Agent Orchestration

At the Staff+ level, you’re designing agentic systems that are reliable, observable, and maintainable at scale.

But mega-agents that try to do everything usually fail. They drown in context, misprioritize tasks, and produce spaghetti prompts. It’s like asking one person to be your engineer, designer, PM, and finance team—burnout (and broken code) is guaranteed.

The fix is to think in systems: compose narrow, purpose-built agents into orchestrated workflows, where each agent is focused, debuggable, and safe.

One of the simplest and most scalable patterns for doing that is the orchestrator–worker model.

Orchestrator-worker pattern

The orchestrator–worker pattern involves two roles:

• The orchestrator agent: Think of this like a conductor. It doesn’t do the work itself; it decides what needs to be done and who should do it.

• Worker agents: These are your specialists: focused, narrow-scope agents optimized for a specific task. Fix linter errors, update docs, refactor legacy code, triage test failures—one job, done well.

Advantages of the orchestrator-worker approach

The orchestrator–worker pattern has many benefits:

• Composable: Add new agents easily—release bots, security scanners, etc. The orchestrator just needs to know who to call.

• Safer: If a worker hallucinates, its blast radius is small. A doc writer messing up a README is recoverable; a monolithic agent accidentally refactoring your database schema is not.

• Debuggable: Smaller agents = smaller logs = easier to trace issues when something breaks. Which it will.

Example: John’s agent workflow

Here’s a John-approved orchestrator–worker pattern:

• The orchestrator agent:

  • Watches GitHub issues (via API) and pulls new issues into an inbox.

  • Classifies the issue type and decides which worker agent should handle it.

• Worker agents (specialists):

  • Debugging agent: Reproduces test failures and suggests patches.

  • Refactoring agent: Improves code style, applies linting rules, and updates deprecated APIs.

  • Doc writing agent: Expands the README, adds usage examples, and fixes documentation typos.

  • Infra agent: Handles dependency bumps, CI pipeline tweaks, and configuration migrations.

Each worker produces a pull request draft. The orchestrator reviews metadata such as diff size, test results, and labels. If clean, it assigns reviewers or merges; if uncertain, it pings John. Instead of one confused “super-agent,” you now have a factory line where the right agent is always on the right job.

How can you start?

AI agents are similar to distributed systems: fragile if monolithic, powerful if orchestrated.

You don’t need to build John’s entire squad on day one.

Start with a two-agent setup:

• Orchestrator: Watches for new GitHub issues and decides which path to take.

• Worker: A simple bug fixer that patches linter issues.

Expand from there—split bug fixes into “unit test failures” vs. “runtime errors,” or add a docs worker.