Ask a research assistant to “put together a market analysis on lithium supply chains” and what needs to happen is four things in a specific order: pulling current sourcing and price data, extracting the relevant figures from raw sources, analysing risk exposure, and writing the final output. Each step might go to a different agent. Orchestration is what connects those steps.
Coordination is a separate problem from capability
An AI agent can perceive inputs, call tools, and produce outputs. What it cannot do on its own is coordinate with other agents reliably across a multi-step goal. Orchestration is the layer that handles that coordination, independent of the domain work any individual agent performs.
That layer takes on four responsibilities:
- Routing: deciding which agent handles which subtask, and when
- Sequencing: determining the order agents run and what dependencies exist between them
- State: holding a shared context so each agent receives the relevant outputs from prior steps
- Handoffs: translating outputs from one agent into the format the next expects
The orchestrator reads the current task state and decides which agent is next and what context to pass it. It does not do the research, the writing, or the analysis.
Three patterns span most multi-agent systems
A 2026 benchmark comparing orchestration architectures across 10,000 document-processing tasks identified four patterns in use (arXiv:2603.22651, March 2026). Three cover the core design space for routing work between distinct agents:
Sequential pipeline. Agents run in a fixed order, each receiving the output of the previous one. Total runtime is the sum of all steps. The cheapest pattern to implement and the most predictable to debug.
Parallel fan-out. Independent subtasks run at the same time and merge into a single output. Total runtime collapses to the slowest agent rather than the sum of all steps. It is most useful when subtasks share no dependencies.
Hierarchical manager-worker. A manager agent decomposes the goal into subtasks and delegates each to worker agents, then synthesises the results. The benchmark found this pattern achieves 0.921 F1 at 1.4x baseline cost, making it the most cost-efficient option for complex, multi-part tasks.
The fourth pattern the benchmark identified, a reflexive self-correcting loop in which an agent checks and revises its own outputs, reaches the highest accuracy (0.943 F1) but at 2.3 times the cost of a sequential baseline. Most production systems combine the first three.
Where most failures actually occur
State management and handoffs are where orchestrated systems break most often, though they draw less attention than the agents themselves.
State is the accumulating context that agents write to and read from across a run. Passing all prior state to every subsequent agent is tempting but counterproductive. Selective state passing, filtering what each agent actually needs, is an orchestration design decision, not a default.
Handoffs are the transfer points between agents. An output from one agent rarely arrives in the format the next agent expects. The orchestrator reformats or filters at each boundary.
The orchestrator itself can be the weakest link
Static orchestration uses a hard-coded graph: agent A runs first, then agent B. This works for predictable, well-understood tasks.
For goals where the right path depends on what earlier agents found, a dynamic orchestrator is more useful: an LLM that reads the current state and decides at runtime which agent to call next. In that design, the orchestrator is not fixed infrastructure but a reasoning agent with its own role and its own context window.
That is the part most introductions to agentic AI understate. In a hierarchical system, the manager agent’s routing decisions are as consequential as the worker agents’ domain outputs. A correct result from the wrong agent, at the wrong stage, still produces the wrong final answer. The reliability of the whole system depends on the orchestration layer, not only on the agents it manages.
Sources
- Benchmarking Multi-Agent LLM Architectures for Financial Document Processing. arXiv:2603.22651, March 2026.
Questions, answered
What does an orchestrator agent do?
An orchestrator receives a goal, breaks it into subtasks, assigns each to a suitable agent, tracks progress, and assembles the final output. It manages context flow between agents so each step receives what it needs from the steps before it.
What are the main agent orchestration patterns?
Research benchmarks identify four patterns in production use: sequential pipeline (agents run in order, each receiving the prior output), parallel fan-out (independent tasks run at the same time and merge), hierarchical manager-worker (a manager delegates subtasks and synthesises results), and reflexive self-correcting loop (an agent checks and revises its own outputs). Most systems combine the first three.
What is the difference between agent orchestration and a static workflow?
A static workflow follows a fixed, pre-written sequence of steps. Orchestration can be dynamic: an LLM-based orchestrator reads the current task state and decides at runtime which agent to invoke next, adapting the path as early results come in.
Can one LLM act as the orchestrator?
Yes. In hierarchical designs, a manager LLM receives a goal, plans subtasks, delegates each to a worker agent, and synthesises the results. The manager is itself a language model making routing decisions, not a hard-coded controller.
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