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Multi-Agent Orchestration Patterns: Getting AI Agents to Actually Cooperate

Practical patterns for chaining specialized AI coding agents into a pipeline — planner, coder, reviewer — that produces reviewable output instead of chaos.

ai coding automation open-source workflow

Multi-Agent Orchestration Patterns: Getting AI Agents to Actually Cooperate

One AI coding agent is good. Two can be chaos. The difference is whether you have a clean handoff between them.

Most developers start with a single agent and hit the same wall: Claude Code is great at reasoning but the context window gets expensive. DeepSeek is fast and cheap but misses edge cases. Codex CLI has solid tool integration but you're still babysitting the loop.

The answer isn't picking the best model. It's making them work together with clear phase boundaries.

The Pipeline Pattern That Actually Works

Instead of asking one model to do everything, split the work into phases where each agent owns a specific job and hands off a reviewable artifact:

  1. Plan — Strong reasoning model defines the scope, acceptance criteria, and what "done" looks like
  2. Build — Fast coding model implements against that spec in an isolated workspace
  3. Verify — A second model (or the same model with different prompting) reviews the diff, runs checks, and flags anything sketchy
  4. Fix — Targeted corrections on anything the review caught, with a tight loop ceiling so it doesn't drift

The key isn't more agents. It's that each phase produces something the next phase can actually judge.

This is cost arbitrage applied to AI coding workflows: the expensive model only runs where reasoning adds value. The cheap model handles the bulk of implementation where speed matters more.

Configuring the Pipeline with Ralph Workflow

Ralph Workflow lets you define this pipeline in a single TOML file. Each phase gets its own agent, model, and validation gate — so you're not copy-pasting between terminal windows at 2 AM.

[phases.planning]
agent = "claude-code"
model = "claude-sonnet-4-20250514"

[phases.development]
agent = "codex-cli"
model = "gpt-5"

[phases.review]
agent = "claude-code"
model = "claude-sonnet-4-20250514"
prompt = "review for bugs, edge cases, and security issues"

[phases.fix]
agent = "claude-code"
model = "claude-haiku-4-20250514"

Ralph Workflow handles the workspace isolation, the handoff artifacts, and the stop-conditions between phases. You define what each phase should produce. It makes sure the next phase actually receives it.

Start Small, Then Compose

You don't need all four phases on day one. Start with two:

  • One agent builds. Another agent reviews the diff.
  • If the diff is small and the checks are green, you merge.

That alone catches most of the silent failures that happen when a single agent both writes and certifies its own output.

Once that loop is clean, add planning and targeted fix phases. The pipeline grows with your trust in it.

Getting Started

pip install ralph-workflow
ralph init my-project
ralph run --task "Build a REST API for user authentication"

Primary repo (Codeberg): codeberg.org/RalphWorkflow/Ralph-Workflow Mirror (GitHub): github.com/Ralph-Workflow/Ralph-Workflow Docs: ralphworkflow.com/docs

This content was generated as part of the Ralph Workflow content pipeline. Content is reviewed before deployment.

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