Fastn is a critical infrastructure provider in the AI agent stack, specifically addressing the 'tool-use' bottleneck. By acting as a Unified Command Layer (UCL), they provide the connective tissue that allows agents to interact with enterprise APIs without the developer needing to manage complex authentication or over-sized context windows. Their support for the Model Context Protocol (MCP) ensures they are compatible with the emerging standards of the ecosystem.
They matter to agent builders because they solve the reliability and security problems that typically prevent agents from being deployed in production. Through Adaptive Context, they make agents more cost-effective by minimizing token usage during tool selection, and through their governance features, they allow agents to operate within strict enterprise guardrails.
The current state of AI agents is defined by a mismatch between the reasoning capabilities of large language models and the rigid requirements of enterprise software. Most companies attempting to deploy agents in production run into a specific set of hurdles: the 'token tax' of massive API schemas, the security risk of giving an agent full system access, and the high latency of serial tool-calling. Fastn is an Austin-based infrastructure company that builds a middleware layer designed to solve these problems.
At the core of the platform is the Unified Command Layer (UCL), which functions as an intelligent gateway between an AI agent and the third-party tools it needs to operate. Rather than forcing a developer to hand-code every integration or trust an LLM to parse a massive documentation file, Fastn manages the schema and the connection. The platform supports the Model Context Protocol (MCP), allowing it to bridge the gap between various agent frameworks and hundreds of enterprise applications like Salesforce, Jira, and Slack.
One of the primary differentiators for Fastn is its Adaptive Context technology. In a typical agent setup, if an agent needs to use an API, the developer often provides the entire API schema in the prompt. This is expensive and inefficient. Fastn optimizes this by filtering schemas and tools based on the agent's intent. If an agent is tasked with updating a record in HubSpot, Fastn only provides the relevant portions of the HubSpot schema. This reduces the amount of text the agent has to process, which in turn lowers token costs and reduces the likelihood of hallucinations or errors.
The platform also implements Tool Composition, which is a performance optimization technique. If a task requires multiple tool calls—such as fetching a ticket from Jira and then posting a summary to Slack—Fastn can learn these frequent chains and batch them. This reduces the total round-trip time, making the agent feel more responsive to the end user. For enterprise developers, this is the difference between a prototype and a production-ready application.
While raw performance is important, Fastn's market position is built on the reality that enterprises will not grant AI agents unfettered access to their data. The platform provides a governance layer that includes role-based access control (RBAC) and prompt safety guardrails. This allows a SaaS company to embed Fastn and give its customers the ability to connect their own apps safely within the host application's environment.
Fastn raised a $2.6 million seed round led by LiveOak Ventures and Antler, with participation from investors including Netlify co-founder Chris Bach. Based in Austin, Texas, the company is positioning itself as a critical piece of the agentic stack. While traditional integration platforms were built for static, human-triggered automations, Fastn is built for a world where software systems interact with each other autonomously. It provides the necessary infrastructure to manage authentication, context, and security at scale, allowing developers to focus on the reasoning logic of their agents rather than the plumbing of API connections.
An intelligent gateway that streamlines context and tool chains for AI agents.
Fastn is hiring.