Agent Based LLM Applications:
Separating the Hype from Practical Applications
Summary
Importance of Governance Controls and Decision-Making:
Establishing governance controls and a hierarchical structure is crucial when orchestrating AI agents. This ensures that AI systems operate within set boundaries and make decisions responsibly, avoiding misuse and errors.
Memory and Privacy in AI Agents:
Structuring memory and managing privacy are essential for effective AI agent operations. While agents need memory to perform their tasks efficiently, they must also respect user privacy and filter out sensitive information to prevent leaks and misuse.
Agent-Based Approaches for Data Enrichment and Retrieval:
Using agents to enrich sparse data and retrieve information can significantly enhance the quality and usability of data. Modular approaches to data augmentation, such as retrieval augmented generation (RAG), can streamline data processing and improve outcomes.
Application of AI in Process Automation:
AI can be utilized to reduce human intervention by automating complex processes, supporting decision-making, and orchestrating workflows. Proper configuration and continuous improvement in AI systems can lead to enhanced efficiency and operational effectiveness.
Integration and Collaboration Using AI Tools:
The use of AI-based agents and collaborative tools (like Arcana and Fizzban) can accelerate project development and enhance team communication and coordination, particularly in remote settings. These tools help integrate diverse expertise and manage tasks seamlessly.
Session Highlights
The Importance of Governance Controls in AI Agents: "The governance controls need to be very intentional, where you need to say, okay, what is this agent going to do? What privileges are we giving it? What are we expecting from it?"— Seth Earley
Encouraging Fast Innovation with Collaborative Channels: "So what we're trying to do there is really invent things, really get to a point where instead of it taking six months to be able to present your idea, you can get there in a couple of weeks because you're bouncing it off the right people, you're building prototypes, you're visualizing what it is that you're trying to do."— Alexander Kline
Reverse Prompting in Modern AI: "For example, in the case of a lending agent, we've configured it in a way to narrow some of the focus, retrieve some files, for example, that are local to the organization, like the dealership, and maybe, for example, how they do lending. And of course, seeding continuing conversations for the user."— Sanjay Mehta
Optimizing System Design with Multi-Agentic Workflows: "Oftentimes if you're trying to design a system that is becoming more and more complex, it's often better to go to a multi agentic workflow to split the problem into multiple different chunks that different agents could be better suited to."— Dominique Legualt