A single AI agent can handle a short task well enough, but hand it a large one — research, write code, test, fix bugs, deploy — and it starts to unravel. It loses track of earlier steps, mixes up context, and makes mistakes that compound. Multi-agent orchestration is the layer that solves this: instead of one agent doing everything, it coordinates several specialised agents so each one does what it is good at.
Why one agent is not enough
Large language models work within a finite context window. The more jobs you pile into a single conversation, the more likely the model is to forget an earlier instruction, contradict itself or produce lower-quality output. A coding agent asked to also research, test and review its own work is spreading its attention across tasks that pull in different directions.
Orchestration splits the work across agents that each hold a smaller, focused context. A research agent reads documentation. A coding agent writes the implementation. A testing agent runs and evaluates the results. None of them needs to remember what the others are doing in full, because the orchestration layer manages what each one sees.
How orchestration works
The orchestration layer sits between the task and the agents. It decides:
- Who does what — routing each sub-task to the agent best suited for it.
- When — running steps in sequence when one depends on another, or in parallel when they don't.
- What context to share — passing just enough output from one agent to the next, rather than dumping the entire conversation.
- What happens when something fails — retrying, falling back to another agent, or escalating.
In the simplest form, orchestration is a pipeline: agent A finishes, its output feeds agent B, and so on. In practice, most systems need branches, loops and parallel paths.
Common patterns
Sequential pipeline
Each agent runs in order. The output of one becomes the input of the next. This is straightforward but slow, because every step waits for the one before it.
# Pseudocode: a sequential pipeline
research = research_agent.run(task)
code = coding_agent.run(research)
review = review_agent.run(code)Parallel fan-out
Independent sub-tasks run at the same time. A summarisation job, for example, might split a long document into sections and send each to a separate agent, then merge the results.
Orchestrator (hub and spoke)
A central controller agent receives the task, breaks it into sub-tasks, assigns each to a specialist, collects the results and decides the next step. This is the most flexible pattern: the orchestrator can re-route work, ask for a second opinion, or run a step again if the output is poor.
Voting and verification
Multiple agents solve the same problem independently, and the system compares their answers. If two out of three agree, the answer is likely correct. This is slower and more expensive, but it catches mistakes that a single agent would miss, making it valuable where accuracy matters more than speed — code generation, medical reasoning, or safety-critical decisions.
A worked example
Imagine a code-review pipeline with three agents:
- Reviewer reads a pull request and lists potential issues.
- Verifier takes each issue and checks whether it is real by reading the surrounding code.
- Reporter writes a summary with only the confirmed issues.
The orchestrator sends the pull request to the reviewer first. Once the reviewer finishes, the orchestrator fans out: it sends each flagged issue to the verifier in parallel. When every verification comes back, the orchestrator passes the confirmed list to the reporter. If the verifier flags something uncertain, the orchestrator can route it back to the reviewer with more context.
This is something a single agent could attempt, but it would review and verify in the same breath, often convincing itself that a false positive is real.
Context sharing
One of the hardest parts of orchestration is deciding what to pass between agents. Too little and the next agent lacks the information it needs. Too much and it drowns in irrelevant detail, which is exactly the problem orchestration was meant to solve.
Most frameworks handle this with a shared state or memory that the orchestrator controls. Each agent reads from and writes to that state, and the orchestrator decides which parts are visible to each agent at each step. Some systems use a structured schema (a JSON object that grows as agents contribute); others pass plain text summaries.
Common mistakes
- Over-splitting. Not every task needs multiple agents. If the job fits comfortably in a single context window, orchestration adds latency, cost and failure points for no gain.
- Chatty agents. Passing the entire conversation history between agents instead of a focused summary defeats the purpose. Each handoff should carry only what the next agent needs.
- No error handling. A single failing agent can stall the whole pipeline. Good orchestration retries transient failures, falls back to alternative agents and has a timeout on every step.
- Ignoring cost. Every agent call is an LLM call. A fan-out to five agents on a large input can cost five times what one agent call would. Design the pipeline so parallel steps use the cheapest model that can handle the sub-task.
Key takeaways
- Multi-agent orchestration coordinates specialised agents instead of overloading one.
- The orchestration layer handles task routing, context sharing, sequencing and error recovery.
- Common patterns include sequential pipelines, parallel fan-out, a central orchestrator, and voting for verification.
- Pass only the context each agent needs; too much is as harmful as too little.
- Not every task benefits from multiple agents — use orchestration when a single agent's context or capability is genuinely the bottleneck.
