AI Agents in Production: Planning, Tool Use, and Safety Boundaries

This is Post 4 in the AI Series. The previous post covered LLM system design and RAG.

What Makes an Agent Different

A chatbot answers once. An agent loops:

• plans,
• chooses tools,
• executes,
• checks results,
• retries or escalates.

Production Agent Architecture

• Planner: decomposes goals into steps.
• Executor: calls APIs/tools.
• Memory: persists relevant state.
• Evaluator: checks correctness and policy compliance.

Safety Boundaries

Essential controls:

1. Least-privilege credentials
2. Action confirmation for irreversible operations
3. Sandboxed tool execution
4. Immutable audit logs
5. Policy-based deny lists

References

• ReAct prompting paper: https://arxiv.org/abs/2210.03629
• Toolformer paper: https://arxiv.org/abs/2302.04761
• Anthropic agentic patterns (engineering notes): https://www.anthropic.com/engineering

Best Books

• Ronald T. Kneusel, Practical Deep Learning (deployment mindset).
• Martin Kleppmann, Designing Data-Intensive Applications (systems thinking).
• Chip Huyen, AI Engineering.