Evaluating Multimodal AI Agents: Router, Skills, & Memory
Join me, Aparta, Co-Founder of Arise, as we dive into the world of AI agents and assistance, exploring the importance of evaluating these multimodal agents in production to ensure they actually work in the real world.
- 1. Topic of discussion: Evaluating AI agents and assistants in real-world production.
- 2. Importance of monitoring agent performance to ensure functionality.
- 3. Speaker introduction: Aparna, founder of Arise.
- 4. Current trend: Voice AI taking over call centers with real-time voice APIs.
- 5. Travel agent example - Price Line Pennybot, a hands-free voice-activated application for booking vacations.
- 6. Shift from text-based agents to multimodal agents (voice and text).
- 7. Components of an AI agent:
- a. Router: Decides the next step for an agent.
- b. Skills: Logical chains that perform tasks.
- c. Memory: Stores information for context in conversations.
- 8. Routers act as a "boss," determining which skill to call based on user queries.
- 9. Importance of routers correctly calling the right skill and passing correct parameters.
- 10. Skills can have multiple Llm evaluators or code-based evaluations.
- 11. Convergence: Ensuring reliability in the number of steps an agent takes to complete a task.
- 12. Voice applications require evaluation beyond just text, including audio quality and user sentiment.
- 13. Arise's AI agent evaluation process for their co-pilot feature.
- 14. Evaluations include checking correctness at various levels: overall response, router call, arguments, and task completion.
- 15. Evals should be present throughout the application flow for debugging purposes.
- 16. Importance of understanding how to evaluate AI agents in leadership roles.
- 17. Arise recently announced their Series C funding round.
- 18. Use of various agent frameworks (e.g., LAN graph, CREI, or LLM index workflow) and their common patterns.
- 19. Memory component stores context for multi-turn conversations.
- 20. Real-world example using an open-source project called "traces" to understand the inner workings of an agent.
- 21. Importance of evaluating routers, skills, and memory components for effective AI agents.
- 22. Advantages of voice applications in call centers and their impact on customer service.
- 23. Monitoring and evaluation are crucial for deploying successful AI agents and assistants.
- 24. Encouragement to consider voice applications as a future trend in complex software development.
Source: AI Engineer via YouTube
❓ What do you think? What are your thoughts on the ideas shared in this video? Feel free to share your thoughts in the comments!