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Is agent engineering the next big AI discipline or a repackaged buzzword? In this episode, we cut through the hype to explore what agent engineering really means for business leaders navigating AI adoption. From market growth and real-world impact to the critical role of AI memory and the evolving tool landscape, we provide a clear-eyed view to help you make strategic decisions.

In this episode:

- The paradox of booming agent engineering markets despite high AI failure rates

- Why agent engineering is emerging now and what business problems it solves

- The essential role of AI memory systems and knowledge graphs for real impact

- Comparing agent engineering frameworks and when to hire agent engineers vs ML engineers

- Real-world success stories and measurable business payoffs

- Risks, challenges, and open problems leaders must manage

Key tools and technologies mentioned: LangChain, LangMem, Mem0, Zep, Memobase, Microsoft AutoGen, Semantic Kernel, CrewAI, OpenAI GPT-4, Anthropic Claude, Google Gemini, Pinecone, Weaviate, Chroma, DeepEval, LangSmith

Timestamps:

00:00 – Introduction & Why Agent Engineering Matters

03:45 – Market Overview & The Paradox of AI Agent Performance

07:30 – Why Now: Technology and Talent Trends Driving Adoption

11:15 – The Big Picture: Managing AI Unpredictability

14:00 – The Memory Imperative: Transforming AI Agents

17:00 – Knowledge Graphs & Domain Expertise

19:30 – Framework Landscape & When to Hire Agent Engineers

22:45 – How Agent Engineering Works: A Simplified View

26:00 – Real-World Payoffs & Business Impact

29:15 – Reality Check: Risks and Limitations

32:30 – Agent Engineering In the Wild: Industry Use Cases

35:00 – Tech Battle: Agent Engineers vs ML Engineers

38:00 – Toolbox for Leaders: Strategic Considerations

41:00 – Book Spotlight & Sponsor Message

43:00 – Open Problems & Future Outlook

45:00 – Final Words & Closing Remarks

Resources:


Thanks for tuning into Memriq Inference Digest - Engineering Edition. Stay curious and keep building!

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22 episodes