AI agent orchestration built for production.
Orchestration is the layer that turns a pile of agents into a working team. Task decomposition, hierarchy, parallel execution, retries, and observability — without you writing the graph.
Six things every orchestration layer has to handle.
Task decomposition
Break a goal into a dependency graph of subtasks the team can execute in the right order.
Agent hierarchy
A coordinator owns the outcome. Specialists own steps. Reviewers own gates. The hierarchy enforces accountability.
Parallel execution
Independent steps run concurrently. The orchestrator joins their outputs before downstream work begins.
Retry and recovery
Tool failures, malformed outputs, and timeouts trigger retries with backoff and rerouting — never silent stalls.
Memory and state
Shared context flows between agents. Per-agent scratch memory stays isolated for focus.
Observability
Every step logged with input, output, latency, and tool calls. You can debug a workflow the way you debug a system.
The orchestration layer we ship to production.
Coordinators
Stateful agents that hold the goal, plan the work, and route to specialists. They handle escalation when something falls outside scope.
Workers
Single-role specialists. Each one has narrow tools and tight prompts so its output is reliable.
Reviewers
Validation agents that check worker output against schemas, brand guardrails, or business rules before it ships.
Production agent work is mostly orchestration.
The model is 20% of an agent platform. The rest — orchestration, retries, memory, observability, integrations — is what determines whether a deployment survives contact with real workflows. Use the AI agent builder to define roles, then let the orchestration layer manage execution across multi-agent systems. See the full platform or learn about autonomous AI agents to understand what the orchestrator is running.
Watch an orchestrated team finish a real task.
We'll demo a full multi-agent run with the orchestrator's traces visible. Bring a workflow.