Most AI agent deployments start with a single agent handling a single workflow. But as teams gain experience and tackle more complex problems, they encounter the natural ceiling of what one agent can do well in one context window.
Multi-agent systems assign distinct specializations to separate agents that collaborate toward a shared outcome. One agent researches; another analyzes; a third writes; a fourth reviews. The result is better than any single agent could produce alone — but it requires more careful architecture.
The following five examples are multi-agent systems running in production. Each example describes the individual agents, their roles, how they pass work to each other, and what the deployment actually achieves.
For foundational concepts, see What Are Multi-Agent Systems? and What Are AI Agents?. For single-agent examples by department, see AI Agent Examples in Business.
Example 1: Content Production Crew (Research + Analysis + Writing)#
Company profile: A B2B technology media company publishing 25-30 original articles per week. Their editorial team of 8 had a research-to-publish ratio of 4:1 — four hours of research for every one hour of writing. Total content capacity was constrained by research throughput.
The goal: Produce fully-researched, publication-ready article drafts without increasing editorial headcount.
The agents:
Agent 1 — Research Analyst
- Role: Gather all primary source material for a given topic
- Tools: Tavily search API (web research), Firecrawl (article extraction), Exa AI (academic/technical source retrieval)
- Input: Topic brief, target keyword, research parameters
- Output: A structured research packet — 8-12 source summaries, key statistics, relevant quotes, expert names, contradictory viewpoints
Agent 2 — Strategic Analyst
- Role: Synthesize the research packet into an article structure
- Tools: OpenAI o1 (complex reasoning), internal editorial style guide (retrieved via RAG from Pinecone)
- Input: Research packet from Agent 1
- Output: Detailed article outline — H2/H3 structure, which sources map to which sections, key points per section, word count targets, differentiation angle
Agent 3 — Writer
- Role: Draft the full article from the outline and research packet
- Tools: Anthropic Claude 3.5 Sonnet (writing quality), style guide (same Pinecone index)
- Input: Outline from Agent 2 + research packet from Agent 1
- Output: Full 1,200-1,800 word article draft
Agent 4 — Editor and Fact-Checker
- Role: Review the draft against the original research packet for factual accuracy, flag unsupported claims, and assess quality against editorial standards
- Tools: GPT-4o (critique mode), original source documents
- Input: Draft from Agent 3 + original research packet
- Output: Annotated draft with line-level comments, a quality score (1-10), and a list of required changes before human review
How they communicate: Built on LangGraph. Each agent operates as a node in a directed graph. The research packet and outline are stored in the shared LangGraph state object. Each downstream agent reads from state rather than receiving outputs directly, so any agent can be re-run without restarting the entire pipeline.
Human checkpoint: A human editor reviews the annotated draft from Agent 4 before any article is published. The typical review time dropped from 90 minutes (reading, fact-checking, structuring) to 20 minutes (reviewing the editor agent's flags and approving or overriding).
Outcomes:
- Article output per week increased from 28 to 44 without adding headcount
- Research time per article reduced from 4 hours to 18 minutes of human involvement
- Factual error rate (caught in final human review) decreased 34% compared to human-only drafts
- Time-to-publish from topic assignment reduced from 6 days to 1.5 days
What makes it work: The editor/fact-checker agent is the critical quality gate. Without it, errors from Agent 3 (hallucinated statistics, misattributed quotes) would reach human review unflaged. With it, the human reviewer is reading a pre-screened draft with specific issues already identified.
Example 2: Sales Qualification + Enrichment + Outreach Crew#
Company profile: A Series C enterprise software company with an outbound sales team of 14 SDRs. Target accounts were identified manually, enriched manually through LinkedIn and ZoomInfo, and outreach was templated with minimal personalization.
The goal: Increase the number of qualified, personalized outreach sequences per SDR per week without sacrificing message quality.
The agents:
Agent 1 — Account Qualifier
- Role: Evaluate inbound leads and target account lists against the company's ICP (ideal customer profile)
- Tools: Clearbit API (firmographic data), internal Salesforce data (existing customer profile), custom ICP scoring rubric
- Input: A list of company names or domains
- Output: Qualified account list with ICP fit scores (1-10) and justification for each score
Agent 2 — Enrichment Agent
- Role: Build a detailed intelligence dossier on each qualified account
- Tools: LinkedIn data (via Proxycurl API), G2 review data, company news via Tavily search, job postings via LinkedIn API, Crunchbase API (funding data)
- Input: Qualified account list from Agent 1
- Output: Per-account intelligence brief — recent company news, tech stack signals, hiring signals, decision-maker contact details, pain points inferred from reviews
Agent 3 — Personalized Outreach Writer
- Role: Draft a 3-touch outreach sequence for each account, personalized to the specific decision-maker and current company context
- Tools: OpenAI GPT-4o (copywriting), brand voice guide (RAG from Pinecone), successful sequence examples (few-shot prompting from top-performing historical sequences)
- Input: Intelligence brief from Agent 2
- Output: 3-email sequence with subject lines, personalized opening hooks referencing specific company context, value propositions matched to inferred pain points, and a clear call-to-action
Agent 4 — Compliance and Quality Reviewer
- Role: Review outreach for CAN-SPAM compliance, brand voice consistency, factual accuracy against the intelligence brief, and spam trigger word presence
- Tools: GPT-4o (review), compliance ruleset (structured document), spam scoring API
- Input: Draft sequences from Agent 3
- Output: Approved sequences or flagged sequences with required changes
How they communicate: Built on CrewAI. Each agent is a defined role with a specific task. The crew runs sequentially — Agent 1 → 2 → 3 → 4 — with each agent's task output passed as context to the next. A CrewAI "Process" definition controls the sequencing and output handoff schema.
This system also integrates with the Salesforce integration to automatically enroll approved sequences into Outreach.io for SDR execution.
Human checkpoint: SDRs review approved sequences in Outreach.io before sending. They can edit any message or skip the sequence for specific accounts. Approval takes 3-5 minutes per account vs. 45-60 minutes of previous manual research and writing.
Outcomes:
- Sequences created per SDR per week increased from 8 to 31
- Email reply rate on agent-generated sequences: 6.8% vs. 3.2% for manual sequences (higher personalization quality)
- Time from lead identification to first outreach email sent: reduced from 4.2 days to 18 hours
- Pipeline generated per SDR per quarter increased 38%
What makes it work: The intelligence brief is the quality foundation. If the enrichment agent produces shallow data, the writer agent produces shallow personalization. The Proxycurl and Crunchbase integrations surface specific recent events (funding round, new product launch, executive hire) that make outreach hooks feel research-based rather than generic.
Example 3: Support Triage + Resolution + Escalation Crew#
Company profile: A cloud infrastructure company with 11,000 business customers and a support team fielding 2,800 tickets per week. Tier-1 (basic) issues consumed 55% of support team time even though they were straightforward and repetitive.
The goal: Resolve tier-1 tickets without human involvement while maintaining customer satisfaction above 4.2/5.0.
The agents:
Agent 1 — Triage and Classifier
- Role: Classify incoming tickets by type, complexity, and urgency
- Tools: OpenAI GPT-4o (classification), Zendesk API (ticket metadata), internal taxonomy document
- Input: Raw ticket (subject, body, customer tier, account history)
- Output: Structured classification — ticket type, estimated resolution complexity (tier 1/2/3), urgency level, relevant knowledge base topic area
Agent 2 — Resolution Agent
- Role: Attempt to resolve tier-1 classified tickets autonomously using the knowledge base
- Tools: Retrieval-augmented generation (RAG) with Pinecone vector index of 1,400 documentation articles, OpenAI GPT-4o (response generation), internal runbook database
- Input: Ticket text + classification from Agent 1
- Output: A draft resolution response with a confidence score and cited source documentation
Agent 3 — Quality Validator
- Role: Review Agent 2's proposed resolution for accuracy, completeness, and tone before sending
- Tools: GPT-4o (validation), the same knowledge base (cross-reference check), tone guidelines
- Input: Proposed resolution from Agent 2
- Output: Approval (send automatically) or rejection with required changes. Rejection routes to human agent queue.
Agent 4 — Escalation and Context Builder
- Role: For tickets that cannot be resolved (tier 2/3, or Agent 2 confidence below 0.70), prepare a complete context brief for the human agent
- Tools: Zendesk API (full ticket history), Salesforce (account data), internal incident database
- Input: Ticket + Agent 1 classification (for non-resolved tickets)
- Output: Pre-built human agent brief: account tier, past ticket history on same issue, related incidents, recommended diagnostic steps
How they communicate: Built on LangGraph with a conditional router. After Agent 1 classifies the ticket, the router directs tier-1 tickets to Agent 2 → Agent 3. Tier-2/3 tickets skip to Agent 4. If Agent 3 rejects Agent 2's resolution, the ticket routes to Agent 4 rather than looping. AI agent memory is used to track conversation state so that a ticket can move between agents without losing context.
Outcomes:
- 47% of tickets resolved autonomously without human involvement
- CSAT on auto-resolved tickets: 4.1/5.0 (vs. 4.4 for human-resolved)
- Human agent time spent on tier-1 tickets: decreased from 55% to 8% of weekly volume
- Time-to-resolution for auto-resolved tickets: median 4 minutes (vs. 6 hours human average)
- Human agent satisfaction improved — specialists now primarily handle complex, interesting problems
What makes it work: The quality validator agent (Agent 3) is not optional. Early testing without it showed a 12% incorrect resolution rate that damaged CSAT. With Agent 3 in the loop, incorrect resolution rate dropped to 1.8%. The validator is what separates a customer-facing automation from a liability.
Example 4: Market Research + Analysis + Reporting Crew#
Company profile: A private equity firm requiring weekly market intelligence reports on 12 portfolio company sectors. Their research team of 4 analysts was spending 80% of their time on data collection and only 20% on analysis and insight generation.
The goal: Automate the data collection and initial synthesis layer so analysts focus on insight generation and recommendation.
The agents:
Agent 1 — Data Collection Agent
- Role: Gather sector-specific news, earnings data, regulatory updates, and competitive signals for each of the 12 sectors
- Tools: Tavily search (news and web), SEC EDGAR API (public company filings), Bloomberg Terminal API (market data), specific industry publication RSS feeds
- Input: Sector list, date range (past 7 days), data collection parameters
- Output: Raw data package per sector — 40-80 source excerpts, relevant financial data points, key events chronology
Agent 2 — Synthesis and Pattern Agent
- Role: Analyze the raw data package for each sector, identify patterns, extract signals, and flag anomalies
- Tools: OpenAI o1 (complex analytical reasoning), historical data comparison (vector search against prior weeks' reports in Pinecone)
- Input: Raw data package from Agent 1
- Output: Structured analysis per sector — key developments ranked by significance, trend directions, anomalies vs. prior period, competitor moves, regulatory changes
Agent 3 — Report Generation Agent
- Role: Assemble the synthesis outputs into formatted weekly reports
- Tools: Anthropic Claude 3.5 Sonnet (report writing), internal report template (structured prompt), Google Docs API (document creation)
- Input: Synthesis outputs from Agent 2 for all 12 sectors
- Output: 12-page weekly briefing document — one page per sector, formatted to the firm's established template
How they communicate: Built on a custom Python orchestration layer using LangChain as the agent framework. Agents 1 and 2 run in parallel across all 12 sectors (12 concurrent Agent 1 instances, 12 concurrent Agent 2 instances), dramatically reducing total runtime. Agent 3 waits for all 12 sector analyses to complete before assembling the report.
Outcomes:
- Report generation time decreased from 3 analyst-days to 2 hours of compute time
- Analyst time shifted from 80% collection / 20% analysis to 15% review / 85% strategic analysis
- Report coverage increased from 8 to 12 sectors with no additional staffing
- Two investment decisions were attributed to analyst insights surfaced by the agent system that would not have been caught under the previous manual process
What makes it work: Running agents in parallel across sectors is the key architectural decision. A sequential approach would take 12x longer. The LangGraph state design ensures that all 12 sector analyses are available to Agent 3 simultaneously, rather than requiring Agent 3 to start before all sectors complete.
Example 5: Code Review + Testing + Documentation Crew#
Company profile: A 90-engineer engineering team at a SaaS company. Code review was a bottleneck — senior engineers were spending 6-8 hours per week reviewing PRs, and some PRs waited 2-3 days for a first review. Documentation was chronically out-of-date.
The goal: Pre-screen PRs for common issues before human review, auto-generate test cases, and keep documentation synchronized with code changes.
The agents:
Agent 1 — Code Review Agent
- Role: Perform first-pass review of every PR before human review
- Tools: GitHub API (PR diff retrieval), GPT-4o (code analysis), internal coding standards document (RAG), static analysis tools (ESLint, Bandit via subprocess calls)
- Input: PR diff, target branch, PR description, linked ticket
- Output: Structured review comment posted to GitHub — categorized findings (bugs, security issues, style violations, performance concerns), severity ratings, specific line references, and suggested fixes
Agent 2 — Test Generation Agent
- Role: Analyze new or modified functions and generate unit test cases
- Tools: Anthropic Claude 3.5 Sonnet (code generation), existing test files (context via file read), testing framework documentation (Context7)
- Input: Modified functions from the PR diff
- Output: Draft unit tests posted as a PR comment or directly committed to the test branch if the coverage is below 80%
Agent 3 — Documentation Agent
- Role: Detect when code changes affect documented behavior and generate updated documentation
- Tools: GPT-4o (documentation generation), existing docs (retrieved via Confluence API), PR diff
- Input: PR diff + existing documentation for affected modules
- Output: Documentation update suggestions as PR comments, or for significant changes, a separate PR against the documentation repository
How they communicate: Triggered as a GitHub Actions workflow on every PR submission. Agent 1, Agent 2, and Agent 3 run in parallel (they each only need the PR diff, which is available from the start). Results are aggregated and posted to the PR within 8-12 minutes of submission.
Human checkpoint: Human engineers review all three agents' outputs. Agent 1 reviews are advisory — engineers decide which findings to address. Test generation outputs require engineer approval before commit. Documentation updates require a documentation owner to approve.
Outcomes:
- Human code review time per PR decreased from an average of 45 minutes to 22 minutes (agents handle first-pass, humans focus on architecture and logic)
- PR wait time for first review decreased from 2.3 days to same-day (agents provide immediate feedback; humans review next)
- Test coverage across the codebase increased from 61% to 79% in 8 months
- Documentation freshness score (percentage of docs updated within 30 days of related code changes) improved from 34% to 71%
What makes it work: Parallelism and non-blocking design. The three agents run simultaneously and post to the PR independently — they do not wait for each other. This means the engineer sees all three reports within 12 minutes of submitting the PR. A sequential design would take 30-40 minutes and create a new kind of bottleneck.
Choosing a Multi-Agent Framework#
| Framework | Best For | Complexity | |---|---|---| | CrewAI | Role-based teams, sequential workflows | Medium | | LangGraph | Complex branching, state-heavy workflows | High | | AutoGen | Conversational agent patterns | Medium | | Custom Python | Maximum control, unique orchestration needs | Very High |
For teams new to multi-agent systems, CrewAI is the recommended starting point — it has the most intuitive role/task abstraction and the most active community of production examples. For workflows requiring complex branching logic (like the support triage example above), LangGraph's graph model is more appropriate.
See the multi-agent systems guide for a technical deep-dive into communication patterns and orchestration architectures. Browse AI Agent Templates for starter configurations for each of the examples above. For a hands-on implementation walkthrough, see Build Your First AI Agent.
The individual agent examples that power these multi-agent systems are documented in the department-specific example pages: Customer Service Examples, HR Examples, and Marketing Examples. The full business context is at AI Agent Examples in Business.