AI Agents in Production: 5 Lessons from the Banking Sector¶

Deploying an AI agent in a demo environment is easy. Getting it into production at a bank — that’s a different story.

Over the past 12 months, we deployed AI agents for document processing, customer support, and internal knowledge management in a regulated environment. Here are 5 things we learned.

1. Governance Is Not Nice-to-Have¶

In a bank, you can’t release an AI agent without an audit trail. Every action must be loggable, reproducible, and explainable.

What we did: - Every agent has defined permissions (RBAC) - Every action is logged to an immutable audit log - Kill switch for immediate agent shutdown

2. Evaluation > Vibes¶

“It looks good” is not a metric. We measure: - Accuracy — correctness of answers against a golden dataset - Latency — P50, P95, P99 - Cost per task — how much one processed invoice costs

3. RAG Needs a Chunking Strategy¶

Bad chunking = bad answers. We tested 6 different strategies and ended up with a hybrid approach: semantic chunking + overlap + metadata enrichment.

4. Human-in-the-Loop Is a Feature, Not a Bug¶

The agent shouldn’t decide everything. Escalation to a human for edge cases is the right design pattern. Our agents escalate ~5% of cases — and that’s OK.

5. Monitoring Is 50% of the Work¶

Deploying the agent is half the job. The other half is monitoring: drift detection, quality degradation, cost spikes. We use a custom dashboard with alerting.

Conclusion¶

AI in production isn’t about the latest model. It’s about governance, measurement, and operational maturity. If you want AI that earns money, not AI that demos — let’s talk.