Link to Folder with Everything
2025.03.26.Kono.Office.Hours.19 - Google Drive
Recording of Video
[https://drive.google.com/file/d/18ZHYx4zlkf4YaIWYyLxwSxniTy9ZpaMY/view?usp=sharing](https://drive.google.com/file/d/18ZHYx4zlkf4YaIWYyLxwSxniTy9ZpaMY/view?usp=sharing)
Summary ( by Gemini )
Rahul Singh led an office hours session focusing on AI agent design and automation. The discussion covered agent design principles, tools, and the importance of agent specifications over specific frameworks. Participants explored creating conversational agents using 11 Labs and Nen, and demonstrated how to leverage ChatGPT for agent definition and tool catalog creation. The session also involved a live demonstration of creating a task management agent using No Code DB and Nen.
Details
- Introduction and Agent Design: Rahul Singh welcomed attendees and discussed the ongoing curriculum, emphasizing agent design over specific tools. He highlighted the shift towards focusing on agent specifications and definitions rather than getting bogged down in framework choices.
- Agent Tools and Frameworks: The session examined the use of 11 Labs and Nen for creating conversational agents. Rahul demonstrated a simple agent in 11 Labs that interacts with a database via API to provide moveout information. He also showed an agent in Nen for managing jobs in a database.
- Agent Specification and Definition: Rahul stressed the importance of drafting agent specs or definitions, arguing that frameworks become less relevant if an agent is well-designed. He introduced the concept of using Notepad for initial agent design, focusing on the agent's name, system prompt, and tools.
- Use Cases and Agent Abilities: The discussion explored various use cases for AI agents, including task management, information retrieval, and weekly digest generation. Rahul emphasized the importance of scoping agent abilities and using basic tools that can be effectively sequenced by the AI.
- Leveraging ChatGPT for Agent Definition: Rahul demonstrated using ChatGPT to assist in creating an agent definition or manifest. He provided ChatGPT with specific instructions and context to generate a structured agent outline, including system prompt, roles, objects, purposes, and a tools catalog.
- Live Demonstration of Agent Creation: Rahul initiated a live demonstration of creating a task management agent using No Code DB and Nen. He defined a job table in No Code DB and set up tools in Nen for getting and adding jobs. He then interacted with the agent to test its functionality and refine its behavior.
- Schema and Prompt Engineering: The importance of schema design and prompt engineering was highlighted. Rahul emphasized that the secret to getting good results from AI tools like ChatGPT lies in asking the right schema and providing detailed instructions.
- Tool Sequencing and AI Decision-Making: Rahul explained that AI can sequence basic tools effectively if properly defined. He demonstrated how the AI could break down a large task into smaller subtasks and manage them as separate jobs.
Suggested next steps
- Rahul Singh will continue to refine the task management agent, potentially adding features like user management and SMS notifications.
- Participants are encouraged to experiment with creating their own AI agents using 11 Labs, Nen, and other available tools.
- Participants are encouraged to explore using ChatGPT or other LLMs to assist in creating agent definitions and tool catalogs.