Build or Buy: Adding AI Agent Visibility to Your Endpoint Security Product

Most endpoint security vendors will get agent visibility to customers faster by licensing an agent-aware layer and shipping it under their own brand. The exception is a vendor that sees agent security as core to its differentiation for years and can staff it long term. Your sensor already sees processes, files, and network connections. What it is missing is agent context: knowing which process is an AI agent, which session and user it belongs to, and what a tool call or MCP connection means. That is the part you are deciding whether to build.

What customers are asking for

When customers ask you to "show what AI agents are doing," they usually mean:

An inventory. Which agents run on each machine, including Claude Code, Cursor, Codex, custom agents, and agents nobody approved, with versions and users.
A record of actions. Per session: files read and written, commands run, tool and MCP calls, and requests to model providers.
Rules. Which agents, MCP servers, and destinations are allowed, applied on the machine.
Evidence. Something they can hand to auditors that ties back to real sessions.
Fewer false alarms. Sophos found that coding agents like Claude Code, Cursor, and Codex regularly trigger endpoint rules built to catch attackers, mostly for credential access. Customers want to tell normal agent work from real problems.

What it takes to build

Agent detection. Recognizing agents by behavior, not a list of binaries, and keeping up as new agents and harnesses appear.
Session attribution. Tying every process, sub-agent, and child process back to the agent, session, and prompt that started it.
Understanding agent traffic. Parsing tool calls, MCP connections, and LLM API requests, including request bodies before encryption.
Decisions at the point of action. Allowing, modifying, or blocking an action before it completes, in a way the agent can handle without breaking the developer's task.
Coverage. macOS, Windows, and Linux, and often servers, containers, and CI runners too, since customers also run agents there.
Upkeep. Agents, harnesses, and MCP tooling change monthly.

When building makes sense

Agent security is central to how you plan to differentiate for the next several years.
You can dedicate a team to it long term, not for one release.
Owning every layer of the technology matters for your platform or your valuation.

When licensing makes sense

Customers are asking now, and this is one item on a long roadmap.
You would rather spend your team on the console, detections, and response workflows customers buy you for.
You want one integration that covers both employee endpoints and hosted agents.

Security vendors have often bought AI security capabilities rather than built them. SentinelOne acquired Prompt Security, and Palo Alto Networks acquired Protect AI. Licensing is the lighter version of the same decision.

What to look for in a layer you license

Works with any agent, with no changes to the agent or to how developers work.
Covers macOS, Windows, and Linux, plus servers and containers.
Can be white-labeled: your name on the binary, the service, and every user-facing string.
Keeps data in your infrastructure and your customers' environments.
Clean integration points: an event stream, hooks at decision points, a policy API, and multi-tenant tagging.
A license written for bundling, so you sign and distribute the binary yourself.
Engineering support through your launch.

How embedding Qpoint works

Qpoint is a single binary that runs underneath AI agents at the OS level and understands every file read, tool call, and outbound request as an agent action. It is built to be bundled into another vendor's product. See the embedded solution page for details.

Package. Bundle the binary into the sensor you already deploy to endpoints and hosts.
Identify. Tag each session with tenant and workspace identifiers, so every event maps to a customer.
Configure. Apply your policies with built-in or custom plugins.
Run. Subscribe to the event stream and send events into your console, audit log, or your customers' SIEM.

The building blocks:

Event stream. Structured events for every session, covering agent, process, file, tool call, request, and token usage, over gRPC or OTLP.
Plugin hooks. Code that runs at each decision point to log, tag, rewrite, or block.
Policy API. Push rules per customer or workspace from your product. Qpoint applies them on every node.

Your console, audit log, and billing stay yours. The binary, service, and user-facing strings carry your name, under a commercial partner license written for bundling. Events go only to the destinations you configure, and none of it reaches Qpoint. The same binary covers hosted agents on Kubernetes, containers, VMs, and CI runners, so one integration serves both endpoint and infrastructure customers. Early partners work directly with Qpoint's engineering team from the first integration through launch.

Frequently asked questions

Do we have to change our sensor or our customers' agents? No. You add the Qpoint binary alongside your sensor and connect to its API and event stream. It runs underneath the agent, so nothing changes in the agent itself.

Will our customers see Qpoint's name? No. The binary, the service, and every user-facing string can carry your name.

Where does customer data go? Only to the destinations you configure. None of it reaches Qpoint.

Can it block actions, or only record them? Both. Plugins can log, tag, rewrite, or block file reads, tool calls, and outbound requests before they complete, using rules you push per customer.

Does it cover servers as well as laptops? Yes. The same binary runs on macOS, Windows, and Linux endpoints and on Kubernetes, containers, VMs, and CI runners.

How is it licensed? Under a commercial partner license written for bundling. You sign and notarize the binary with your own certificates and distribute it with your product.

See how it fits your product

Book a demo and we will walk through your sensor, your customers' environments, and what an integration would look like.