Your Agent's Self-Improving Swiss Army Knife: Composio CTO Karan Vaidya on Building Smart Tools

**Nathan Labenz** (0:00)
Hello, and welcome back to The Cognitive Revolution. Today my guest is Karan Vaidya, CTO of Composio, a platform that allows AI agents to access more than 50,000 tools, spanning more than 1,000 apps, all through a single interface, and which is one of the best examples of the smart tool pattern that I've been watching out for since the MCP paradigm was introduced. This is a sponsored episode, but as always, I have played around with the tool for the last couple of weeks, and it's clear to me that Composio does address several real problems. For starters, core platforms like Gmail, Google Drive, and Slack do not make it easy for do-it-yourselfers to grant access to AI agents. The number of clicks required to get started is a serious barrier for casual users. Most other tools are simpler to connect, but many aren't popular or well documented enough for AIs to know how best to use them from the start, and sometimes quite a bit of iteration is required to get things working well. Looking ahead, as people delegate larger projects to their agents, those agents will often need tools that the humans never anticipated. All of this is indeed made much easier by simply giving your agent access to Composio, which allows agents to express high-level intent, identifies the right tools for the job, and provides authentication, execution sandboxes, and logging infrastructure that few developers really want to build on their own.
In this conversation, we get into the details of how Composio works, and how they're delivering on the smart tool promise by using an AI-powered continuous improvement process that can detect when a tool isn't working for an agent, generate a new version in real-time, and then swap the upgrade into the agent's context. And which, over time in the background, automatically identifies and diffuses successful patterns across the entire Composio customer base.
One of the most interesting arguments that Karan makes is that excellence in tooling and increasingly in skills can help developers avoid model lock-in. The idea is that while models do have different default behaviors, they are all very good at following instructions. Which means that if you have very thorough instructions, you can probably get similar outputs from any frontier model. And for cases where that doesn't work, Karan and team are also working on meta skills, which translate skills from one provider to another, reducing switching costs even further. Beyond that, we also hear about Karan's favorite agent use cases, which notably look more like full jobs than discrete tasks. His perspective on which technology companies are gaining strength from the AI wave, which are most threatened, and how sticky agent products like Intercom's FIN will prove to be over time. His thoughts on memory platforms, payment frameworks, and other tools built specifically for AI agents. And how Composio works today, which includes individual engineers who manage tens of AI agents. And for the team that manages Composio's own agentic pipeline, a token bill that exceeds human payroll. For me, this conversation couldn't be more timely. Over the last couple of months, I've put in the work to curate the context that Claude Code needs to serve as a second brain and a capable assistant. And it's become my go-to interface for just about everything that I do on a computer. The next level up will be to get agents doing large-scale projects autonomously on my behalf. And as I enter this next phase, Composio will definitely be a part of my stack. I'll report back on how I'm doing, of course. But for now, I hope you enjoy this conversation about building smart tools for AI agents. With Karan Vaidya, CTO of Composio. The Cognitive Revolution is brought to you in part by Google, makers of the Gemini family of models. Anyone who's followed the show for a while knows that we've been sporadic, at best, when it comes to posting clips on social media. The hit rate of AI clip makers, in my experience, isn't super high, either because the segments they highlight are kind of beside the point, or because the edits end up awkward and are too much trouble to fix. I think a big part of the reason those problems happen is that most models simply cannot watch the full video. And this is why I'm now sending full videos of the podcast directly to Gemini 3.1 Pro and asking it to select the best clip-worthy moments, with full awareness of everything that happened in the entire conversation. Gemini is the only Frontier Model API that even accepts video inputs right now, so there is literally no other product that supports this workflow. And it doesn't stop there either. Once we select clip candidates, I apply a layer of agentic editing, and then send the rendered clips back to Gemini for a 13-point evaluation. With its scores, I'm able to structure a mini social media campaign where I'm working with only genuinely good clips from the start. Honestly, it's getting so easy, you can do it for just about anything. Internal company meetings, long presentations, or even home videos. Visit Google's AI Studio to explore Gemini's native video understanding. Thank you to Google for supporting The Cognitive Revolution. And now, on with the show. Karan Vaidya, CTO at Composio. Welcome to The Cognitive Revolution.