Getting an AI agent demo running isn't difficult anymore.
Production is where the engineering starts.
If an agent can interact with business systems, I would design these seven layers before worrying endlessly about prompt optimization.
- Permission boundaries
Give the agent only the tools and data required for its specific job.
Reading an invoice and approving a payment are completely different permission levels.
- State
Your system needs to know what has already happened.
Otherwise retries can send duplicate emails, create duplicate records or execute actions twice.
- Deterministic validation
Important business rules should live in application logic.
Don't ask an LLM to verify every decision made by another LLM.
- Tool failure handling
APIs fail.
Tokens expire.
Schemas change.
The agent needs predictable error states.
- Observability
Track tool calls, latency, cost, failures, retries and outcomes.
Without this, debugging becomes guesswork.
- Human escalation
Low-confidence, high-value or irreversible actions should be capable of stopping for approval.
- Evaluation
Create repeatable test scenarios before changing prompts, tools or models.
Otherwise improvements are based on anecdotes.
Disclosure: I work at Inument and AI/software systems are part of the engineering work we deal with. One recurring lesson is that the model itself is often only a small part of production reliability.
The strongest agent architecture isn't the one with the most autonomy.
It's the one where autonomy is intentionally controlled.
What has been the hardest part of taking your agent from demo to production?
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