Building your first multi-agent pipeline with OpenClaw
Single-agent setups work great for focused tasks. But the moment your use case involves multiple steps with different expertise requirements, you need a pipeline: a sequence of agents where each one handles a specific part of the job and passes the result to the next.
This guide walks through building a practical multi-agent pipeline for a common scenario: processing inbound customer messages. The pipeline has three agents. A classifier reads the incoming message and tags it (billing, technical, general). A specialist handles the tagged request using domain-specific tools and knowledge. A reviewer checks the specialist's draft response for tone, accuracy, and policy compliance before it goes out.
## Setting up your agents
Each agent in the pipeline gets its own SOUL.md and AGENTS.md. The classifier is lightweight. Its SOUL.md is about 50 lines: identity, classification categories, and rules for ambiguous cases. It does not need tool access, so its AGENTS.md is minimal.
The specialists are heavier. The billing agent needs access to the payment system API and the pricing knowledge base. The technical agent needs access to the docs search tool and the issue tracker. Each gets a focused SOUL.md under 200 lines covering only their domain.
The reviewer is interesting because it needs to understand all domains at a surface level without being an expert in any of them. Its SOUL.md focuses on communication guidelines, brand voice, and compliance rules. It reads the specialist's draft and the original customer message, then either approves the draft or sends it back with specific feedback.
## Wiring the pipeline in ClawVortex
In ClawVortex, you build this pipeline visually. Drag the classifier onto the canvas. Add the three specialists. Connect them with edges and define the routing conditions: "billing" tag routes to the billing agent, "technical" to the technical agent, "general" to the general agent.
Then add the reviewer downstream of all three specialists. Every specialist's output flows to the reviewer before reaching the customer. This is a fan-in pattern, and ClawVortex handles it natively. You do not need to write routing logic or manage message queues.
The visual canvas gives you something that YAML config never will: you can see the entire pipeline at a glance. When your product manager asks "what happens when a customer asks about a refund?" you can trace the path on screen in five seconds.
## Handling handoffs cleanly
The trickiest part of multi-agent pipelines is context preservation across handoffs. When the classifier passes a message to the billing agent, the billing agent needs the original message plus the classification metadata. When the billing agent passes its draft to the reviewer, the reviewer needs the original message, the classification, and the draft response.
ClawVortex manages this through a context envelope that accumulates data as it moves through the pipeline. Each agent can read everything upstream agents have added, and it appends its own output. You configure what each agent sees through the handoff settings on each edge. Sometimes you want the downstream agent to see everything. Sometimes you want to filter out intermediate reasoning to keep the context window focused.
## Testing before you ship
Before deploying, use ClawVortex's simulation mode to run test messages through the entire pipeline. Write test cases that cover each routing path, edge cases like messages that could go to multiple specialists, and adversarial inputs that try to confuse the classifier.
Pay special attention to the handoff points. Most pipeline bugs are not in the individual agents. They are in the transitions. A classifier that tags ambiguous messages as "general" when they should go to a specialist. A reviewer that rewrites responses so heavily it changes the meaning. These are the bugs that simulation catches.
## Monitoring in production
Once deployed, ClawVortex's fleet dashboard shows you end-to-end metrics for every pipeline run: total latency, per-agent latency, routing accuracy, reviewer approval rate, and customer satisfaction scores. The most useful metric early on is the reviewer rejection rate. If the reviewer is sending more than 15-20% of drafts back for revision, one of your specialists needs a SOUL.md tune-up.
Start simple, measure everything, and iterate. Your first pipeline will not be perfect. But with visibility into how each agent performs and where handoffs break down, you can improve it systematically instead of guessing.