--- title: "Multi-Agent Workflows — When One AI Isn't Enough | Certant" description: "A multi-agent workflow is a pipeline where each AI agent has one job, passing results to the next — with humans approving where it matters. See one real workflow end-to-end." url: "https://certant.ai/multi-agent-workflows" --- # When one agent isn't enough Real work has steps, branches and approvals. Give each step an agent with one job, handing its result and its sources to the next. [Start free](https://certant.ai/register) See the walkthrough One job per agent, a structured hand-off between them, and every run replayable. Example The low-risk path ships itself; the rest stops for a person. In one sentence A multi-agent workflow is a pipeline where each agent has one job, and hands its structured result to the next, with a person approving where it matters. One big agent that does everything is hard to debug and easy to confuse. A line of small ones can be replayed step by step and changed one piece at a time. The reframe ## Zapier moves data. Agents make decisions. If the work is "when X happens, copy it to Y", you don't need agents. Once there is a "but check the policy" or a "but escalate if it looks risky", you do. Workflow automation (Zapier, n8n) ### Wiring Triggers and actions: if a new row lands in Sheets, send a Slack message. Very good at moving data, and it never reads the document. Multi-agent workflows (Certant) ### Decisions between the wires Triggers and actions, plus agents that read, classify, score and explain. If a contract arrives, read it, flag the risky clauses, draft a redline, and ask the right person to approve it. The walkthrough ## Contract review, end to end What happens between a vendor emailing a contract and your risk team signing it off. 1. **Inbox watcher** Watches a shared inbox, picks up the PDF, and routes the workflow. 2. **Agent A: extract** Pulls the parties, dates, value, termination terms, indemnities and governing law into one structured record. 3. **Agent B: match policy** Compares each clause against your policy library, and returns a score and a citation for each finding. 4. **Branch: risk?** Low risk, and it approves itself with an audit log. Anything ambiguous carries on down the pipeline. 5. **Agent C, then a person** Drafts a redline with the citation, opens a ticket, and pings the right person to approve, edit or reject it. Pipeline design ## Why small agents win What makes a pipeline safer than one agent that does everything. Declares its shape Every agent declares its shape in and out, so a wrong shape is caught where it happens, not three steps later. JSON in, JSON out Pauses for approval Any node can pause for approval, and the approver sees the full reasoning trail before the rest of the pipeline runs on their decision. Approval on any node Full reasoning trail Replay the pipeline Replay the whole pipeline over data you already have, and see what each agent received, returned and cost against the previous version. Replay a run Cost vs last run FAQ ## What buyers ask Common questions on how a multi-agent workflow behaves, and where it differs from the tools you already run. What is a multi-agent workflow? A pipeline of agents where each one has a single, well-defined job, passing structured results to the next, with a person approving at the points that matter. One agent reads, the next decides, the next acts, and a person checks the parts that should never run unattended. What is agentic AI? Agentic AI describes systems that act rather than only answer. Agentic workflows chain together several agents and tools to complete a real business task end to end, with logging, replay and human approval built in. How is this different from Zapier or n8n? Zapier and n8n move data when a trigger fires. Multi-agent workflows make decisions: extracting, classifying, comparing, drafting, escalating. Same canvas, except each node can reason. Keep your existing Zaps and let them fire the pipeline. Can a multi-agent workflow include a human approval step? Yes. Any node in a Certant workflow can be a human-in-the-loop step. The workflow pauses, an approver sees the agent's reasoning and the citation, and approves, edits or rejects. The pipeline resumes with their decision logged. How do you stop multi-agent workflows from hallucinating? Two things. Every step has a known input and output shape, so a wrong shape is caught where it happens. And every reasoning step cites your documents, so a claim with no source never reaches the next agent. Can I edit a workflow without code? Yes. The agent builder is drag-and-drop. You can also drop Python or JavaScript anywhere in the flow, for the cases a template doesn't fit. ## Build one and run it Drag the agents, wire them together, replay against real data, then ship. No code required. [Start free](https://certant.ai/register) [Talk to an architect](https://certant.ai/contact-sales) In one line A pipeline makes the decisions between the wires, and stops when it should.