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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.

Full Video Podcast

Flo’s talk at AI Engineer Summit

Chapters

* 00:00:00 Introductions

* 00:04:05 AI engineering and deterministic software

* 00:08:36 Lindys demo

* 00:13:21 Memory management in AI agents

* 00:18:48 Hierarchy and collaboration between Lindys

* 00:21:19 Vertical vs. horizontal AI tools

* 00:24:03 Community and user engagement strategies

* 00:26:16 Rickrolling incident with Lindy

* 00:28:12 Evals and quality control in AI systems

* 00:31:52 Model capabilities and their impact on Lindy

* 00:39:27 Competition and market positioning

* 00:42:40 Relationship between Factorio and business strategy

* 00:44:05 Remote work vs. in-person collaboration

* 00:49:03 Europe vs US Tech

* 00:58:59 Testing the Overton window and free speech

* 01:04:20 Balancing AI safety concerns with business innovation

Show Notes

* Lindy.ai

* Rick Rolling

* Flo on X

* TeamFlow

* Andrew Wilkinson

* Dust

* Poolside.ai

* SB1047

* Gathertown

* Sid Sijbrandij

* Matt Mullenweg

* Factorio

* Seeing Like a State

Transcript

Alessio [00:00:00 ]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.

Swyx [00:00:12 ]: Hey, and today we're joined in the studio by Florent Crivello. Welcome.

Flo [00:00:15 ]: Hey, yeah, thanks for having me.

Swyx [00:00:17 ]: Also known as Altimore. I always wanted to ask, what is Altimore?

Flo [00:00:21 ]: It was the name of my character when I was playing Dungeons & Dragons. Always. I was like 11 years old.

Swyx [00:00:26 ]: What was your classes?

Flo [00:00:27 ]: I was an elf. I was a magician elf.

Swyx [00:00:30 ]: Well, you're still spinning magic. Right now, you're a solo founder and CEO of Lindy.ai. What is Lindy?

Flo [00:00:36 ]: Yeah, we are a no-code platform letting you build your own AI agents easily. So you can think of we are to LangChain as Airtable is to MySQL. Like you can just pin up AI agents super easily by clicking around and no code required. You don't have to be an engineer and you can automate business workflows that you simply could not automate before in a few minutes.

Swyx [00:00:55 ]: You've been in our orbit a few times. I think you spoke at our Latent Space anniversary. You spoke at my summit, the first summit, which was a really good keynote. And most recently, like we actually already scheduled this podcast before this happened. But Andrew Wilkinson was like, I'm obsessed by Lindy. He's just created a whole bunch of agents. So basically, why are you blowing up?

Flo [00:01:16 ]: Well, thank you. I think we are having a little bit of a moment. I think it's a bit premature to say we're blowing up. But why are things going well? We revamped the product majorly. We called it Lindy 2.0. I would say we started working on that six months ago. We've actually not really announced it yet. It's just, I guess, I guess that's what we're doing now. And so we've basically been cooking for the last six months, like really rebuilding the product from scratch. I think I'll list you, actually, the last time you tried the product, it was still Lindy 1.0. Oh, yeah. If you log in now, the platform looks very different. There's like a ton more features. And I think one realization that we made, and I think a lot of folks in the agent space made the same realization, is that there is such a thing as too much of a good thing. I think many people,