Designing decision-heavy enterprise AI systems without losing control: tri-agent architecture (Planner–Orchestrator–Executor)
Enterprise AI workflows involving complex, multi-step decisions cannot be handled by single-agent architectures, which failed to enforce execution order, track state, or prevent repeated and skipped steps under real-world conditions.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User request triggers workflow
A natural-language user request initiates the multi-step enterprise workflow.
Tools used
LLMtask queue
Outcome
The tri-agent architecture (Planner–Orchestrator–Executor with a task queue backbone) delivered deterministic, ordered execution of decision-heavy enterprise workflows; pruning the Executor's toolset cut execution latency roughly in half.
What failed first
Three successive designs failed: (1) a monolithic agent executed steps out of order and lacked state tracking; (2) upgrading to a more capable model increased latency and made the agent overly cautious; (3) delegating planning to a separate component still allowed parallel execution that violated sequential dependencies.