluate orchestration at the workflow level, where quality, latency, cost, and failure recovery can be compared against a simpler architecture.