Aurimas Griciūnas

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𝗧𝗵𝗲 𝟯 𝗣𝗶𝗹𝗹𝗮𝗿𝘀 𝗼𝗳 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀: Why most prototypes fail in production 👇

Building a demo is easy. Building a system that doesn't hallucinate under pressure is where the real AI Engineering begins.

When we move from simple prompts to autonomous agents, we must architect for reliability. It is useful to think about this through three specific lenses:

𝟭. 𝗗𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗶𝘀𝘁𝗶𝗰 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 - You cannot rely on the LLM to police itself. You need hard-coded validation layers that check tool outputs before they reach the user.

𝟮. 𝗦𝘁𝗮𝘁𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 - Agents need to know where they are in a multi-step process. Using a persistent 'checkpoint' system allows for recovery when an API call times out.

𝟯. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗟𝗼𝗼𝗽𝘀 - If you aren't measuring your agent's success rate on a fixed set of tasks, you aren't engineering; you're just guessing.

❗️ Most developers spend too much time on the prompt and not enough time on the surrounding 'scaffolding.'

❗️ A reliable agent is 20% LLM and 80% traditional software engineering.

Learn how to build production-grade systems in my End-to-End AI Engineering Bootcamp: https://lnkd.in/dagWE5r3

And that is it! The difference between a toy and a tool is the architecture you build around the model.

What is the biggest bottleneck you've faced when moving agents to production? Let me know in the comments 👇

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