Agent Orchestration Patterns: How Enterprise AI Agents Work Together
Enterprise agentic AI rarely means a single agent working alone. The three agent orchestration patterns — Delegate/Handover, Orchestrator-Worker, and Intelligent Intake and Routing — define how multiple agents collaborate to handle complex, multi-domain tasks.
What Is Agent Orchestration?
Agent orchestration is the coordination of multiple AI agents to handle tasks that exceed the scope, domain, or capability of any single agent. In enterprise environments, orchestration enables specialist agents — each scoped to a domain like HR, IT, or Sales — to work together without requiring users to manage which agent handles what.
The right orchestration pattern depends on whether the user needs continuity across domains, whether tasks can be decomposed and run in parallel, and whether routing logic is simple enough to be deterministic or requires LLM-based intent classification.
The 3 Agent Orchestration Patterns
| Pattern | What It Does | Best When | Key Trade-off |
|---|---|---|---|
| Delegate / Handover | Transfers the conversation to a specialist agent when a request falls outside the current agent's domain | Domain separation is clean; users can tolerate switching apps | User loses conversation continuity on handover |
| Orchestrator-Worker | A coordinator agent decomposes a task and delegates sub-tasks to specialist agents, then aggregates results | Tasks can be decomposed; sub-tasks don't require user-context-sensitive data | Worker agents currently can't access user context, runtime connections, or permission-aware KBs |
| Intelligent Intake & Routing | A front-door agent (or Slack bot) classifies user intent and routes the request to the correct specialist agent or workflow | Single entry point; many downstream agents; routing accuracy is critical | Routing quality depends entirely on Genie description quality |
Choose Your Orchestration Pattern
Delegate and Handover
Orchestrator-Worker
Intelligent Intake and Routing
Orchestration vs. Single-Agent: When Does Complexity Pay Off?
Adding orchestration introduces coordination overhead, additional failure points, and increased latency. It's only justified when a single agent genuinely can't meet the need — typically because the domain is too broad, the task requires genuinely parallel execution, or the organization requires strict separation of agent permissions.
Before adding orchestration, validate that the single-agent alternative has actually been tried and found insufficient. Most enterprise AI failures come from premature orchestration — adding multi-agent complexity before single-agent scoping has been optimized.
How Workato Implements Agent Orchestration
Workato supports all three orchestration patterns natively:
- Genie Handover — available via the FDE Agentic Common Utilities module. Maintains a curated list of Genies with descriptions; the LLM selects and initiates the handover.
- Genie Collaboration (Assign a Task to Genie) — enables orchestrator Genies to delegate sub-tasks to worker Genies and receive results, all within the same conversation session.
- Slack Single App Experience — a unified Slack bot with a main Genie that routes to specialist Genies based on intent, with support for channel-specific Genies and custom home pages.
- Federated MCP Server — an MCP server that acts as orchestrator, directing MCP clients to the appropriate downstream MCP server for a given request.