Building AI Agents: Focus on Reasoning, Iterate ACI for Optimized Performance

As the co-founder and CTO of Rosco, I've learned valuable lessons about building AI agents and multi-agent systems, including the importance of focusing on reasoning over knowledge, iterating on the agent computer interface, and designing effective manager agents to incentivize worker agents.

1. Patrick is the co-founder and CTO of Rosco, which rebuilt its product around AI agents two years ago.
2. An AI agent is defined by three criteria: ability to take directions, access to tools with responses, and autonomous reasoning for tool usage.
3. A key lesson learned was focusing on enabling the agent to think rather than limiting it to what the underlying model knows.
4. Rosco's product enables an AI agent to search and query enterprise data in a warehouse.
5. Limitations arise when focusing on knowledge over reasoning, such as writing SQL queries with given data.
6. Discreet tool calls for retrieval help agents find relevant context for their actions.
7. Reasoning models allow models to attempt finding data first and then admit if they cannot, rather than forcing a query response.
8. GPT 40 may write bad queries regardless of the underlying data's presence or ability to answer a question.
9. Small tweaks in agent-computer interface (ACI) syntax and structure can significantly impact an agent's accuracy and performance.
10. The format of the response depends on the model, with GPT 40 preferring JSON over markdown and Claude requiring XML.
11. The actual model making decisions should be generally intelligent for best results, even if other tasks run on cheaper models.
12. Model hallucinations can indicate how a model expects tool calls to be defined, helping improve agent performance.
13. Fine-tuning models is often a waste of time, as it may decrease reasoning and overfit the model to specific tasks.
14. Choosing an abstraction or framework should depend on end goals and production requirements.
15. An agent's setup, user experience, connections, and security protocols are more valuable than system prompts.
16. Designing and executing multi-agent systems requires a manager agent to delegate subtasks to worker agents.
17. Implementing a manager agent helps prevent the main agent from becoming overwhelmed by too much information.
18. A "two pizza rule" applies to the number of agents working together, typically between 5 and 8, for optimal task completion.
19. Incentivizing the manager agent is crucial for effective multi-agent team design.
20. The manager agent should rely on worker agents' output to achieve broader objectives.
21. Rosco's blog post at asb.com provides more details about designing effective multi-agent teams.
22. Understanding and avoiding common mistakes in agent system design can save time and resources.
23. Model reasoning abilities are essential for user satisfaction, as models with logical fallacies will negatively impact the user experience.
24. Producing a high-quality agent product involves designing an effective ecosystem around the agent, including user interaction, connections, and security protocols.

Source: AI Engineer via YouTube

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