The single-agent pattern (one model, one loop, one task) is great for narrow workflows and gets you past the demo. The moment you try to do something a real team would do, you find yourself wanting specialists. A researcher who's good at the web. A writer who's good at structure. A reviewer who's good at catching what the writer missed.
That instinct is right, and it's why multi-agent systems are the dominant 2026 architecture for non-trivial agent work. The cost is that orchestration is now your problem.
There are five orchestration patterns that have crystallized in the last twelve months. Most production systems use a combination of them. Knowing which is which lets you read someone else's architecture diagram in thirty seconds.
1. Sequential
Agents take turns. Each one reads the previous one's output, adds its layer, and hands off. Document drafting, then editing, then fact-check, then polish is the canonical example.
When it fits: the task has a clear pipeline, each stage has a different specialty, and order matters.
Quiet failure mode: error compounding. The drafter's small misunderstanding becomes the editor's structural confusion becomes the polisher's confidently-wrong final paragraph. Mitigation is making each stage citable: every claim traceable back to a source the next stage can check.
2. Concurrent
Agents run in parallel and a merge step combines their output. Three researchers each take a different angle on a topic; an aggregator dedupes and synthesizes.
When it fits: the task is genuinely parallel and you can afford to spend N times the tokens for a faster wall-clock and broader coverage.
Quiet failure mode: the merge step is hard. If your aggregator is too dumb, the output is just three reports stapled together. If your aggregator is the smart one, you have a sequential pattern with extra steps. Most teams under-invest in this layer.
3. Group chat / maker-checker
Two or more agents debate or review each other's output before committing. The simplest version is a maker who proposes and a checker who validates against rules. Most useful when the rules are too nuanced for code but too important for one model to be trusted with alone.
When it fits: correctness matters and you have explicit criteria that a second pass should enforce. Compliance review, code review, contract clauses.
Quiet failure mode: agreement collapse. Both agents drift toward consensus on a wrong answer because they share the same priors. Mitigation is making the checker structurally different: different model, different prompt frame, different tool access, so the disagreement is real.
4. Dynamic handoff
A router agent triages incoming work and assigns it to a specialist. Tier-1 support is the obvious case: classify the ticket, route to the right specialist, escalate to human if confidence is low.
When it fits: the input space is broad, the work splits cleanly into categories, and you have specialists for each category.
Quiet failure mode: the router becomes a single point of failure. A misclassified ticket goes to the wrong specialist who confidently mishandles it. Logging the router's decision and confidence is non-negotiable; reviewing its mistakes weekly is the part most teams skip.
5. Magentic
A manager agent decomposes a goal into subtasks, assigns them to subordinate agents, monitors progress, and revises the plan as evidence comes in. This is the closest to "give it a goal and walk away," and the hardest to operate.
The Azure Agent Factory team calls this magentic orchestration. It's also what Anthropic's Cowork-style long-horizon work looks like under the hood.
When it fits: the work is open-ended enough that a fixed pipeline doesn't capture it, and the budget allows for re-planning.
Quiet failure mode: plan drift. The manager re-plans into something nobody asked for. The classic mitigation is forcing the manager to keep its current plan visible to the user as it evolves. An interruptable, legible plan beats an autonomous one almost every time at this stage of the technology.
How real systems combine them
Read enough production agent architectures and the same shape emerges:
A router out front (dynamic handoff). A manager inside the chosen lane (magentic). Specialists running mostly sequentially but with concurrent research stages where they make sense. A maker-checker pair on anything that ships externally.
That's the whole pattern. If you find yourself drawing a diagram with twelve agents in a fully-connected graph, you're probably overcomplicating it.
The orchestration tax
Every layer of orchestration costs latency, tokens, and surface area for failure. The instinct to give every problem its own agent is wrong. A useful rule from the Vellum guide:
Add an agent only when you can name the specific failure it's supposed to prevent that the previous architecture couldn't.
If the answer is "I dunno, more agents seemed better," you're paying for a feature you're not actually using.
In your M365 environment
Multi-agent systems land in an M365 tenant in two main ways:
- Inside Copilot Studio as multi-agent topics or flows, with the orchestrator native and the specialists usually backed by your own data sources or Power Platform connectors. The Studio surface is intentionally a constrained version of the patterns above. It nudges you toward sequential and dynamic-handoff, which is the right starting place for most teams.
- Through Cowork's plan view, which exposes a magentic-shaped plan to the user as it executes. The user can intervene mid-plan. Treat this as a UX feature and a governance feature: it's the moment a human can catch an agent before it does something irreversible.
The honest version of multi-agent design in 2026 is: as few agents as you can get away with, each with a job description you could write on an index card, all of them logged. Anything beyond that, you're going to be debugging on a Friday night.
Sources: Azure Agent Factory: patterns and use cases · Vellum: Agentic workflows guide 2026 · Multi-agent frameworks for enterprise AI