Agent orchestrators coordinate multiple AI agents to work together on complex tasks. They manage which agents handle which subtasks, pass context between agents, and combine their outputs into cohesive results. For businesses, this enables autonomous systems that tackle multi-step workflows without constant human intervention. Without orchestration, agents work in isolation and produce fragmented, inconsistent outputs.
Your AI handles simple requests fine, but complex tasks fall apart
Every multi-step process needs you to manually connect the pieces
Agents work in isolation, producing fragmented, inconsistent results
What if your AI systems could coordinate themselves like a well-run team?
Part of the Orchestration & Control Layer
The Coordination Layer for Autonomous AI
Agent orchestrators manage multiple AI agents working together on complex tasks. Unlike workflow orchestration that coordinates predefined steps, agent orchestration coordinates autonomous decision-makers whose actions are not fully predictable.
The orchestrator decides which agent handles each subtask, passes context between agents, handles failures when agents produce unexpected results, and combines outputs into coherent final results. It acts as the project manager for your AI workforce.
This matters because real business problems rarely fit neatly into what one agent can handle. Research requires searching, analyzing, and synthesizing. Customer support requires understanding, investigating, and responding. Agent orchestration lets you build AI systems that match the actual complexity of your work.
Get orchestration wrong and your agents work against each other, duplicating effort and producing inconsistent outputs. Get it right and you have AI systems that tackle complex work autonomously, combining specialized capabilities into coherent results.
Complex work requires coordinated specialists, not overloaded generalists
Task arrives that exceeds single-agent capability. Orchestrator decomposes the task, assigns subtasks to specialists, and coordinates execution. The result is a coherent output synthesized from multiple agent contributions.
When researching a topic requires finding sources, evaluating credibility, extracting key points, and synthesizing a summary...
That's agent orchestration - a search agent finds sources, an analysis agent evaluates them, and a synthesis agent produces the final output.
Research time: 4 hours manual work to 15 minutes coordinated
When handling a customer issue requires understanding the problem, checking account history, investigating systems, and drafting a response...
That's agent orchestration - specialized agents handle each phase while the orchestrator maintains context across the full interaction.
Resolution completeness: 60% first-contact to 85% with multi-agent support
When processing an expense report requires extracting receipt data, validating against policies, checking budgets, and routing for approval...
That's agent orchestration - each validation step runs independently but the orchestrator ensures all checks pass before proceeding.
Processing time: 3 days manual review to same-day automated processing
When generating a business report requires pulling data from multiple sources, running different analyses, and combining into a coherent narrative...
That's agent orchestration - data agents gather information, analysis agents interpret it, and a writing agent produces the final report.
Report generation: weekly manual effort to daily automated delivery
Where do your complex tasks currently require you to manually coordinate between different steps or capabilities?
Toggle orchestration on or off, then run the same research task. See how agent outputs change when they work together versus in isolation.
Gather data on 3 competitors, analyze trends, write summary report
Pipeline processing
Agents execute in a predefined order, each processing the output from the previous agent. Simple to reason about and debug. Best for tasks with clear dependencies between steps.
Concurrent execution
Independent agents run simultaneously, with results merged at the end. Maximizes speed when subtasks do not depend on each other. Requires careful result synthesis.
Supervisor and workers
A supervisor agent breaks down tasks and delegates to worker agents. The supervisor reviews outputs and may reassign work. Best for complex tasks requiring judgment about delegation.
Answer a few questions to find the right orchestration approach.
Do your subtasks depend on each other?
Agent orchestrators coordinate specialized agents to tackle multi-step tasks. This flow shows a research request moving through search, analysis, and synthesis agents, with the orchestrator managing handoffs and combining outputs.
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This component works the same way across every business. Explore how it applies to different situations.
Notice how the core pattern remains consistent while the specific details change
Adding orchestration complexity for tasks a single well-prompted agent handles fine. Multi-agent systems have coordination overhead.
Instead: Start simple, add agents only when you hit clear capability limits.
Giving every agent the full conversation history. Context overload degrades agent performance and increases costs.
Instead: Route only the context each agent needs for its specific subtask.
Assuming agents will always agree. When two agents produce conflicting outputs, the orchestrator needs a resolution strategy.
Instead: Define hierarchies, confidence thresholds, or escalation paths before conflicts occur.
Agent orchestration is the coordination layer that manages multiple AI agents working together. It decides which agent handles each subtask, routes information between agents, handles failures when agents produce unexpected results, and combines outputs into a coherent final result. Think of it as the project manager for your AI workforce, ensuring agents collaborate rather than compete or duplicate effort.
Use multi-agent systems when tasks require different specialized capabilities, when workload exceeds what one agent can handle, or when you need redundancy and fault tolerance. A research task might need one agent for web search, another for document analysis, and a third for synthesis. If a single prompt can solve your problem, multi-agent adds unnecessary complexity.
Workflow orchestration coordinates predefined steps with deterministic paths. Agent orchestration coordinates autonomous decision-makers whose actions are not fully predictable. Workflows execute recipes; agent systems supervise collaborators who may take unexpected approaches. Agent orchestration requires handling emergent behavior, managing agent disagreements, and synthesizing potentially conflicting outputs.
Sequential orchestration chains agents in a pipeline where each processes the previous output. Parallel orchestration runs agents simultaneously for speed. Hierarchical orchestration has supervisor agents managing worker teams. Group chat orchestration enables agents to collaborate through discussion. The right pattern depends on task dependencies, latency requirements, and whether agents need to negotiate.
Establish clear agent roles with explicit responsibilities and boundaries. Use standardized APIs and message formats for communication. Implement arbitration logic for when agents disagree. Design handoff protocols that preserve context during transitions. Monitor agent interactions and log decisions for debugging. The orchestrator should resolve conflicts based on confidence scores, domain expertise, or predefined hierarchies.
Good orchestration includes fallback strategies for agent failures. Options include retrying with adjusted parameters, routing to alternative agents with similar capabilities, degrading gracefully by skipping non-essential steps, or escalating to human review. The orchestrator should detect failures quickly, prevent cascade effects, and maintain system-wide coherence even when individual agents underperform.
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Choose the path that matches your current situation
You have single agents but need them to work together. Start with sequential orchestration for a two-agent pipeline. Add state passing between agents before adding more complexity.
Your first action
Build your first agent handoffYou have agents coordinating but hit reliability issues. Add failure handling with retry logic and fallback agents. Implement monitoring to track where coordination breaks down.
Your first action
Improve reliabilityYou have reliable orchestration and need more sophisticated coordination. Explore hierarchical patterns with supervisor agents. Add dynamic routing based on task characteristics.
Your first action
Scale your agent systemsAgent orchestration works alongside other orchestration patterns. Explore how to structure individual agents and coordinate larger systems.