Antfarm Patterns: Orchestrating Specialized Agent Teams for Compound Engineering

Published: at 05:00 AM

Antfarm Patterns: Orchestrating Specialized Agent Teams for Compound Engineering

How multi-agent workflows turn compound engineering from theory into practice

TL;DR

Compound engineering promises 300-700% productivity gains, but most teams struggle to actually do it. The secret? Building orchestrated AI agent teams where each agent has a specific role, fresh context, and clear handoffs.

Antfarm makes this practical with:

The result? Features that ship in hours instead of weeks, with fewer bugs and less human toil.

In this post, I’ll walk through real patterns you can use today—with concrete YAML examples, lessons from running these workflows in production, and a honest look at what’s hard.

I’ve Been Here Before

A few months ago, I was hammering away with a single AI agent trying to build a feature. It started strong—generating code, running tests, making progress. But as the conversation grew, things got… messy.

The agent would:

I was spending more time babysitting the AI than actually building. The promise of compound engineering—300-700% velocity gains—felt distant.

Then I discovered multi-agent patterns. The shift was night and day.

Instead of one generalist agent doing everything, I split the work:

Each got a fresh session, clear expectations, and explicit acceptance criteria.

The difference? The first feature shipped in 45 minutes with zero human intervention. That’s when I knew this was the future.

Why Multi-Agent Beats Single-Agent

Before we dive into Antfarm, let’s talk about why specialization matters for AI agents.

The Context Degradation Problem

LLMs have a well-documented issue: as conversations get longer, they start to lose the plot. You’ve seen it—after 50 messages, the model starts hallucinating, forgetting what you agreed on, making sloppy mistakes.

The Ralph Loop solved this by starting fresh each iteration. But with a single agent doing everything in one long session, you still hit the wall eventually.

Antfarm’s insight: Each step gets its own clean session. No shared memory except git and progress files. No context rot. The agent only sees what it needs to see right now.

Specialization Enforces Discipline

When one agent tries to both implement and verify, it’s tempted to:

With separate agents, the verifier’s only job is to say “this isn’t good enough” if it’s not. The tester lives to find failure modes. The reviewer applies consistent standards across all stories.

This isn’t just about quality—it’s about feedback integrity. Each step gives honest, uncompromised feedback to the next.

Parallelization Without Chaos

In traditional teams, parallel work causes merge conflicts, integration hell, and communication overhead. With Antfarm, each agent works in its own branch-like isolation, then passes validated artifacts downstream.

You can run multiple stories in parallel (if they’re independent), and the workflow ensures clean handoffs. No more “waiting on backend” because the backend agent is already done.

Real Workflow: Feature Development

Let’s look at the feature-dev workflow that Antfarm ships with:

steps:
  - id: plan
    agent: planner
    input: |
      Decompose this feature request into discrete, implementable stories.
      Each story must have clear acceptance criteria.
      Reply with STATUS: done and STORIES: [list with criteria]

  - id: setup
    agent: setup
    input: |
      Prepare workspace for implementation.
      Install dependencies, configure environment.
      Reply with STATUS: done when ready.

  - id: implement
    agent: developer
    input: |
      Implement the next incomplete story from {{plan}}.
      Follow the project's architectural patterns.
      Run typecheck and lint before marking done.
      Reply with STATUS: done and FILES_CHANGED: [list]

  - id: verify
    agent: verifier
    input: |
      Verify the implementation against acceptance criteria from {{plan}}.
      Does the code actually meet requirements?
      Reply STATUS: done if verified, STATUS: retry with feedback if not.

  - id: test
    agent: tester
    input: |
      Run the project's test suite.
      Add regression tests for the new feature.
      Ensure all tests pass.
      Reply STATUS: done when tests green.

  - id: pr
    agent: developer
    input: |
      Create a pull request for the changes.
      Include summary, testing notes, and screenshots if applicable.
      Reply STATUS: done with PR URL.

  - id: review
    agent: reviewer
    input: |
      Review the PR for code quality, security, performance.
      Request changes or approve.
      Reply STATUS: approved or STATUS: changes-requested with feedback.

This is compound engineering in action—every step has a clear handoff, acceptance criteria, and automated validation. No step advances until the previous one succeeds.

The Human Touch (Because We’re Not There Yet)

Let me be honest: these workflows aren’t magic. I’ve run them enough to know where they shine and where they stumble.

What works beautifully:

Where they still struggle:

The sweet spot? Well-specified, bounded tasks. The more you can break work into discrete, verifiable stories, the better Antfarm performs.

My rule of thumb: if you can describe the done state in one clear sentence, Antfarm can probably build it.

Designing Your Own Workflows

You’re not limited to the bundled workflows. The power of Antfarm is defining custom agent teams for your specific needs.

Start Simple

Don’t try to build a 7-step workflow on day one. Start with:

  1. plan → implement → review

Get that working end-to-end. Then add verify, then test, then pr. Each step should earn its keep.

Personas Matter

Each agent’s AGENTS.md defines its personality and constraints:

# Verifier Agent

You are a senior QA engineer with a skeptical mindset. Your job is to say "no" until the work is truly complete.

## Guidelines
- Check every acceptance criterion from the plan
- Run the code yourself if possible
- Verify edge cases are handled
- Don't accept "works on my machine" without evidence

## Output Format
STATUS: done | retry
FEEDBACK: [detailed, specific feedback if retry]

A clear, bounded persona helps the AI stay in character and do the job you need.

Handoffs Are Everything

The magic is in the {{plan}} and {{verify}} references—each step receives the actual output of the previous step, not just a summary. This creates a chain of evidence that nothing was lost in translation.

If the planner says “implement user authentication with bcrypt,” the verifier sees the actual implementation and can check: “Is bcrypt actually used? Are passwords salted? Is there rate limiting?”

This isn’t just automation—it’s auditable, reproducible engineering.

Metrics That Matter

How do you know if your compound engineering setup is actually working? Track these:

MetricTargetWhy It Matters
Cycle time per story< 30 minMeasures actual velocity
First-pass success rate> 70%High rate = good specs & agents
Human touch rate< 20%Low rate = agents understand standards
Escalation rate< 5%Low rate = workflows are well-designed

If your escalation rate is high, your workflows are too complex or your agents need better prompts. If first-pass success is low, your acceptance criteria are vague.

The Bigger Picture: This Is How We Scale

I’m convinced that multi-agent orchestration is the only way to achieve true compound engineering at scale. Single-agent workflows plateau. Human-only teams hit headcount limits. But agent teams?

This isn’t replacing engineers—it’s freeing engineers from the low-leverage work of writing boilerplate, writing basic tests, and reviewing trivial changes.

The engineers who win will be those who can design, orchestrate, and improve these agent systems—not those who write the most code themselves.

That’s the compound engineering mindset.

Getting Started Today

If you want to try this:

  1. Install Antfarm (see their README)
  2. Run a sample: antfarm workflow run feature-dev "Add dark mode toggle"
  3. Watch the dashboard at http://localhost:3333
  4. Tweak the agent personas to match your project
  5. Ship your first AI-built feature with zero implementation effort

Once you’ve felt the velocity of an agent team that just… works… there’s no going back.

Further Reading

I’m Vinci Rufus, exploring the intersection of agentic AI and compound engineering. I write about building reliable, high-velocity AI systems. Follow me on Twitter @areai51 or read more at vincirufus.com.

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