5 Multi-Agent Patterns That Actually Work

After months of building and using multi-agent development workflows, certain patterns keep proving their worth. These aren't theoretical — they're patterns we use daily and see other developers adopt consistently.

Here are five that actually work in practice.

1. The Code Review Pipeline

Setup: Two terminal tiles, connected in one direction.

Writer Agent → Reviewer Agent

How it works: The writer agent implements a feature or fix. When it finishes, the output is routed to the reviewer agent with a prompt like "Review the changes in src/auth/ for correctness, security issues, and edge cases."

Why it works: AI agents catch different things depending on their prompt context. A writer agent is optimizing for correctness and completion. A reviewer agent, given explicit review criteria, catches issues the writer's tunnel vision missed.

Tip: Use the on-idle trigger with an 8-second silence window. This ensures the writer is truly done before the review begins. Set a stop keyword like "LGTM" so the loop terminates when the reviewer approves.

Typical cycle: 1 writing pass + 1-2 review rounds. Usually converges in under 10 minutes for a focused change.

2. The Test Generator

Setup: One implementation agent, one test-writing agent.

Implementation Agent → Test Agent

How it works: The implementation agent builds or modifies a feature. On completion, the routing system sends the file changes and a prompt to the test agent: "Write unit tests for the changes made to the auth middleware. Cover the happy path, error cases, and edge cases."

Why it works: Writing tests immediately after implementation, while the code is fresh, catches bugs at the cheapest possible moment. The test agent has the full context of what changed and why.

Variation: Reverse the direction — write tests first (TDD style), then route the test specifications to the implementation agent.

3. The Parallel Module Builder

Setup: Multiple terminal tiles, no connections. A note tile in the center for coordination.

Agent A (auth)    Agent B (api)    Agent C (database)
      \               |               /
       \              |              /
        Note: Architecture Plan

How it works: You break a large task into independent modules. Each agent gets a specific module with clear interface boundaries. The note tile contains the shared architecture plan and interface contracts that all agents reference.

Why it works: This is the highest-throughput pattern. Three agents working on independent modules finish 3x faster than one agent working sequentially. The key is defining clean interfaces upfront so the modules integrate cleanly.

When to avoid: If the modules have tight coupling or shared state, agents will step on each other. Reserve this pattern for truly independent work with well-defined interfaces.

4. The Iterative Refinement Loop

Setup: Two agents connected bidirectionally with round limits.

Agent A ⇄ Agent B (max 3 rounds)

How it works: Agent A makes an initial attempt. Agent B critiques it and suggests improvements. Agent A revises based on the feedback. This continues for a set number of rounds (typically 2-3).

Why it works: Each round tightens the solution. The first pass gets the structure right. The second catches bugs and edge cases. The third polishes. Diminishing returns kick in after 3 rounds — set the max accordingly.

Configuration: Use ai-routing transform so each agent's feedback is reframed as an actionable instruction, not raw output. Set maxRounds: 3 and cooldownMs: 5000 to give each agent enough time to produce a complete response.

Example prompts:

• Round 1 (A→B): "Review this implementation of the rate limiter for correctness and performance."
• Round 2 (B→A): "Apply these fixes: [specific issues]. Then verify the fix handles the concurrent access edge case."
• Round 3 (A→B): "Final review of the revised rate limiter. Approve with LGTM if ready to ship."

5. The Scout and Builder

Setup: A lightweight, fast agent scouts the codebase; a more capable agent builds.

Scout Agent → Builder Agent

How it works: The scout agent (using a smaller, faster model or simpler prompts) explores the codebase to answer preliminary questions: "What files are involved in the payment flow? What patterns does the codebase use for error handling? What tests exist for the billing module?"

Once the scout has gathered context, its findings are routed to the builder agent with the actual implementation task, now enriched with codebase-specific context.

Why it works: Large implementation tasks often fail because the agent doesn't have enough context about the existing codebase. The scout phase is cheap (fast model, read-only operations) and produces the context map that makes the builder's work precise.

Tip: Use the full-output transform for this pattern. The scout's output is already structured information (file lists, pattern descriptions) that the builder can consume directly without AI reformulation.

Choosing the right pattern

These patterns compose. A real workflow might use the Scout and Builder to gather context, the Parallel Module Builder to implement, and the Code Review Pipeline to validate — all on the same canvas.

Anti-patterns to avoid

The Infinite Loop: Two agents routed to each other without round limits or stop keywords. They'll generate increasingly abstract meta-commentary. Always set maxRounds.

The Kitchen Sink: Routing every agent's output to every other agent. Information overload. Each connection should have a clear purpose.

The Premature Router: Setting up complex routing for a task that takes 2 minutes with a single agent. The overhead of configuring connections isn't worth it for simple, self-contained tasks.

The best multi-agent workflows feel effortless — agents working in parallel, outputs flowing to where they're needed, and you making the strategic decisions while the execution happens around you.