AgentInfra Radar
For AI security teams

Find agent, MCP, and LLM app projects before security review becomes urgent

AgentInfra Radar helps AI security companies turn public MCP, agent, and LLM infrastructure signals into reviewable account research. It does not claim purchase intent; it gives your team better context for deciding who is worth checking.

Pain points
New agent surfaces appear faster than security teams can map them manually.
  • MCP servers and tool integrations can introduce permissions, data access, and prompt-injection risks
  • Open-source agent projects may gain usage before governance, audit logs, or sandboxing are mature
  • Generic prospect lists rarely explain why a project is security-relevant
How AgentInfra Radar helps
We convert public-source project activity into a short list your security GTM or research team can review.
  • Segment projects by MCP, agent framework, AI coding, automation, and developer-tool categories
  • Summarize why a lead may need security review without using private data
  • Keep manual verification in the loop before outreach
Example lead type: MCP servers
Projects exposing tools, data, or workflow actions through MCP interfaces.
  • Useful for teams selling MCP security, policy enforcement, access review, or runtime controls
  • Review source links, source-check status, and the suggested angle before contacting
Example lead type: agent frameworks
Frameworks and runtimes used to build multi-step agent workflows.
  • Relevant when teams may need tracing, evals, guardrails, sandboxing, or approval flows
  • Fit scores help prioritize which projects deserve manual review first
Example lead type: AI coding agents
Coding agents and browser agents that interact with repositories, local environments, or authenticated web sessions.
  • Potential fit for code security, secret handling, audit logging, and permission governance
  • The reason a project may matter should be treated as a hypothesis, not a confirmed buying event
Respectful first-touch context
The suggested angle is meant to help a human write a relevant note, not automate fear-based outreach.
  • Start with the public project context
  • Ask whether the team is reviewing agent security risks
  • Avoid claiming you know their internal security posture

Using fit notes, scores, and suggested angles

These fields are designed to make security research easier to triage.

  • The fit note highlights a plausible security concern such as tool permissions, prompt injection, sandboxing, or auditability
  • The fit score ranks category fit, evidence clarity, and actionability so your team can review the strongest records first
  • The suggested angle gives a respectful conversation starter that should be personalized after manual review

Risk boundary

AgentInfra Radar is public-source research plus manual verification, not a private contact database.

  • No guaranteed leads, replies, customers, or security issues
  • No guessed personal emails or private inbox scraping
  • Manual verification is recommended before using a record in outbound or partnership research
Next step

Download an AI security sample

No form is required. Download a small AI security sample focused on MCP, agent runtime, AI coding, and governance signals.

Download AI security sample