Practices / Agency

Agency AI Coordination Playbook

Coordination practices for agencies running AI agent teams that manage client advertising, call centers, project delivery, and sales pipelines. Battle-tested patterns from a 25-agent production deployment.

3 practices 10 categories

Learning

Measured

Every Correction Is a Learning

When the founder corrects an agent's output, that correction must be captured as a structured learning (what failed, what to do instead, why) before the agent continues. Corrections that never reach the learning system are wasted lessons. The system gets smarter only if corrections are recorded.

What goes wrong without this

The founder corrects the same mistake 5 times across 5 sessions. Each session starts fresh. The agent has no memory of being corrected. The founder gets frustrated and stops trusting agents.

Observed

Frontier Scanner with Quality Gate

A dedicated learning agent scans for new tools, frameworks, and techniques weekly. But every candidate must pass two gates: "Will this make our team better?" and "Is what we already have better?" Only "better than current" survives. No hoarding interesting links.

What goes wrong without this

The team adopts 3 new frameworks in a month because they looked promising. None are properly integrated. The existing tools were fine. Six weeks of work is wasted on horizontal moves disguised as improvements.

Rule

Nightly Maturity Evaluation

An evaluator agent scores the entire agent team nightly against a maturity framework (e.g., 8 Levels of Agentic Engineering). The score should feel like a challenge, not a compliment. Weaknesses at lower levels cap the score regardless of higher-level capabilities.

What goes wrong without this

The team adds advanced features (agent-to-agent messaging, autonomous outreach) while basic reliability (data accuracy, consistent formatting) is still broken. The foundation crumbles under the weight of complexity.

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