FirstOps is a critical piece of the infrastructure for enterprise agent adoption, focusing on the governance of agentic workflows. As agents transition from simple chatbots to autonomous actors with tool access, they create a blind spot for traditional security teams. FirstOps addresses this by providing a control plane that manages identity and maintains an audit trail of every action an agent takes, ensuring that autonomous actions are always linked back to a human principal.
The company is a significant proponent of the Model Context Protocol (MCP), acting as a secure gateway for MCP-based connections. By allowing agents to interact with corporate data through a brokered proxy, FirstOps enables companies to use agentic tools without exposing raw credentials. Their focus on the runtime level—intercepting bash commands, file I/O, and LLM calls—positions them as a necessary layer for organizations moving agents from experimental phases into production environments.
Most enterprise security models rely on the assumption that a human is the primary actor. When an employee uses a browser or a terminal, traditional identity and access management tracks that person's credentials. The rise of AI agents, particularly those capable of executing code and calling external APIs, breaks this model. FirstOps is a security and governance platform designed to bridge this gap by treating the agent runtime as a distinct, auditable layer of the corporate stack.
The company focuses on the agent runtime, which encompasses every action an agent takes between the initial prompt and the final completion. This includes the reasoning layer (LLM calls), the connection layer (Model Context Protocol or MCP), and the action layer (tool calls like bash or git). By positioning itself in the middle of these flows, FirstOps creates a control plane where security teams can define policies, redact sensitive data, and audit actions in real-time.
One of the primary hurdles for security software is developer friction. FirstOps addresses this by deploying via mobile device management (MDM) tools like Jamf, JumpCloud, and Intune. This approach allows the platform to intercept the activity of local coding agents, such as Cursor, Claude Code, and Aider, without requiring developers to change their code or configuration. For autonomous agents built on frameworks like LangGraph or AutoGen, the company provides a small SDK that writes to the same centralized audit log.
This deployment strategy is a clear attempt to solve the "shadow AI" problem. While developers are often the first to adopt agentic tools to increase productivity, security teams are frequently left in the dark about what these tools can actually do. By hooking into the machine level via MDM, FirstOps gives IT departments visibility into which tools an agent is invoking and which repositories it is accessing before a potential security incident occurs.
The Model Context Protocol (MCP) has quickly become a standard for how agents interact with external data sources like Notion, GitHub, and internal databases. FirstOps includes a dedicated MCP gateway that brokers these connections. Instead of giving an agent raw API tokens, which could be leaked or abused, the gateway manages credentials per request. This ensures that the agent never sees the actual secret, and the security team retains a granular record of every system the agent touches.
Beyond credential management, the platform inspects tool calls before they execute. If an agent attempts to run a destructive bash command or access a production database that is out of scope for the user's role, the policy engine can deny the request or flag it for human review. This shifts the security model from post-hoc log analysis to active prevention.
A nuanced feature of the FirstOps platform is its focus on "skills." In the context of agents, a skill is often a set of instructions or a prompt template that expands what the agent is capable of doing. FirstOps treats these as the equivalent of npm packages—external dependencies that can introduce vulnerabilities. The platform uses hash-based lineage to track every skill and subagent loaded into an agent's context across a tenant. This allows administrators to see if unverified or malicious instruction sets are being used by multiple agents across the organization.
Governance and security for the agent runtime.
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