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Most people miss these principles while building AI agents. I’ve explained everything that you should keep in mind. 1. Never run an agent without clear context. 2. Define who the agent is and what it is responsible for. 3. Always log inputs, actions, and outputs. 4. If it’s not logged, it didn’t happen. 5. Add guardrails before giving autonomy. 6. Limit what tools an agent can access. 7. Plan actions before executing them. 8. Separate reasoning from execution. 9. Make agents think before they act. 10. Measure outcomes, not just responses. 11. Optimize for results, not good-looking text. 12. Continuously evaluate agent behavior. 13. Feedback loops improve reliability. 14. Speed strongly affects user trust. 15. Reduce unnecessary model and tool calls. 16. Remember: agents are built to act, not chat. 17. Test agents in real-world scenarios, not demos. 18. Autonomy without control leads to failure. 19. Always verify tool outputs. 20. Call tools only when truly required. 21. Track cost, latency, and performance. 22. Hardcoded logic breaks over time. Keep system evolving. 23. Design workflows before choosing models. 24. One agent should handle one clear job. 25. Start with a single agent, then scale. 26. Clearly define agent roles and boundaries. 27. Assign tasks instead of repeating work. 28. Share context so agents can collaborate. 29. Use memory instead of repeating prompts. 30. Review every action to enable learning. 31. Assume failures will happen. 32. Build retries, fallbacks, and recovery paths. 33. Use standards to enable cooperation. 34. Observe behavior before optimizing. 35. Involve humans only when judgment is required. 36. Avoid hardcoding intelligence. 37. Design systems that can evolve. 38. Prefer event-driven systems over polling. 39. Make agents explain their decisions. 40. Transparency builds trust. 41. Use state to support long-running tasks. 42. Stateless agents forget too easily. 43. Optimize for scalability from day one. 44. Balance intelligence with cost and speed. 45. Treat agents as software systems, not prompts. 46. Good workflows beat powerful models. 47. Simple systems scale better than complex ones. 48. Real reliability comes from engineering discipline. 49. Test, break, fix, and repeat. 50. Build agents that survive real-world usage. This is a solid list for anyone or any company planning to build/use AI agents and systems. ✅ Repost for people in your network who're building or learning AI agents.