AGENT DISCOVERY
Discover every AI agent in your environment
AI agents are spreading across applications, teams, and infrastructure. Eyrane helps security teams build an inventory of agents, models, tools, MCP servers, identities, and connected systems.
RELATIONSHIP FOUND
Finance Agentservice account → data warehouse → CRMAccess path mappedTHE DEFINITION
What is AI agent discovery?
AI agent discovery is the process of identifying AI agents running across an organization and mapping the systems, models, tools, identities, and data they interact with. It gives security teams an inventory of agentic systems so they can assess risk, permissions, and behavior.
- Identify known and unknown agents
- Inventory models and agent frameworks
- Map tools and MCP connections
- Identify credentials and identities in use
- Understand connected enterprise systems
- Find shadow or unmanaged AI deployments
THE INVENTORY GAP
AI agents are appearing faster than security inventories can keep up
Agents can be introduced through internal engineering teams, automation platforms, SaaS applications, AI assistants, low-code tools, scripts, experimental projects, third-party applications, and MCP servers.
SHADOW AI
Find agents security teams did not deploy
A team may connect an AI workflow to production data. An employee may deploy an automation agent. A SaaS product may introduce an embedded agent. An MCP connection may expose an internal tool.
Shadow agents may simply be unmanaged systems that security teams cannot yet assess.
DISCOVERY COVERAGE
Build an inventory of the agent ecosystem
Discovery is more useful when it shows the relationships around an agent, not just the agent name.
RELATIONSHIP GRAPH
An agent inventory needs a graph, not just a table
Agent risk lives in the connections: identity to permissions, tools to systems, MCP servers to downstream data.
ILLUSTRATIVE INVENTORY
See the context behind each agent
A useful inventory connects ownership, identity, model, tools, MCP, systems, and access context in one place.
| Agent | Owner | Identity | Model | Tools | MCP | Connected systems | Risk |
|---|---|---|---|---|---|---|---|
| Finance Agent | Finance Engineering | Service account | Model endpoint | 3 tools | 1 MCP server | Data warehouse + CRM | Elevated access |
| Support Agent | CX Platform | User delegated | Model endpoint | 2 tools | None | Ticketing + knowledge base | Moderate access |
DISCOVERY → RISK CONTEXT
Discovery is only useful when it explains risk
A list of agents is not enough. Security teams need to understand who owns the agent, what identity it uses, what systems it reaches, what permissions it inherits, what tools it can invoke, and what sensitive data it can access.
Understand agent accessOWNERSHIP
Know who owns every agent
Ownership gives security teams a path for remediation, approvals, policy exceptions, incident response, decommissioning, and lifecycle management.
MCP DISCOVERY
See where MCP expands the attack surface
MCP servers can expose tools and data to AI applications. Agent discovery should include the servers, exposed tools, connected agents, credentials, downstream systems, and external dependencies in that path.
Explore MCP SecurityCOMMON QUESTIONS
Questions security teams ask about AI agent discovery
What is AI agent discovery?
AI agent discovery is the process of identifying AI agents running across an organization and mapping the systems, models, tools, identities, and data they interact with.
Why do enterprises need an AI agent inventory?
Enterprises need an AI agent inventory because agents can be introduced through teams, applications, automation platforms, scripts, and MCP connections that traditional asset inventories may not capture.
What are shadow AI agents?
Shadow AI agents are AI systems operating without complete visibility, ownership, or policy context from the security team. They may simply be unmanaged systems that security teams cannot yet assess.
How can security teams discover AI agents?
Security teams can discover AI agents by looking across applications, infrastructure, automation tools, model endpoints, identities, tool calls, and MCP connections, then mapping the relationships between them.
What should an AI agent inventory include?
An AI agent inventory should include each agent, its owner, model, identity, permissions, tools, MCP connections, connected systems, data access, environment, and action context.
How is AI agent discovery different from AI model inventory?
AI model inventory tracks models and endpoints. AI agent discovery also maps the identities, tools, permissions, systems, data, and actions around the models.
Why should MCP servers be included in agent discovery?
MCP servers should be included because they can expose tools, data, and external actions that expand what an AI agent can reach.
How does agent discovery support AI security posture management?
Agent discovery gives AI security posture management the inventory and relationship context needed to assess what exists, what is managed, and where access or ownership is incomplete.
AI AGENT DISCOVERY
You cannot secure agents you cannot see.
Build visibility into the agents, identities, tools, and systems operating across your environment.