AI Agent Orchestration Patterns

Start with the complexity question

The guide opens with advice many teams skip: use the lowest level of complexity that reliably works. It describes three levels:

• A direct model call with a good prompt, for single-step tasks such as classification or summarisation.
• A single agent with tools, which it calls often the right default for enterprise use cases, since it is simpler to test and debug.
• Multi-agent orchestration, justified only when one agent cannot reliably handle the task because of prompt complexity, too many tools, or the need for separate security boundaries.

The five patterns

• Sequential (also called pipeline or prompt chaining): agents run in a fixed order, each refining the previous output. Good for step-by-step processing with clear dependencies.
• Concurrent (fan-out/fan-in, scatter-gather): several agents work on the same task in parallel from different perspectives, and results are combined. Good for diverse analysis and speed; watch resource limits and shared state.
• Group chat (roundtable, debate): agents discuss in a shared thread managed by a chat manager, useful for brainstorming and validation, and well suited to a human taking part. The maker-checker loop is a special case in which one agent produces and another checks against clear criteria, with an iteration cap.
• Handoff (routing, triage): each agent decides whether to handle a task or pass it to a more suitable one, as in customer support, where the right specialist is only clear during the conversation.
• Magentic (dynamic or adaptive planning): a manager agent builds and updates a task ledger for open-ended problems, delegating to agents that can act on external systems.

Each pattern has an example scenario and explicit when-to-use and when-to-avoid lists.

Implementation advice worth reading twice

The second half covers context and state management, reliability, security, cost, observability and testing, and human participation. The list of common pitfalls is especially useful:

• Using a complex pattern when simple sequential or concurrent orchestration would do.
• Adding agents that do not bring real specialisation.
• Ignoring the latency of multiple hops.
• Sharing mutable state between concurrent agents.
• Using deterministic patterns for non-deterministic work, and the reverse.
• Letting context windows grow unchecked as agents accumulate information.

It also notes that real systems often combine patterns, for example sequential stages for data preparation followed by concurrent analysis.

How to use it

It is a long article (roughly 7,000 words), best used as a reference rather than read in one go. Read the complexity section and the pattern summaries first, then the implementation considerations for the pattern you pick. The final section maps patterns to Microsoft Agent Framework and Foundry, but the ideas are framework-neutral and work with LangGraph, the OpenAI Agents SDK or your own code.

It pairs well with LangChain's short essay on how and when to build multi-agent systems, which explains why parallel reading tasks suit multiple agents while writing tasks usually do not.

Common questions

Is this beginner material? It assumes you have built at least a simple agent. Start with an agents course if you have not.

Does it require Azure? No. Only the implementation section is Azure-specific.

Best for: engineers and architects choosing how to structure an agent system who want trade-offs stated plainly.