Welcome to episode 23 of the HockeyStick podcast, where we dive into groundbreaking advancements in technology, business, and performance. In this episode, host Miko Pawlikowski sits down with Ravi Ramachandran and Dave Brewster, two-thirds of the co-founder team at Eidolon AI, to discuss the intricacies of AI agents and their potential to transform industries.
What is an AI Agent?
Kicking off the discussion, Miko asks, "What the hell is an AI agent?" Dave Brewster elucidates that an AI agent is essentially the smallest atomic unit capable of providing an autonomous answer. These agents can range from complex systems that debug code to simple tools that search documents. At the heart of their definition lies the element of autonomy—a pivotal characteristic that distinguishes these agents.
The Trust Issue
Miko raises an important concern about trust, given that even advanced AI models like ChatGPT sometimes "make things up." Dave explains that trustworthy AI agents need built-in fault tolerance. He points to Claude as an example of an AI that internally checks its processes, ensuring reliability. For enterprises, trust also extends to data governance, ensuring that sensitive information remains secure.
Market Maturity and Observability
Ravi sheds light on market maturity, explaining that apprehensions often stem from the evolving nature of technology. Miko elaborates on the limitations he faces as a software engineer, particularly the limited observability within LLM (Large Language Models). Dave acknowledges this challenge, emphasizing the need for multi-query, fault-tolerant systems that can better manage and debug AI outputs.
Building Practical AI Agents
When asked about their unique selling proposition at Eidolon AI, Ravi and Dave stress the importance of practical deployment. They describe their AI agent server, which focuses on easy integration and management using Kubernetes. By leveraging Kubernetes, the team ensures scalability, security, and seamless integration with existing infrastructures.
Real-World Applications
Miko presents a hypothetical yet practical use case: an AI agent that could analyze Slack history to gauge the need for a coffee break, estimate work hours, and notify his wife via text. Dave responds affirmatively but notes that while they don't have a Slack loader or text capability, integrating services like Twilio would be straightforward.
Production Use Cases
Dave shares real-world applications where AI agents have already made a significant impact, such as acting as API gateways to simplify calling multiple endpoints or using AI to detect fraudulent insurance claims. These use cases highlight the versatility and potential of AI agents in day-to-day operations and specific enterprise needs.
The Future of AI: Memory and Beyond
Dave and Ravi delve into the future, pointing to the compelling concept of adding memory to AI agents. Memory would allow agents to learn and adjust based on past interactions, dramatically enhancing their utility. This advancement could revolutionize everything from personal productivity tools to complex enterprise solutions.
Ethical Considerations
The conversation takes a philosophical turn as Miko and Dave discuss the ethical implications of AI and potential misuse. Dave stresses that while AI can enhance productivity, it's crucial to use these advancements responsibly to avoid dystopian outcomes.
Open Source and Community Engagement
Ravi emphasizes Eidolon's commitment to making AI accessible to everyone. This open-source philosophy extends to their working structure, where daily standups are open to public participation, fostering a transparent and collaborative environment.
Conclusion
In this enlightening episode, Ravi and Dave offer a nuanced perspective on the current state and future of AI agents. As they continue to push the boundaries of what's possible, their work at Eidolon AI stands as a testament to innovation, collaboration, and responsible technology development.
If you're intrigued by the possibilities AI agents present and how they can revolutionize both your personal and professional life, tune in to the full episode of the HockeyStick podcast. You won't want to miss the insights from these pioneers in the AI industry.
0:00 Introduction to AI Agents
0:38 Meet the Founders of Eidolon AI
0:59 Defining AI Agents
2:05 Challenges and Trust in AI Agents
7:54 Eidolon AI's Unique Approach
8:42 Building and Deploying AI Agents
16:54 Use Cases and Practical Applications
25:31 Autonomous LLM Use Cases in Insurance
26:49 Future of LLMs: Memory and Context
27:17 Challenges and Opportunities with LLM Toolkits
29:04 Integrating Memory into LLM Systems
30:40 Real-World Applications and Ethical Concerns
32:49 Advancements in LLM Memory Research
34:59 Future Breakthroughs and Practical Implementations
43:25 Building Open and Accessible AI
46:15 Conclusion and Final Thoughts
The real benefits of AI agents, with Dave Brewster & Ravi Ramachandran - HS#23