Building Enterprise AI Agents: Focus on Reasoning Over Knowledge

As a co-founder and CTO, Patrick shares lessons learned from rebuilding Rosco's product around AI agents, highlighting the importance of enabling autonomous reasoning, refining agent-computer interfaces, and designing effective multi-agent systems.

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: taking directions towards a specific objective, accessing tools and getting responses, and using autonomous reasoning to use its tools for that objective.
3. A key lesson in building agents is focusing on enabling the agent to think rather than relying on the underlying model's knowledge.
4. Rosco's product involves an AI agent searching and querying enterprise data in a warehouse.
5. Focusing on retrieval-based tool calls instead of inserting content into the system prompt helps agents perform retrieval and get relevant context for their actions.
6. Reasoning models, such as GPT 4.0, should first attempt to find data needed to answer questions and then inform users when they can't find it.
7. Small tweaks to the agent-computer interface (ACI) can significantly impact an agent's accuracy and performance.
8. The format of the response from a tool call is crucial for different models, as some may consume JSON or XML better than others.
9. An intelligent model making decisions on which tool call to make next is essential for agents.
10. Learning from the failure modes of agents using specific models can help improve agent performance.
11. Fine-tuning models is generally a waste of time, as it often decreases reasoning and overfits or overtunes the model to specific tasks.
12. Abstraction libraries, like Lang graph or Crew AI, have limitations, such as difficulties with end-user security credentials in production environments.
13. The most valuable part of building an agent is setting up the ecosystem around it, including user experience and connections/security protocols.
14. Designing and executing multi-agent systems requires a manager agent to delegate subtasks to worker agents and avoid overwhelming them.
15. A multi-agent team should ideally consist of between five and eight agents for optimal results.
16. Incentivizing the manager agent is more effective than forcing worker agents through specific steps.
17. Rosco's blog (asb.com) provides more detail on designing effective multi-agent teams.
18. Transitioning to an agent-based product involved introducing a multi-agent concept as customers became comfortable with single agents.
19. Implementing a manager agent within a hierarchy helps avoid overwhelming it with too much information and allows for better delegation of subtasks.
20. Rosco learned from their mistakes in designing agent systems and hopes to help others avoid making the same errors.
21. Keep the end goal in mind when choosing which tools or frameworks to use for building agents, especially when planning for production environments.
22. The user experience of how users interact with an agent and connections/security protocols are critical components of a successful agent system.
23. When designing multi-agent systems, limiting the number of agents working together increases the likelihood of accomplishing tasks.
24. Incentivizing manager agents helps ensure that worker agents contribute valuable output within the context of achieving broader outcomes.

Source: AI Engineer via YouTube

❓ What do you think? What is one key takeaway from Patrick's experience building AI agents, and how does it impact the design of effective agent systems? Feel free to share your thoughts in the comments!