What does it actually take to build agentic AI applications that hold up in the real world? In this episode, Ashok sits down with Austin, founder of Focused, to share field stories and hard-won lessons from building AI systems that go beyond flashy demos. From legal assistants to government transparency tools, Austin breaks down the concrete criteria for identifying where AI makes sense — and where it doesn't.
They unpack how to find the right starting point for your first agentic app, why integration with legacy systems is the real hurdle, and the engineering must-haves that keep AI behavior safe and reliable. You'll hear practical guidance on designing eval frameworks, using abstraction layers like LangChain, and how observability can shape your development roadmap just like in traditional software. Whether you're a product leader or a CTO, this conversation will help you distinguish hype from real opportunity in AI.
Unlock the full potential of your product team with Integral's player coaches, experts in lean, human-centered design. Visit integral.io/convergence for a free Product Success Lab workshop to gain clarity and confidence in tackling any product design or engineering challenge.
Inside the episode...
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A practical checklist for identifying your first AI-powered app
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The hidden cost of "AI for AI's sake" and where traditional software is better
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Why repetitive knowledge work is prime territory for automation
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How Focused helped Hamlet build an AI for parsing government meeting data
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Where read-only data access gives you a safe starting point
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Why integration is often more complex than the AI itself
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The importance of eval frameworks and test-driven LLM development
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How to use observability to continuously improve AI agent behavior
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Speed vs. believability: surprising lessons from Groq-powered inference
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Using multiple models in one system and LLMs to QA each other
Mentioned in this episode
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Hamlet (government transparency startup) - https://www.myhamlet.com/?convergence
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LangChain - https://www.langchain.com/?convergence
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Claude (Anthropic) - https://claud.ai/?convergence
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Dspy Prompting framework - https://dspy.ai/?convergence
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Shopify AI memo (referenced) - https://convergence.fm/episode/shopifys-leaked-ai-mandate-explained-6-takeaways-for-your-product-team?convergence
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Amazon Bedrock / SageMaker - https://aws.amazon.com/bedrock/?convergence
Unlock the full potential of your product team with Integral's player coaches, experts in lean, human-centered design. Visit integral.io/convergence for a free Product Success Lab workshop to gain clarity and confidence in tackling any product design or engineering challenge.
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