Building Agent Teams

Why Teams Beat Solo Agents

A single agent can write code, but it cannot objectively review its own work. Agent teams solve this by introducing delegation, specialization, and parallel execution — the same principles that make human engineering teams effective.

When a Code Agent writes an implementation and a separate Review Agent audits it, you get genuinely independent review. Add a Test Agent and you have three perspectives catching different classes of errors.

Composing Agent Teams

The most effective team for everyday development combines three specialists:

• Code Agent — Writes implementations, refactors existing code, builds features from specs
• Review Agent — Audits for bugs, security vulnerabilities, edge cases, and style violations
• Test Agent — Generates test suites, verifies correctness, checks coverage

For larger projects, add a Docs Agent (keeps documentation current) and a Planner Agent (breaks complex tasks into subtasks before coding begins).

Delegation Patterns

Agent teams need clear rules about who assigns work and how handoffs happen:

• Hierarchical — A lead agent (or you) assigns tasks to specialists. Best for structured workflows.
• Pipeline — Each agent passes output to the next in sequence. Best for code → review → test flows.
• Parallel fan-out — Multiple agents work simultaneously on independent subtasks. Best for speed.
• Consensus — Multiple agents evaluate the same output and must agree before proceeding. Best for critical decisions.

Shared Context

Agents on the same team need shared knowledge to stay aligned. The brain provides this through:

• knowledge-base.md — Permanent learned rules every agent reads before acting
• memory.md — Current working state so agents know what's in progress
• Agent memory files — Per-agent persistent state for specialized context
• CLAUDE.md — Project conventions and architecture decisions all agents follow

Without shared context, agents duplicate work, contradict each other, or miss critical requirements. Shared memory is the foundation of effective teams.

Quality Gates in Teams

A quality gate is a checkpoint where one agent must approve before the pipeline continues. The most important gate: the Review Agent must approve code before it gets committed.

Quality gates prevent bad code from accumulating. They enforce standards automatically, catch regressions early, and give you confidence that merged code meets your bar — every time, without exception.

What's Next: Level 301

You have completed AI 201. You now understand how to build, configure, and operate an AI brain with memory, skills, agents, MCP integrations, workflow automation, and agent teams.

Level 301: Agent Orchestration takes you further — swarm coordination, consensus protocols, autopilot mode, and managing teams of 10+ agents working in parallel on complex projects.