Patrick Dougherty - How to Build AI Agents that Actually Work
AI Summary
Summary of AI Agent Development Lessons
Definition of an AI Agent
- Criteria for an AI Agent:
- Ability to take directions (human or AI-provided).
- Access to at least one tool and can receive a response.
- Autonomous reasoning to use tools for achieving objectives.
Key Lessons Learned
- Focus on Enabling Reasoning:
- Prioritize retrieval tool calls over inserting content into system prompts.
- Agents should iteratively use tools like ‘search tables’ or ‘profile a column’ to construct SQL queries.
- Reasoning vs. Knowledge:
- Reasoning models can determine if data is sufficient to answer a question.
- GPT-4 often attempts to write queries without considering data sufficiency.
- Agent Computer Interface (ACI):
- Small tweaks to ACI can significantly impact agent performance.
- Response format matters; JSON improved GPT-4L’s performance, while XML was better for Claude.
- Model Quality:
- The model’s quality is crucial; it should be intelligent and capable of making good decisions.
- Claud 3.5 is favored for its balance of speed, cost, and decision-making.
- Model Expectations:
- Observing a model’s failure modes can indicate its expectations for tool calls.
- Aligning tool calls with these expectations can improve performance.
- Fine-Tuning Models:
- Fine-tuning is generally a waste of time and can decrease reasoning capabilities.
- Abstraction and Frameworks:
- Abstractions were not used due to lack of availability and production considerations.
- Consider end goals before relying on third-party frameworks.
- Agent Ecosystem:
- The ecosystem around the agent, including user experience and security protocols, is crucial.
Multi-Agent Systems
- Implementing a Manager Agent:
- A manager agent delegates tasks to worker agents and ensures the final outcome.
- Optimal Number of Agents:
- A team of 5 to 8 agents is optimal for task completion.
- Incentivization:
- Incentivize the manager agent to achieve the overall objective and manage worker agents effectively.
Additional Resources
- Blog Post:
- Further details on designing effective multi-agent teams can be found on Patrick’s blog at asb.com (exact URL not provided).
Notes
- The video does not contain any CLI commands, website URLs (aside from a mention of a blog post without an exact URL), or detailed instructions that need to be extracted.