Composio's Self-Evolving Agents Tackle AI's Tooling Bottleneck

AI agents, despite their rapid advancements, often stumble where human intuition excels: learning from experience and adapting to complex system nuances. This fundamental challenge is precisely what Composio, a company recently announcing its Series A funding, aims to solve. Karan Vaidya and Soham Ganatra, co-founders of Composio, recently joined Alessio Fanelli and Swyx on the Latent Space podcast to discuss their vision for "self-evolving skills" that empower AI agents to interact seamlessly and reliably with a vast ecosystem of applications.

Composio positions itself as the crucial infrastructure layer for AI agents, managing the intricate details of authentication and user accounts for over 15,000 tools. As Karan Vaidya explained, their platform provides "agent-friendly skills which can be anything from like a direct API call to a somewhat more complex like directly agent giving natural language tasks to an app." This liberates developers from the cumbersome task of handling integration specifics, allowing them to focus on agent logic.

The founders highlighted a critical gap in current AI tooling protocols, such as OpenAI's Function Calling (often referred to as MCP). While effective for universal clients like ChatGPT, these protocols often fall short for developers building on top of them. Soham Ganatra articulated this by noting that for builders, the "DX [developer experience] of creating on top of MCP is somewhat difficult." This difficulty stems from limitations in customizing tool descriptions and handling large, complex responses, which can lead to "context overflows" and potential failures in production scenarios.

Composio addresses these pain points by prioritizing the client-side developer experience and, more importantly, by building an overarching "learning infrastructure." This infrastructure allows agents to collectively benefit from discovered edge cases and usage patterns. If one agent encounters an issue while integrating with Salesforce, for example, "other agents also benefit from that," improving overall reliability. The idea is that "the skills evolve over time," with Composio analyzing usage patterns to enhance agent reliability.

A striking revelation from Soham Ganatra underscored the maturity of Composio's approach: "95% of all our integrations are completely built using agents, maintained using agents." This demonstrates a profound commitment to autonomous development and maintenance, allowing Composio to rapidly scale its tool integrations. The company currently supports over 500 applications, with plans to reach 5,000 by year-end, adding "close to 100 a week" in the coming months. This aggressive growth is fueled by their agent-driven development model, which tackles the often-poor documentation and complex dev environments of enterprise tools.

The conversation also delved into the future of tool calling, moving beyond simple function execution. Composio is optimizing its tools for "code act," where AI agents generate code to interact with applications directly. This enables a single natural language instruction to trigger complex multi-step actions across various tools. Furthermore, Composio employs A/B testing and internal benchmarks to refine how agents utilize tools, ensuring optimal performance and adaptability. Their dynamic tool exposure mechanism allows them to present different tool sets to agents based on the specific query, further enhancing efficiency and accuracy.

This commitment to self-correction and continuous improvement, driven by agent feedback and internal evaluations, positions Composio at the forefront of the evolving AI agent landscape. It's not just about connecting tools; it's about making those connections smarter, more reliable, and ultimately, self-improving.