{"id":1519,"date":"2026-05-18T11:30:26","date_gmt":"2026-05-18T11:30:26","guid":{"rendered":"https:\/\/bogdanburuiana.com\/?p=1519"},"modified":"2026-05-18T11:30:27","modified_gmt":"2026-05-18T11:30:27","slug":"five-ways-to-orchestrate-multiple-ai-agents-and-when-to-use-each","status":"publish","type":"post","link":"https:\/\/bogdanburuiana.com\/index.php\/2026\/05\/18\/five-ways-to-orchestrate-multiple-ai-agents-and-when-to-use-each\/","title":{"rendered":"Five Ways to Orchestrate Multiple AI Agents (and when to use each)"},"content":{"rendered":"\n

Single agents are useful. Multi-agent systems are powerful. But “multiple agents working together” isn’t one thing – it’s five distinct architectural patterns, each suited to different problems. Using the wrong pattern is like choosing the wrong algorithm: you’ll get to an answer eventually, but inefficiently, and sometimes incorrectly.
In the Microsoft Agent Framework, all five patterns are supported. In this article, I’ll walk through each one with a diagram, a real use case, and an honest assessment (as usually \ud83d\ude42 ) of where it works and where it breaks down.<\/p>\n\n\n\n

Why Multi-Agent in the First Place?<\/strong><\/p>\n\n\n\n

Before choosing a pattern, let’s be clear on why you’d use multiple agents at all.
Specialisation:<\/strong> Different agents can be optimised for different tasks. A research agent has deep web access and summarisation instructions. A writing agent has strict editorial style guidelines. A review agent is configured to be critical and flag inconsistencies. Each is better at its role than a single general agent would be.
Parallelism:<\/strong> Some tasks can run simultaneously. Research on three different topics, analysis of five different documents – these don’t need to happen sequentially.
Cognitive load management:<\/strong> A single agent handling a very complex task with too many tools and too many competing instructions performs worse than a set of focused agents. Specialisation improves individual agent quality.
Oversight:<\/strong> Multi-agent patterns make it natural to insert checkpoints – human approvals, validation agents, quality gates – at appropriate points in a workflow.<\/p>\n\n\n\n

Pattern 1: Sequential Orchestration<\/strong><\/p>\n\n\n\n

One agent after another. Each depends on the previous.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

How it works: <\/strong>Agent 1 completes its task and passes output to Agent 2. Agent 2 completes and passes to Agent 3. Execution is linear and predictable.
Best for:<\/strong>
– Multi-stage content pipelines (research -> write -> review -> publish)
– ETL-like processes (extract -> transform -> load)
– Compliance workflows where order matters (collect evidence \u2192 assess risk \u2192 generate report)

Real example: <\/strong>Customer complaint handling

– Agent 1: Extract complaint details and classify category
– Agent 2: Look up customer account history and previous complaints
– Agent 3: Draft personalised response based on context and resolution policy

Pros<\/strong>: Easy to reason about, predictable output, straightforward debugging
Cons:<\/strong> One slow agent blocks everything; not suited for independent parallel work
Code pattern:<\/p>\n\n\n\n

from azure.ai.agents import SequentialOrchestration\n\nworkflow = SequentialOrchestration(agents=[\n    collector_agent,\n    analyser_agent,\n    reporter_agent\n])\nresult = await workflow.run(initial_input=\"Customer complaint text here\")<\/code><\/pre>\n\n\n\n
Pattern 2: Concurrent Orchestration<\/strong><\/p>\n\n\n\n
Multiple agents run simultaneously. Results merged at completion.<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
How it works:<\/strong> All agents receive input simultaneously and process in parallel. A merge step combines their results.
Best for:<\/strong><\/p>\n\n\n\n
– Independent analysis of the same data from different angles
– Research across multiple domains simultaneously
– Competitive analysis (evaluate option A, B, C in parallel)<\/p>\n\n\n\n
Real example: Cloud architecture review<\/p>\n\n\n\n
    \n
  • Agent A: Security assessment of proposed architecture<\/li>\n\n\n\n
  • Agent B: Cost optimisation analysis<\/li>\n\n\n\n
  • Agent C: Well-Architected reliability review<\/li>\n\n\n\n
  • Merge: Consolidated findings report with all three perspectives<\/li>\n<\/ul>\n\n\n\n
    The same work that would take 3\u00d7 longer sequentially completes in roughly 1\u00d7 time (plus a small merge overhead).
    Pros<\/strong>: Dramatically reduces total processing time for parallelisable tasks
    Cons<\/strong>: Requires careful merge logic; all agents must complete before you get output; harder to debug individual failures<\/p>\n\n\n\n
    Pattern 3: Group Chat Orchestration<\/strong><\/p>\n\n\n\n
    Agents share a conversation and hand control dynamically.<\/p>\n\n\n\n
    \"\"<\/figure>\n\n\n\n
    How it works:<\/strong> All agents share a common conversation thread. An orchestrator agent (or a defined selection function) decides which agent responds next based on the current context. Agents can build on, challenge, or extend each other’s contributions.
    Best for:<\/strong><\/p>\n\n\n\n
    – Brainstorming and creative problem solving
    – Technical design reviews with multiple perspectives
    – Strategy development requiring diverse expert input<\/p>\n\n\n\n
    Real example: <\/strong>Architecture decision record (ADR) development<\/p>\n\n\n\n
      \n
    • Security architect agent: Raises security implications of proposed design<\/li>\n\n\n\n
    • Platform engineer agent: Identifies operational complexity concerns<\/li>\n\n\n\n
    • Cost analyst agent: Estimates TCO impact<\/li>\n\n\n\n
    • Orchestrator: Guides conversation toward consensus and synthesises the final ADR<\/li>\n<\/ul>\n\n\n\n
      The conversation evolves organically, with agents reacting to each other’s inputs.
      Pros:<\/strong> Rich collaborative output; agents can genuinely challenge each other; mimics expert panel dynamics
      Cons:<\/strong> Most complex to configure correctly; can loop without proper termination conditions; harder to predict conversation path; most expensive in token terms<\/p>\n\n\n\n
      Pattern 4: Handoff Orchestration<\/strong><\/p>\n\n\n\n
      One agent transfers control to another based on expertise.<\/p>\n\n\n\n
      \"\"<\/figure>\n\n\n\n
      How it works:<\/strong> The receiving agent has clear ownership. Once handed off, the previous agent is no longer in the conversation. Each agent specialises in a specific domain and knows its boundaries – it handles what it can and hands off when it can’t.
      Best for:<\/strong><\/p>\n\n\n\n
      – Customer service workflows with different specialisations
      – Escalation paths (tier 1 -> tier 2 -> specialist)
      – Domain routing (HR queries -> HR agent, IT queries -> IT agent)<\/p>\n\n\n\n
      Real example: <\/strong>IT helpdesk<\/p>\n\n\n\n
        \n
      • First-line agent: Handles password resets, common issues, account unlocks<\/li>\n\n\n\n
      • Network agent: Receives handoff for connectivity issues<\/li>\n\n\n\n
      • Security agent: Receives handoff for access permission requests<\/li>\n\n\n\n
      • Senior analyst agent: Receives handoff when other agents can’t resolve<\/li>\n<\/ul>\n\n\n\n
        Pros: <\/strong>Clean ownership; each agent has a focused scope; natural escalation path; easy to trace which agent handled what
        Cons: <\/strong>Requires careful handoff condition definition; risk of bouncing (A hands to B, B hands back to A); user experience can feel disjointed if handoffs aren’t smooth<\/p>\n\n\n\n
        Pattern 5: Magentic Orchestration<\/strong><\/p>\n\n\n\n
        Adaptive, emergent collaboration with no fixed execution order.<\/p>\n\n\n\n
        \"\"<\/figure>\n\n\n\n
        How it works: <\/strong>No fixed orchestrator. Agents self-organise based on the current state of the task. Any agent can contribute at any point. The system emerges to a solution through collective reasoning.
        Best for:<\/strong><\/p>\n\n\n\n
        – Complex, open-ended problems where the solution path isn’t known upfront
        – Research tasks requiring exploration before convergence
        – Creative generation with multiple contributing perspectives
        – Scenarios where rigid sequencing would miss important insights<\/p>\n\n\n\n
        Real example: <\/strong>Strategic planning<\/p>\n\n\n\n
          \n
        • Market analyst agent contributes competitive intelligence<\/li>\n\n\n\n
        • Financial analyst agent contributes cost\/revenue modelling<\/li>\n\n\n\n
        • Risk agent raises regulatory and operational concerns<\/li>\n\n\n\n
        • Innovation agent proposes differentiated approaches<\/li>\n\n\n\n
        • No fixed order – agents contribute when they have something relevant to add<\/li>\n<\/ul>\n\n\n\n
          Pros:<\/strong> Most flexible; can discover solution paths that predetermined patterns would miss; well-suited for genuinely complex problems
          Cons: <\/strong>Least predictable; requires careful termination conditions; most difficult to debug; most expensive; not suitable for processes requiring auditability or fixed ordering<\/p>\n\n\n\n
          Pattern Selection Guide <\/strong><\/p>\n\n\n\n
          \"\"<\/figure>\n\n\n\n
          Combining Patterns<\/strong>
          Real enterprise workflows often combine patterns. A common architecture:<\/p>\n\n\n\n
            \n
          1. Handoff routes the initial request to the right domain<\/li>\n\n\n\n
          2. Concurrent runs independent analyses in that domain<\/li>\n\n\n\n
          3. Sequential produces a final validated output from those analyses.<\/li>\n\n\n\n
          4. Human-in-the-loop pauses for approval before action<\/li>\n<\/ol>\n\n\n\n
            Don’t feel constrained to one pattern. Design for the actual process.<\/p>\n\n\n\n
            Honest Cost Warning<\/strong>
            Multi-agent systems multiply your token consumption. Every agent call costs tokens. Sequential four-agent workflows cost roughly 4\u00d7 a single agent call. Concurrent systems multiply in parallel. Group chat can spiral.<\/p>\n\n\n\n

            Before deploying any multi-agent system to production, calculate your expected token consumption per workflow execution and set cost alerts accordingly. I’ve seen elegant multi-agent demos that cost $5-7 per execution – which is fine for a demo, catastrophic at scale.<\/p>\n","protected":false},"excerpt":{"rendered":"
            Single agents are useful. Multi-agent systems are powerful. But “multiple agents working together” isn’t one thing – it’s five distinct architectural patterns, each suited to different problems. Using the wrong pattern is like choosing the wrong algorithm: you’ll get to an answer eventually, but inefficiently, and sometimes incorrectly.In the Microsoft Agent Framework, all five patterns […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[],"_links":{"self":[{"href":"https:\/\/bogdanburuiana.com\/index.php\/wp-json\/wp\/v2\/posts\/1519"}],"collection":[{"href":"https:\/\/bogdanburuiana.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/bogdanburuiana.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/bogdanburuiana.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/bogdanburuiana.com\/index.php\/wp-json\/wp\/v2\/comments?post=1519"}],"version-history":[{"count":5,"href":"https:\/\/bogdanburuiana.com\/index.php\/wp-json\/wp\/v2\/posts\/1519\/revisions"}],"predecessor-version":[{"id":1530,"href":"https:\/\/bogdanburuiana.com\/index.php\/wp-json\/wp\/v2\/posts\/1519\/revisions\/1530"}],"wp:attachment":[{"href":"https:\/\/bogdanburuiana.com\/index.php\/wp-json\/wp\/v2\/media?parent=1519"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/bogdanburuiana.com\/index.php\/wp-json\/wp\/v2\/categories?post=1519"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/bogdanburuiana.com\/index.php\/wp-json\/wp\/v2\/tags?post=1519"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}