The interoperability problem in agentic workflows

The development of AI agents has historically been plagued by a fragmentation problem. Every time a developer wants to give a large language model (LLM) access to a new tool, database, or API, they typically have to write bespoke integration code. This creates a high "integration tax" that slows down development and makes agents fragile when switching between different models or providers. The Model Context Protocol (MCP) is the industry's response to this, aiming to standardize the interface between models and tools. Manufact is the technical plumber of this new standard.

Manufact provides the core infrastructure needed to make MCP functional for developers. Their primary offering is mcp-use, an open-source library that serves as the connective tissue between the reasoning engine of an LLM and the functional capabilities of an MCP server. By using this library, developers can avoid the trap of closed-source application clients. Instead, they can build custom agents that maintain tool access regardless of whether they are using an OpenAI, Anthropic, or open-source model.

Infrastructure for a standardized future

The company operates with a clear focus on the developer experience. They offer SDKs in both Python and TypeScript, ensuring that the two most common languages in the AI stack are supported. This dual-language support is crucial for adoption, as it allows both data scientists and web engineers to integrate MCP-compliant tools into their existing workflows. The mcp-use library handles the complex negotiation of tool definitions and execution loops, allowing the developer to focus on the agent's logic rather than the underlying transport layer.

Beyond the open-source library, Manufact maintains a platform at manufact.com designed for the building and deployment of these agents. This creates a path for engineering teams to move from local experimentation with MCP servers to production-ready deployments. The platform likely acts as a managed environment for the execution of agentic loops, providing the stability and observability that home-grown scripts often lack.

Competitive position in the agent stack

Manufact is betting on the long-term success of open standards. While large labs like Anthropic have championed MCP, the actual implementation of that protocol into a working agent remains a non-trivial task for most teams. Manufact fills this gap. They sit in the orchestration and integration layer of the agent stack, competing indirectly with high-level agent frameworks by offering a more modular and protocol-first alternative.

In a market where many companies are building "wrappers" or verticalized agent solutions, Manufact is building the glue. Their success is tied to the adoption of MCP as the dominant protocol for tool-use. If the industry continues to move toward standardization, Manufact is positioned as a critical utility for any developer building agents that need to do more than just generate text. They are turning the theoretical promise of a "universal tool interface" into a practical reality for engineers.