Best Practices for Using AI Agents – My Learnings

Nowadays, many companies are using AI agents to help with tasks. But without proper rules and watching, these agents can do wrong things also.

From my side, I am writing down what I learnt and collected from my experience, some research articles, and others say.

Important Practices for Managing AI Agents

1. Human-in-the-Loop (HITL) Strategy

Always keep some human person to check AI agent when it is taking big decision. For example, approving loan or rejecting customer request – should not be done fully by AI. Human should be in middle.

2. Monitoring and Logs

Whatever AI agent is doing, it should be written in logs. We should have dashboard to monitor. If anything wrong happens, at least we can check what agent was thinking or doing.

3. Governance and Control

There should be proper rules – who can use AI agent, what data it can access, and when it must stop. Like in banks, audit trails are important. Same thing for AI also.

4. Optimization and Feedback

Don’t just leave the agent after creating. Keep checking performance. Take feedback. Improve. Retrain. Like we do revision before exams.

5. Full Lifecycle Care (ModelOps)

From the day we deploy AI agent to the day we retire it, we must maintain it. Like we service our car time to time. Version control, monitoring, retraining – all are part of ModelOps.

More Learnings and Tips

1. Start with Small Use Case (Risk-Based)

Don’t give AI agent full control from first day. Start with simple task. See how it performs. Slowly give more work. Like we train new employee.

2. Explainability

Whatever AI decides, it should be explainable. Why it gave such output? What factors it considered? If agent cannot explain, we must worry.

Example: If agent rejects insurance claim, customer should know reason.

3. Fail-Safe Mechanism

If something goes wrong, AI agent should stop and alert team. It should not keep doing wrong thing. Like when ATM fails, it gives message and stops transaction.

3. Data Quality Matters

Bad data in – bad output from agent. Always make sure clean and updated data is given. Otherwise AI can become biased or give wrong answers.

4. Team Collaboration

Always involve cross-team – data scientists, software engineers, compliance officers, and business people. One person cannot manage AI agent alone.

Final Words

Using AI agents is like giving responsibility to a very smart but new employee. We must train, supervise, and correct. Just because it is AI doesn’t mean we leave everything to it. With proper care and best practices, AI agents can be great helpers.

Conclusion

Since I am working in a bank, couldn’t share my realtime examples. Hope I shared key best practices. Consider this as a living post. I will update time to time.