Majority of “AI Agents” advertised today aren’t really agents at all. They’re 𝐢𝐧𝐭𝐫𝐢𝐜𝐚𝐭𝐞 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 𝐰𝐢𝐭𝐡 𝐝𝐞𝐭𝐞𝐫𝐦𝐢𝐧𝐢𝐬𝐭𝐢𝐜 𝐜𝐨𝐧𝐭𝐫𝐨𝐥 𝐟𝐥𝐨𝐰𝐬, where LLMs are used only for single-prompt, narrow tasks in isolation. Ironically, those who jumped on LLMs early and built these intricate pipelines are now the ones facing the 𝐠𝐫𝐞𝐚𝐭𝐞𝐬𝐭 𝐢𝐧𝐞𝐫𝐭𝐢𝐚 𝐭𝐨 𝐬𝐰𝐢𝐭𝐜𝐡 𝐭𝐨 𝐟𝐮𝐥𝐥 𝐚𝐠𝐞𝐧𝐭𝐬. I saw this phenomenon firsthand with the self-driving car industry: companies spent years building brittle, rule-based systems, patching edge cases one by one, reluctant to invest in end-to-end training. Only Tesla committed fully to this switch early on – and now they're the only ones with a truly scalable system adaptable to almost anywhere. Few companies running LLMs in production are ready to switch to real agents. It's risky – all those edge cases you spent months tuning could suddenly break. Your metrics might regress. Your system might behave in ways you didn't expect. That fear is real. You know you have to make the switch eventually, but before you know it, you’re stuck maintaining a 𝐟𝐫𝐚𝐠𝐢𝐥𝐞, 𝐮𝐧𝐬𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐬𝐭𝐚𝐜𝐤 that’s harder and harder to adapt as use cases grow more demanding. Meanwhile, O1-level reasoning models are becoming faster, cheaper, and more powerful by the day. 𝐒𝐨, 𝐬𝐡𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐬𝐰𝐢𝐭𝐜𝐡 𝐭𝐨 𝐚 𝐦𝐨𝐫𝐞 𝐚𝐠𝐞𝐧𝐭𝐢𝐜 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰? Yes—but you can't do it overnight. Building systems that are both autonomous and reliable takes time. You need: - 𝐂𝐥𝐞𝐚𝐫 𝐛𝐨𝐮𝐧𝐝𝐚𝐫𝐢𝐞𝐬 for what your agent can and can't do - 𝐅𝐚𝐢𝐥-𝐬𝐚𝐟𝐞𝐬 that kick in when things go wrong - 𝐑𝐨𝐛𝐮𝐬𝐭 𝐯𝐞𝐫𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 to catch problems early Then you can gradually expand which tasks you trust to your agent – similar to how self-driving cars expanded their 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐃𝐞𝐬𝐢𝐠𝐧 𝐃𝐨𝐦𝐚𝐢𝐧𝐬 (𝐎𝐃𝐃𝐬). The key is to start now. The longer you wait, the harder it gets to change. If you begin experimenting with agents today, you'll be ready when powerful reasoning LLMs become the standard. Don't let today's comfort become tomorrow's constraint!

How to choose the right agent framework? We built the same Agentic Finance App using LangGraph, CrewAI, and OpenAI Swarm and here's our recommendation. Deep-dive blog: relari.ai/blog/ai-agent-fram… @langchain @crewAIInc @OpenAI @RelariAI

Join me at this webinar next week! lu.ma/u5sgzulm?tk=Nmypxz @qdrant_engine @RelariAI

I was inspired by the SF breakfasts hosted by @Inferless_ and started organizing "GenAI Developer Breakfast" at Harvard Square. I find these small-group conversations super fun and insightful. Thank you everyone for who came to exchange learnings on LLMs today!

Giving a talk on synthetic data using @RelariAI tomorrow alongside folks from @milvusio, @TectonAI, and @bentomlai. Come join us at Github's HQ: lu.ma/cw8q5dhx

Curious about generating synthetic data for RAG and AI agents? Check out the latest blog from us @AntonanteP @RelariAI link.medium.com/lFXT4ejhpJb

If you missed @AntonanteP's talk @_odsc last week on 3 pillars of good LLM evaluation, please DM us and we are happy to share the slides!

@dflieb was instrumental in @RelariAI's journey at YC! Glad he will be able to mentor more YC startups now as a Group Partner!

Our customers are using Relari's "simulated user feedback" to rapidly iterate on LLM products. Take a look at how it works!