Six AI orchestration patterns and when to use each

Six patterns cover almost every multi-agent system you will build. Sequential, concurrent, group chat, handoff, magentic, and loop.

One rule matters more than any of them: start with the simplest pattern that solves the problem. Add complexity only when you hit a real capability gap.

Before you pick a pattern, check three things. How it handles crashes and retries. What it costs in tokens. How much latency it adds.

What is AI orchestration and why does the pattern matter?

Orchestration is the layer that decides which AI agent does what, when, and with what information.

Think of the kitchen pass in a busy restaurant. The head chef does not cook every dish. They read the order, send it to the right station, and check the plate before it goes out.

That is AI agent orchestration in plain terms. A coordinator, a set of specialist agents, some tools they can call, and a record of what has already happened.

Get the pattern wrong and you feel it fast. Fan a task out to five agents when two would do, and you burn tokens for no gain. Chain ten steps in a row and small errors stack up until the final answer is wrong more often than it is right.

The pattern you choose shapes three things directly:

• Token cost. More agents talking to each other means more tokens spent on coordination, not on the actual task.
• Latency. Sequential steps add up. Parallel steps race, but only as fast as the slowest one.
• Reliability. Every extra handoff is a place where things break, get lost, or contradict each other.

This guide covers each pattern, when to use it, when to leave it alone, and a decision process you can run in ten minutes. It also covers durable execution and the platforms that handle it. Written for people who build these systems.

The six core AI orchestration patterns explained

Architecture guidance from Microsoft's Azure Architecture Center sets out five of these patterns. The sixth, loop and evaluator, shows up constantly in production systems doing quality control.

Here is each one, straight up.

1. Sequential (prompt chaining)

One agent finishes a step and hands the output to the next agent, who builds on it. An assembly line.

Use it when: the task breaks into ordered steps. Draft, then edit, then fact check.

Avoid it when: the chain gets long. Errors compound. Vercel's guidance on agent orchestration patterns gives the maths. At 95% accuracy per step, a ten-step chain lands around 60% end-to-end. Every point you lose per step costs you more at the end than you expect.

Sketch: Agent A writes a draft. Its output becomes Agent B's input. Agent B's output becomes Agent C's input. No branching, no going back.

2. Concurrent (parallel, or fan-out fan-in)

Several agents work on separate parts of a task at once. Their results get merged.

Use it when: the subtasks are genuinely independent. Summarising five documents. Checking five data sources.

Avoid it when: the subtasks depend on each other, or you are not yet sure they are independent. Splitting too early is the most common orchestration mistake, and it is expensive in tokens and in debugging time.

Sketch: A coordinator splits the task into N pieces, dispatches them to N agents, waits for all N to finish, then merges the results.

3. Group chat

Several agents share one conversation thread. They see each other's messages and respond to each other, not just to the coordinator.

Use it when: you want something closer to peer review. One agent proposes, another critiques, a third checks facts.

Avoid it when: nobody is in charge of turn-taking. Group chat without a strict speaking order loops forever. Agents answer each other in circles and never reach a conclusion.

4. Handoff (coordinator)

A coordinator agent reads the incoming task and decides on the fly which specialist should handle it. Google Cloud's guidance on choosing an agentic design pattern calls this dynamic task routing. The right agent only becomes clear once the request is read.

Use it when: you have several narrow specialists (billing, technical support, sales) and requests arrive unpredictably.

Avoid it when: simple rules would route the request instead. If a keyword match or a form field tells you which specialist to use, you do not need an AI agent deciding that for you.

5. Magentic (manager)

A planner agent builds a task ledger, a running list of what needs doing. It assigns and re-assigns work as the picture gets clearer. Built for open-ended goals where you do not know the full task list at the start.

Use it when: the goal is genuinely open-ended. "Research this market and propose three strategies."

Avoid it when: the task is well defined. Magentic patterns are slow to converge, because the planner keeps revising its own plan. That flexibility costs time and tokens.

6. Loop (evaluator and optimiser)

A generator agent produces work. A critic agent scores it. The cycle repeats until the score clears a threshold.

Use it when: quality improves with iteration and you can measure the improvement. Code generation with test-based scoring is a strong fit.

Avoid it when: there is no reliable way to measure "better". Without a real metric, the loop spins and burns tokens.

Pro Tip: Write the scoring function before you build any loop or evaluator pattern. If you cannot describe how you will know the output improved, the pattern never converges cleanly. It just runs until you cut it off.

Why durable execution matters for orchestrated AI

Long-running agent workflows crash. Servers restart. APIs time out. Someone deploys a new version mid-run. Durable execution is what stops a crash meaning "start over from scratch".

It keeps three things. An event history, a full record of what has happened. The ability to resume from the last completed step. Automatic retries when a step fails.

Think of autosave in a video game, except the game also remembers exactly which enemies you had already beaten.

Three platforms handle this differently, and the shape of each is worth knowing:

• Temporal is a durable runtime built for exactly this. Workflows as code, automatic retries, long-lived state, observability included. Strong pick when you need human-in-the-loop steps inside a long process.
• Mistral Workflows runs hybrid. Your code runs in your environment. The orchestrator holds the event history and dispatches tasks to workers. Durable execution underneath is powered by Temporal, so you get the reliability without managing the runtime.
• Flyte and Prefect are Python-first. You write workflows as normal Python functions instead of learning a new language to describe a pipeline. Flyte handles infrastructure failures like out-of-memory errors and preemption, and recovers the workflow rather than losing it.

Build this yourself and it usually breaks at real scale. Retry logic, event history and crash recovery sound simple until you are the one debugging a half-finished workflow at 2am with no record of what happened.

Before you pick a runtime, run through this:

• Does it support hybrid hosting (your code, their orchestration) or does it need everything on their infrastructure?
• What security controls exist for data passed between agents and tools?
• Can you observe a live run step by step, or only after it finishes?
• Can you test it locally before deploying, without spinning up the whole cloud stack?

One honest caveat. Most orchestration we see inside founder-led businesses does not need a durable runtime on day one. A scheduled Claude Code routine with a checkpoint file will carry you a long way. Reach for Temporal when a single run spans hours, touches money, or waits on a human.

Where the patterns sit inside an AI Operating System

Patterns are the wiring. They are not the system.

The thing that makes orchestration pay for a consulting or coaching business is what the agents encode. Your judgment. The decisions you currently make yourself because nobody else can. We call the result an AI Operating System: a set of AI employees, each narrow, each trained on one job, coordinated by one of the patterns above.

Claude Code is the build tool we lead with. It sits on your files, your data and your documented process, which is where your IP actually lives. That matters more than the pattern you pick, because a perfectly wired handoff between two agents that know nothing about your business still produces generic work.

The sequence we use is Explore, Map, Transform. Find where the founder is the bottleneck. Map the decision the founder keeps making. Then encode it, starting with one agent, and only wire in a pattern once a second agent earns its place.

Njin's write-up on running Claude Code routines for a sales team shows what this looks like once it is scheduled and running unattended. For the build side, custom AI delivery systems with Claude Code covers how the pieces get assembled.

How do you choose the right orchestration pattern?

Run this checklist before you write a line of orchestration code.

1. Can the task be broken down at all? If one agent with a good prompt already solves it, stop there. Do not orchestrate for the sake of it.
2. What is your latency budget? Sequential and magentic patterns take longer. If you need an answer in two seconds, parallel patterns or a single agent are your only real options.
3. What is your token budget? Every extra agent talking to another agent adds tokens. Multi-agent systems can cost several times more than a single well-prompted agent doing the same job.
4. Can you measure quality? Loop and evaluator patterns only work if you can score the output. No metric, no loop.
5. Do you need a human in the loop? If yes, durable execution stops being optional. Someone might not answer that approval request for hours.

Three signals say it is time to move past a single agent. The context window is full and the task will not shrink. The subtasks are provably independent. You can measure a real quality gain from splitting the work.

Three signals say stay simple. You cannot test your routing logic reliably. You have no clear success metric. Your token budget will not carry the coordination overhead.

Pro Tip: Write your success metric down before you write your first agent prompt. If you cannot state it in one sentence, you are not ready to add a second agent.

For a deeper look at when the jump from single to multi-agent actually pays off, measuring before you build is worth reading before you commit engineering time.

Common orchestration pitfalls and how to avoid them

Most production failures trace back to one of four habits. All four are avoidable.

Accuracy compounding. Chain enough steps and small errors stack. At 95% accuracy per step, ten steps in a row give you roughly 60% end-to-end success, according to practitioner data from Vercel. Set a stop condition. Monitor per-step accuracy, not just the final output.

Premature fan-out. Splitting a task into parallel agents before you have confirmed the subtasks are independent inflates cost and failure surface. If merging the results is harder than doing the task in one pass, you split too early.

Watch for these warning signs:

• Your merge step needs its own agent to resolve conflicts between the parallel outputs.
• Token spend went up but output quality did not.
• You cannot explain in one sentence why each subtask needed its own agent.

Retry storms. A failed step retries. The retry fails too. Without backoff (a growing pause between attempts) or a hard quota, that failure multiplies across a whole fan-out and burns your API budget in minutes. Set retry limits and exponential backoff at the platform level, not as an afterthought.

Observability gaps. If you cannot see inside a live run, you are debugging blind. Event history and step-level tracing are how you find out which of your five parallel agents caused the bad output.

Implementation checklist for orchestrated AI systems

Before you ship, run through this:

1. Unit test the orchestration logic, separate from the model calls. Routing decisions and merge logic need coverage like any other code.
2. Integration test every tool call. Mock the failure cases too, not just the happy path.
3. Run chaos tests. Kill a worker mid-run. Does the workflow resume, or does it silently vanish?
4. Track four metrics continuously: per-step accuracy, token consumption per run, retry rate, and latency distribution. The tail matters more than the average.
5. Write workflows as plain code, not a bespoke configuration language. Easier to test, easier to review, easier for the next engineer to read.

Pro Tip: Keep a local replay tool in your dev workflow. Re-running a failed production workflow on your laptop, step by step, saves hours compared with reading logs and guessing.

For a working comparison of sequential versus parallel setups in real agent systems, this practical guide to agentic workflows walks through both side by side.

Integrating orchestration with external systems and APIs

Agents are only as useful as the systems they can touch. Most integration-layer failures come down to treating an external API as always available and always fast. It is neither.

Put timeouts on every external call, not just the ones that failed in testing. An API that is usually fast can still hang, and a hung call inside a sequential chain stalls the whole thing.

Idempotency matters more here than almost anywhere else. If a retry fires because your orchestrator lost track of whether the first call succeeded, and that call was "charge the customer" or "send the email", the downstream system needs to safely ignore the duplicate.

Rate limits deserve real attention. A concurrent pattern that fans out to twenty agents, each calling the same third-party API, trips a rate limit in seconds. Coordinate a shared budget across agents rather than letting each one call freely.

Tool calls need contracts. Define exactly what an agent can pass to a tool and what it gets back, in a schema, not in prose the model might read loosely on a bad day. A partner example worth studying: automating meeting notes for design teams shows how shared conversation threads and clear handoff points stop integration points turning into a mess of ad hoc calls.

Security and privacy in AI orchestration patterns

Every extra agent is another place where data leaks, gets logged somewhere it should not, or reaches a tool with more access than it needs.

Treat each agent like a new intern. Eager, quick, and completely ignorant of your business on day one. Give it access to the tools and data it needs for its job, nothing more. A summarising agent does not need write access to your customer database.

Log what agents send to each other and to external tools, with the same rigour you would apply to a human employee. When something goes wrong you need to trace which agent passed what data, and when.

Be deliberate about where sensitive data lives during a run. Durable execution platforms keep event history so workflows can resume. That history is a record of everything that happened, including any sensitive data that passed through. Know what is stored, where, and for how long.

Human-in-the-loop steps need their own access controls. An approval request that anyone with the link can click is not an approval step, it is a formality. Treat agentic workloads with the same discipline around state, access and failure recovery you would apply to a payments pipeline.

Monitoring and observability for orchestrated workflows

You cannot fix what you cannot see, and orchestrated systems hide problems well until they get expensive.

Track these as a baseline:

• Per-step accuracy, so you catch compounding before it reaches the final output.
• Token consumption per run, broken down by agent, so you know which step is expensive.
• Retry rate, which tells you early that a downstream system is flaky.
• Latency distribution, because the slowest 5% of runs usually explain your worst complaints.

Event history is not only for crash recovery. It is your debugging tool. Opening a live run and seeing which agent said what, in what order, turns a two-hour investigation into a five-minute one.

Set alerts on the boring stuff. A retry rate that has crept up 3x over a week is a far better early warning than waiting for the system to fall over.

My take on why orchestration goes wrong

I made this mistake myself, and it cost me about a month.

I was building an AI assistant that knew everything about me. My personal life, every project, all of it. One agent, total context. It did not work. Every time I cracked something it felt like one step forward and two steps back. I would fix one thing and it would forget another. The all-knowing assistant kept losing the plot.

What worked was the opposite. Pick one job. Build a narrow agent trained on that job alone. If you want a social media manager, build that and only that. It does not need to know about your other projects. Then let an orchestrator tie the narrow agents together.

Scope is the skill, not raw intelligence. That is really what these six patterns are for. They are ways of tying narrow agents together without letting any one of them try to be the whole business.

The founders we work with rarely have a technology problem. They have a bottleneck problem, and it looks the same every time. Every important decision routes through them, because they are the only one who has internalised how the business actually works.

Here is the honest bit. Orchestration is not right for every business. If your process changes weekly, or you cannot describe your own decision-making in clear steps yet, building agents around it just encodes the confusion faster than a human would.

James Killick

Want a working prototype instead of another consulting deck?

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If you want the vocabulary first, start with the AI Orchestration Glossary, or read our original research on orchestration outcomes to see what a working system looks like once it is built.

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Sources

• AI agent orchestration patterns - Azure Architecture Center
• Six Agent Orchestration Patterns - Vercel
• AI Applications & Agents With Temporal | Temporal
• Overview | Mistral Docs
• Flyte