Alessio will be at AWS re:Invent next week and hosting a casual coffee meetup on Wednesday, RSVP here! And subscribe to our calendar for our Singapore, NeurIPS, and all upcoming meetups! We are still taking questions for our next big recap episode! Submit questions and messages on Speakpipe here for a chance to appear on the show! If you've been following the AI agents space, you have heard of Lindy AI; while founder Flo Crivello is hesitant to call it "blowing up," when folks like Andrew Wilkinson start obsessing over your product, you're definitely onto something. In our latest episode, Flo walked us through Lindy's evolution from late 2022 to now, revealing some design choices about agent platform design that go against conventional wisdom in the space. The Great Reset: From Text Fields to Rails Remember late 2022? Everyone was "LLM-pilled," believing that if you just gave a language model enough context and tools, it could do anything. Lindy 1.0 followed this pattern: * Big prompt field ✅ * Bunch of tools ✅ * Prayer to the LLM gods ✅ Fast forward to today, and Lindy 2.0 looks radically different. As Flo put it (~17:00 in the episode): "The more you can put your agent on rails, one, the more reliable it's going to be, obviously, but two, it's also going to be easier to use for the user." Instead of a giant, intimidating text field, users now build workflows visually: * Trigger (e.g., "Zendesk ticket received") * Required actions (e.g., "Check knowledge base") * Response generation This isn't just a UI change - it's a fundamental rethinking of how to make AI agents reliable. As Swyx noted during our discussion: "Put Shoggoth in a box and make it a very small, minimal viable box. Everything else should be traditional if-this-then-that software." The Surprising Truth About Model Limitations Here's something that might shock folks building in the space: with Claude 3.5 Sonnet, the model is no longer the bottleneck. Flo's exact words (~31:00): "It is actually shocking the extent to which the model is no longer the limit. It was the limit a year ago. It was too expensive. The context window was too small." Some context: Lindy started when context windows were 4K tokens. Today, their system prompt alone is larger than that. But what's really interesting is what this means for platform builders: * Raw capabilities aren't the constraint anymore * Integration quality matters more than model performance * User experience and workflow design are the new bottlenecks The Search Engine Parallel: Why Horizontal Platforms Might Win One of the spiciest takes from our conversation was Flo's thesis on horizontal vs. vertical agent platforms. He draws a fascinating parallel to search engines (~56:00): "I find it surprising the extent to which a horizontal search engine has won... You go through Google to search Reddit. You go through Google to search Wikipedia... search in each vertical has more in common with search than it does with each vertical." His argument: agent platforms might follow the same pattern because: * Agents across verticals share more commonalities than differences * There's value in having agents that can work together under one roof * The R&D cost of getting agents right is better amortized across use cases This might explain why we're seeing early vertical AI companies starting to expand horizontally. The core agent capabilities - reliability, context management, tool integration - are universal needs. What This Means for Builders If you're building in the AI agents space, here are the key takeaways: * Constrain First: Rather than maximizing capabilities, focus on reliable execution within narrow bounds * Integration Quality Matters: With model capabilities plateauing, your competitive advantage lies in how well you integrate with existing tools * Memory Management is Key: Flo revealed they actively prune agent memories - even with larger context windows, not all memories are useful * Design for Discovery: Lindy's visual workflow builder shows how important interface design is for adoption The Meta Layer There's a broader lesson here about AI product development. Just as Lindy evolved from "give the LLM everything" to "constrain intelligently," we might see similar evolution across the AI tooling space. The winners might not be those with the most powerful models, but those who best understand how to package AI capabilities in ways that solve real problems reliably.