The Easiest Way to Create Self-Improving AI Agents
AI Summary
Summary of Video Transcript
Overview of Agentic Memory
- AI agents are evolving to resemble human cognitive functions, including reasoning and memory.
- Agentic memory is compared to human short-term and long-term memory.
- Short-term memory in AI agents includes conversation history and attention layers.
- Long-term memory is broken down into procedural, semantic, and episodic memory.
- The focus of the video is on semantic memory, which is similar to retrieval-augmented generation in AI agents.
Building Agents with Memory
- Two approaches to building semantic memory in agents:
- Background approach: Process data in the background and add to a vector database.
- Agentic approach: The agent updates its own memory, which is more natural and powerful.
Benefits of Agentic Memory
- Self-improvement: Agents can remember and learn from their mistakes.
- Personalization: Agents that remember user preferences provide a competitive edge.
Options for Building Memory in Agents
- Vector database: Most control but requires more work.
- Agentic framework with built-in memory: Easier at the start but limiting in production.
- Specialized agent memory platform: A balance between control and development effort.
Practical Tutorial Using MZero Platform
- The video provides a tutorial using the MZero platform and the Agenes framework on GitHub.
- Steps include creating a GitHub repo, setting up a virtual environment, and using Cursor agent to build agents.
- The agent built in the demo is a customer support agent with memory tools (read, update, delete memory) and a make refund tool.
- The tutorial includes testing and fixing tools, setting user IDs for distinct memories, and writing instructions for the agent.
Implementation and Testing
- The agent is tested to ensure it can remember user preferences and correct its own mistakes.
- The agent learns from errors encountered when performing tasks, such as processing refunds for non-refundable items.
- The tutorial demonstrates how the agent can prevent repeating the same mistakes in future interactions.
Conclusion
- Memory in AI agents is powerful for preventing mistakes and improving performance.
- The code for the video will be available for download, and viewers are encouraged to subscribe.
Detailed Instructions and URLs (if present)
- No specific CLI commands, website URLs, or detailed instructions were provided in the summary.