The AI company brain architecture checklist.
Building it in-house? Here are the 10 layers a production-grade AI system actually needs — and exactly where each one tends to break. This is the same map we use when we build and audit them.
- 1
The brain (shared memory)
What good looks like: One versioned, permissioned source of truth every agent reads from and writes back to.
Where it breaks: Knowledge lives inside prompts; agents can't learn; every agent re-derives the same context.
- 2
Connections (tools & actions)
What good looks like: Agents act in your real systems through scoped, authenticated integrations with least privilege.
Where it breaks: Brittle one-off scripts and over-broad API keys with no sandbox.
- 3
Agents (scoped workers)
What good looks like: Each agent has a narrow job, explicit instructions, and hard limits — deterministic where it matters.
Where it breaks: One mega-agent does everything from a vague prompt with no boundaries.
- 4
Orchestration (control flow)
What good looks like: Work routes between agents with retries, queues, and waits for input.
Where it breaks: Everything is one synchronous chain that dies on the first error.
- 5
Human-in-the-loop (approvals)
What good looks like: Anything irreversible is approved by a person; spend and permission caps are enforced.
Where it breaks: Agents take irreversible actions with no checkpoint.
- 6
Observability (logs & traces)
What good looks like: Every action is logged with inputs and outputs — you can answer 'what did it do and why.'
Where it breaks: It's a black box; you can't debug it, so you can't trust it.
- 7
Evaluation (tests + feedback loop)
What good looks like: Test sets and regression checks, plus corrections flowing back into the brain so it compounds.
Where it breaks: No evals; quality drifts silently; fixes never stick.
- 8
Reliability (runs unattended)
What good looks like: Error handling, fallbacks, retries, and alerting — it survives bad inputs and outages.
Where it breaks: Works in the demo, breaks in production at 2am with no one watching.
- 9
Security & data
What good looks like: Secrets in a vault, clear data boundaries, PII handling, and access control.
Where it breaks: Keys in code and data leaking across customers or tenants.
- 10
Cost & scale
What good looks like: Token/cost controls, caching, and graceful behavior under load.
Where it breaks: Costs balloon unpredictably with no caps as usage grows.
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