Exploring Agentic Systems: Don't Build Agents for Everything

Join Barry, AI enthusiast and expert, as he dives into the world of agents, exploring three core ideas for building effective agents: don't build them for everything, keep it simple, and think like your agent.

1. Barry is speaking about building effective agents.
2. He and Eric wrote a blog post on this topic two months ago.
3. Today, they will discuss three core ideas from that post and share personal insights.
4. The first idea is not to build agents for every use case.
5. Instead, agents should be used to scale complex and valuable tasks.
6. Agents are best suited for ambiguous problem spaces where decision trees cannot be easily mapped out.
7. The second idea is to keep it simple when building agents.
8. An agent is a model using tools in a loop, with an environment, set of tools, and system prompt defining its behavior.
9. Keeping the initial complexity low allows for faster iteration and optimization later on.
10. The third idea is to think like your agents as you build them.
11. Agents make decisions based on their limited context, and understanding this can help bridge the gap between builder and agent understanding.
12. Most builders start by creating simple features such as summarization, classification, or extraction.
13. As products mature, more sophisticated models are used in creative ways through orchestrated model calls in predefined control flows (workflows).
14. Workflows allow for trading off resources for better performance but require more complex control flows.
15. Agents can decide their own trajectory based on environment feedback and are becoming increasingly popular in production.
16. Giving systems more agency results in greater usefulness and capability, but also increases the cost, latency, and consequences of errors.
17. The checklist for building agents includes considering task complexity, value, critical capabilities, and cost of error and discovery.
18. Coding is a great agent use case due to its complexity, high-value output, and easily verifiable results.
19. When iterating on agents, focus on building the three basic components first (environment, tools, prompt) before optimizing later.
20. Thinking like your agents helps improve understanding of their context and decision-making process.
21. Placing yourself in an agent's context can help identify necessary improvements for gaining user trust and avoiding unnecessary exploration.
22. Personal musings include making agents more budget-aware, enabling self-evolving tools, and exploring multi-agent collaborations in production.
23. The open questions are how to best define and enforce budgets, generalize meta tools for agent use cases, and enable asynchronous communication between agents.
24. The three takeaways from the talk are: don't build agents for everything, keep it simple, and think like your agents as you iterate.

Source: AI Engineer via YouTube

❓ What do you think? What are your thoughts on the ideas shared in this video? Feel free to share your thoughts in the comments!