# Bring every AI agent under control > Automatically register AI agents, monitor their activity, and maintain a complete inventory of agent deployments across your organisation. **Source:** https://deeploy.ai/product/agent-governance/ **About this file:** Machine-readable markdown version of the page above, for LLMs and AI assistants. Regenerated automatically whenever the page is updated. AI agents are being deployed faster than governance teams can track them. Deeploy’s MCP Server gets every AI agent in an organisation to register itself, ship its traces, and live under the same controls as the rest of your AI estate. ## The AI agent explosion is creating a governance gap By 2028, an estimated 1.3 billion AI agents could be operating autonomously across cloud environments. As adoption accelerates, governance teams are struggling to maintain visibility into which agents are running, who owns them, and how they are being used. Keeping up often means spreadsheets, manual registration processes, and constant follow-ups with teams. The result is wasted effort, incomplete inventories, and AI agents operating outside governance controls. - ✗ Incomplete AI inventories - ✗ Time wasted on manual registration and tracking - ✗ Limited visibility into agent activity and ownership - ✗ Missing audit trails for investigations & compliance reviews ## Make AI agent governance automatic Deeploy's **MCP Server** makes registration, inventory management, and observability a natural part of how AI agents operate. Governance cannot start without knowing an agent exists. When an MCP-compatible agent connects to Deeploy, it can register itself directly in your AI registry. Agent identity, ownership, source platform, and metadata are captured automatically without requiring separate governance workflows. **Outcome:** Every agent becomes visible to governance teams from the moment they're created. Most AI inventories become outdated the moment they are created. The MCP Server maintains a living inventory of AI agents by allowing teams to reuse existing registrations across sessions. This prevents duplicate records and keeps activity linked to the correct agent over time. **Outcome:** One source of truth for AI agents across Claude, Microsoft Copilot Studio, GitHub Copilot, and other MCP-compatible platforms. Registration is only the first step. Governance also requires visibility into how agents behave. Once registered, agents automatically send structured traces to Deeploy. Governance teams can review full inputs and outputs, interaction types, timestamps, session context, and custom metadata from a centralised interface. **Outcome:** Faster investigations, stronger oversight, and evidence for audits. Registration is only the first step. Once registered, agents can be linked to risk assessments, approval workflows, controls, and compliance processes within Deeploy. Manage AI agents alongside models and applications using the same governance framework, documentation requirements, and oversight processes. **Outcome:** Every registered agent becomes part of your existing governance workflows. ## The Deeploy MCP Server in action The Deeploy MCP Server is a Model Context Protocol server that any compliant agent platform (Claude, Microsoft Copilot Studio, GitHub Copilot, and any other MCP-aware host) can connect to. Paired with a Deeploy Skill that instructs the agent on how to behave, it does four things, every session: **Registers the agent in Deeploy** Once MCP is configured, Deeploy prompts you to register as a new agent or reuse an existing one. Authenticate with your Deeploy credentials and the agent's identity, owner, platform of origin, and metadata land in your AI registry. **Prevents duplicate registrations** Reusing an existing registration ensures that all traces from the same agentic use case stay together, making audits faster and more complete. **Patches deployments when reality changes** A renamed owner, a corrected description, a tag fix; the MCP server applies updates without breaking the audit trail. **Sends traces into Deeploy** Every interaction the agent has is captured as a structured trace and centralised in one place, ready to search, alert on, and audit. Learn more about Deeploy's [MCP Server](https://deeploy.ai/mcp-server-for-ai-agent-governance/). ## Frequently asked questions ### What is an MCP server for AI agent governance? A **Model Context Protocol** (MCP) server provides a standardised way for AI agents to connect with governance systems. Deeploy's MCP Server allows agents to register themselves, send traces, and become part of an organisation's AI governance process. ### Why is agent registration important for AI governance? Registration is a prerequisite for governance. An agent that is not registered cannot be reliably monitored, audited, or included in governance processes. By making registration part of every session, Deeploy helps organisations maintain visibility into their AI estate. ### How does Deeploy register AI agents? When an MCP-compatible agent starts a session, it can register itself in Deeploy using a new or existing registration. The registration captures the agent's identity, source platform, ownership information, description, and custom metadata. ### What information is stored in an AI agent registration? An agent registration can include: - Agent name and description - Source platform (Claude, Microsoft Copilot Studio, GitHub Copilot, etc.) - Agent endpoint or URL - Owner and team information - Version information - Capabilities and custom metadata ### How does Deeploy prevent duplicate agent registrations? Agents can reuse an existing registration across sessions. This ensures that traces, metadata, and audit records remain associated with the same agent instead of creating duplicate entries. ### What is AI agent tracing? AI agent tracing captures an audit trail of agent activity. Deeploy automatically records traces for registered agents throughout a session, creating a searchable history of interactions. Each trace can contain: - Full input and output text - Interaction type - Timestamp - Environment, model, and user context - Custom metadata provided by the agent ### Which AI agent platforms are supported? The Deeploy MCP Server works with MCP-compatible platforms, including Claude, Microsoft Copilot Studio, GitHub Copilot, and other tools that support the Model Context Protocol. ### How does the Deeploy MCP Server fit into an existing AI governance stack? The MCP Server extends Deeploy's governance capabilities to AI agents. Registered agents become part of the same inventory, monitoring, and audit framework used for AI models, applications, and other governed AI systems. --- **Get in touch:** You can book a demo at https://deeploy.ai/book-demo/ or reach the team via https://deeploy.ai/contact/.