Tired of AI agents that forget context mid-conversation or drift subtly off course in production? You're not alone. In this episode, the AI, Actually crew unpacks six critical engineering principles for building reliable AI agents—principles that separate proof-of-concepts from production-ready systems.
Pete, Mike, Andy, and Stew break down insights from AI expert Nate B. Jones, translating technical concepts into business-focused guidance. They explore why AI memory isn't just about storage, how to bound uncertainty without killing creativity, and why monitoring AI systems requires a completely different approach than traditional software.
This episode covers:
This episode of AI, Actually centers around a video by @nate.b.jones about the 6 principles of AI Agents. That video can be watched in its entirety here: I've Built Over 100 AI Agents: Only 1% of Builders Know These 6 Principles
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Chapters:
00:00 Introduction to AI Agents and Engineering Principles
01:34 Introducing Nate B. Jones' AI Engineering Principles
03:03 Stateful Intelligence
10:16 Bounded Uncertainty
19:55 Intelligent Failure Detection
20:51 Evaluating LLM Responses
22:16 Monitoring Quality and Performance
23:53 Active Maintenance of LLM Systems
26:18 Understanding Subtle Failures
26:55 Capability-Based Routing
30:22 Aligning Models with Business Processes
33:41 Nuanced Health State Monitoring
37:36 Continuous Input Validation
41:36 Closing Thoughts
Keywords: AI agents, agentic AI, AI engineering, AI memory, stateful intelligence, AI monitoring, capability-based routing, AI evaluation, production AI, enterprise AI, AI agent development, LLM engineering, AI testing, AI agent failures, AI system monitoring
