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5 production-tested patterns

Multi-Agent Orchestration Patterns.

How you coordinate agents matters as much as what each agent does. The 5 patterns we use in production — with trade-offs, examples, and when to use each.

New to multi-agent architecture? Read Multi-Agent Teams: Why One Agent Is Never Enough for the strategic rationale before diving into patterns.

1

Pattern 1

Sequential Pipeline

Agent A → Agent B → Agent C → Output

Each agent completes its work and passes the result to the next agent in a fixed sequence. Every stage builds on the previous one.

Best for:

  • Email workflows: triage → research → draft → compliance check
  • Content pipelines: research → outline → write → edit
  • Data processing: extract → transform → validate → load

Strengths

  • +Simple to build and debug
  • +Clear data flow — easy to trace errors
  • +Each agent has a focused, narrow prompt

Trade-offs

  • -Total latency is the sum of all stages
  • -One slow agent bottlenecks the entire pipeline
  • -Not ideal for tasks with no natural order

Real-world example

Our email automation deployment uses this pattern. The inbox agent classifies and extracts requirements, passes to the research agent for data gathering, then to the content agent for draft production. Each stage is independent and testable.

2

Pattern 2

Parallel Fan-Out

Task → [Agent A | Agent B | Agent C] → Merge → Output

Multiple agents work simultaneously on different aspects of the same task. Results are collected and merged downstream.

Best for:

  • Research tasks: SEC filings + market data + news — all at once
  • Due diligence: financial check + legal check + background check
  • Multi-source aggregation: pull from 3-5 APIs simultaneously

Strengths

  • +Dramatically reduces latency for data-heavy tasks
  • +Agents are fully independent — no shared state
  • +Easy to add or remove parallel branches

Trade-offs

  • -Requires a merge step to combine results
  • -All branches must handle failure independently
  • -Higher concurrent API costs (burst usage)

Real-world example

For financial research workflows, we fan out to three agents simultaneously: one pulls SEC filings, one grabs real-time market data, and one scans recent news coverage. A merge agent combines results into a single research brief. What would take 15 minutes sequentially finishes in under 2 minutes.

3

Pattern 3

Supervisor / Worker

Task → Supervisor → [Worker A | Worker B | Worker C] → Supervisor → Output

A supervisor agent receives the task, decides which worker agents to invoke, collects their outputs, and assembles the final result. The supervisor handles routing logic.

Best for:

  • Complex requests that need dynamic routing
  • Customer support with multiple specializations
  • Tasks where the right approach depends on the input

Strengths

  • +Adaptive — the supervisor chooses the best workers per task
  • +Workers can be added without changing routing logic
  • +Supervisor handles error recovery and retries

Trade-offs

  • -Supervisor is a single point of failure
  • -Supervisor prompt is more complex than worker prompts
  • -Extra LLM call for routing decisions adds latency and cost

Real-world example

Our most common production pattern. A client's support agent receives a customer inquiry. The supervisor classifies the intent (billing, technical, account), routes to the appropriate specialist worker, reviews the worker's output for quality and tone, then delivers the final response. If the worker's answer is insufficient, the supervisor can retry or escalate.

4

Pattern 4

Human-in-the-Loop

Agents → Checkpoint → Human Review → Agents → Output

Agents handle research, drafting, and formatting, then pause at defined checkpoints for human approval. The human reviews, edits, or rejects. Agents incorporate feedback and continue.

Best for:

  • External communications (emails, proposals, reports)
  • Financial data and compliance-sensitive workflows
  • Any task where errors have significant consequences

Strengths

  • +Maximum accuracy — human catches what agents miss
  • +Builds trust during early deployment phases
  • +Meets compliance requirements for regulated industries

Trade-offs

  • -Throughput limited by human review speed
  • -Requires a well-designed review interface
  • -Human becomes the bottleneck at scale

Real-world example

The default pattern for any AlphaForge deployment handling external communications. The agent team does 90% of the work — research, drafting, formatting, compliance checks — then presents the output for human review. The human spends 30 seconds reviewing instead of 30 minutes producing. Over time, as trust builds, checkpoints can be reduced.

5

Pattern 5

Event-Driven / Reactive

Event → Router → Agent(s) → Action → Monitor

Agents respond to external triggers — new emails, CRM updates, scheduled times, webhook events. No human initiates the workflow. The system watches for events and acts autonomously within defined boundaries.

Best for:

  • Inbox monitoring and auto-response
  • Lead scoring when new contacts enter a CRM
  • Scheduled reports and recurring data pulls
  • Alert-based workflows (price changes, mentions, deadlines)

Strengths

  • +Zero human effort for routine tasks
  • +Responds instantly — no wait for someone to notice
  • +Scales to hundreds of events per day without added labor

Trade-offs

  • -Requires robust error handling — no human to catch failures
  • -Must define clear boundaries to prevent runaway actions
  • -Monitoring and alerting are non-negotiable

Real-world example

Our lead generation agents use this pattern. When a new form submission hits the CRM, an event triggers the qualification agent to score the lead, the research agent to pull company data, and the outreach agent to draft a personalized follow-up. The entire sequence fires without human intervention — the sales team sees a qualified, researched lead with a draft email ready to send.

Quick decision guide

If your workflow...Use this pattern
Has clear, ordered steps that build on each otherSequential Pipeline
Needs data from multiple independent sourcesParallel Fan-Out
Requires dynamic routing based on input typeSupervisor / Worker
Produces external communications or handles sensitive dataHuman-in-the-Loop
Responds to external triggers without human initiationEvent-Driven / Reactive

Most production deployments combine 2-3 patterns. A supervisor routes to workers that run sequential pipelines, with human-in-the-loop checkpoints for external communications.

Pick the right pattern

Not sure which pattern fits?

Describe your workflow to our AI architect. You'll get a scoped recommendation with the right pattern, team size, and cost estimate in minutes.

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