26 Key Takeaways from Building 150+ Agents in 9 months

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AI Summary

Video Summary: 26 Key Takeaways from Building AI Agents

1. AI agents are not employees or automations: They require specific SOPs (Standard Operating Procedures) to function.
2. Start with well-documented processes: SOPs simplify agent training.
3. Business owners won’t build their own agents: Demand for AI agent developers will increase.
4. Business owners often don’t know which agents they need: Consulting and analyzing customer journeys can identify valuable automation opportunities.
5. Fewer agents are better: Start with one and expand as needed to avoid complexity and maintenance issues.
6. Data-driven decisions with actionable data yield results: Combine knowledge with actions for better agent performance.
7. Prompt engineering is an art: Effective prompts are crucial for agent performance.
8. Integrations are as important as functionality: Agents must integrate into existing systems used by employees.
9. Agent reliability is a developer’s responsibility: Use data validation libraries like penic to ensure reliability.
10. Tools are crucial for building AI agents: Agents provide value through actions, not just responses.
11. Limit tools per agent: Four to six tools per agent to prevent confusion and hallucination.
12. Model costs are secondary to ROI: Focus on the value provided by the agent.
13. Clients don’t care about the model used: They value the outcome and privacy policies.
14. Don’t automate without established value: Validate the process manually before automating.
15. Focus on ROI: Use a formula to calculate the return on investment.
16. Agent development is iterative: Test different architectures to find the best solution.
17. Use a divide and conquer approach: Break down complex problems into manageable tasks.
18. Evals (evaluation metrics) are important for large companies: They help improve solutions over time.
19. There are two types of agents: Agents and workflows, with workflows being a sequence of agentic steps.
20. Agents need to adapt based on feedback: Include tools for self-analysis and environment interaction.
21. Don’t build around limitations: Assume models will improve and avoid becoming obsolete.
22. Deploying agents is harder than building: Integration into client processes is challenging.
23. Waterfall projects don’t work: Use a subscription model for agility and continuous improvement.
24. Include a human in the loop for critical agents: Review steps before full automation.
25. 2025 is the year of vertical AI agents: Specialized agents for specific use cases will become prevalent.
26. Agents don’t replace people: They help businesses scale and allow employees to focus on higher-level tasks.

The video emphasizes the importance of understanding AI agents, their development, and integration into business processes to maximize efficiency and ROI. It also highlights the evolving nature of AI and the need for adaptability in agent development.