Practical AI Full-stack approach for effective AI agents
39 snips
May 15, 2024 
In this engaging discussion, Josh Albrecht, CTO and co-founder of Imbue, shares insights on building more robust AI agents. He highlights the significant challenges in developing effective AI for enterprise, emphasizing the importance of domain expertise and high-quality data. Albrecht also discusses the role of graph databases in enhancing data modeling and the value of prototype testing for user interfaces. The conversation wraps up with a look into the future of AI and its transformative potential in the workplace.
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Agent Robustness is Key
- Current AI tools excel at initial versions of systems, achieving 60-70% accuracy.
- Reaching higher accuracy levels for deployment requires substantial effort, especially for general assistants.
Building Robust Agents
- Implement safeguards and guardrails, including domain-specific checks and LLM scoring, to improve agent robustness.
- Evaluate systems rigorously, defining success metrics, and checking for data drift.
Domain Expertise is Crucial for Agents
- Successful agent workflows are driven by domain experts who understand the nuances of their field.
- They leverage this expertise to address implementation details and improve agent performance.
