Your Infra Data Is the Agent Input Now

For years, "your data is precious" meant keep it safe, keep it compliant, don't let it leak. Still true. But there's a newer reason it's precious, and it raises the stakes: your observability data is the context your agents run on.

When an agent debugs your system, your logs, traces, and runtime metadata are the context it needs to do root cause analysis. Sample that data and the diagnosis becomes a guess. Put it somewhere the agent can't reach and the agent is guessing too. The quality of an agentic investigation is capped by the quality of its input.

So if the data is that precious, three things follow. It's worth protecting, which is why it stays in your own cloud instead of being shipped off to a vendor. It's worth seeing all of, which is why eBPF captures everything at the kernel rather than making you sample upfront and bake in blind spots. And if it’s precious, your data should be available, which is why connectors and MCP put that rich context in reach of the agents and tools that turn it into answers. Precious data is only precious when agents and people can act on it in a secure way.

From debugging symptoms to debugging evidence

Without infra, APM, and RUM data, an agent debugs from symptoms. A failing check. A vague bug report. A screenshot someone pasted into Slack. Maybe a stack trace, if you're lucky. From that, it can only list the usual suspects. Maybe the frontend broke, the auth failed, or the API is timing out but it can’t launch a true investigation and validate hypotheses.

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However, when you give your agent the infra, APM, and RUM data the job changes. Now it has logs around the failing request, traces showing where latency actually started piling up, the deploy and restart events near the incident, and metadata that pins the problem to a specific namespace, service, or pod. The input stops being "a human says it's broken" and becomes "requests to /api/search spend 28 seconds in service X, then fail because dependency Y is refusing connections after this config change." One of those is a complaint. The other is a fixable problem. Pair that with tribal knowledge from connected applications like Slack or Notion and your agentic orchestrator has the complete context it needs to perform evidence-based diagnosis.

When the manifest lies

To really ground the importance of providing the full fidelity data to your agent, let’s imagine the following scenario: say you've got a service that won't come up. You check the Kubernetes manifest. It looks fine. The Deployment is healthy, the container is running, the Service is defined, the port looks right on paper. Everything the manifest can tell you says this should work.

It doesn't work. The health check keeps failing and nothing's reaching the pod.

What the manifest doesn't show you: the Service targetPort is pointing at 8080, and the container is actually listening on 3000. Somebody changed the app's listen port and never updated the Service. On paper it's coherent. In reality, traffic is being routed to a port where nothing is home.

From the manifest and a red health check, an agent can only guess. The config is internally consistent, so there's nothing to flag. But eBPF sees the actual wire. It sees connection attempts hitting port 8080, getting connection-refused at the kernel level, traffic never reaching the process. The manifest is intent while eBPF Infra data exposes the reality. The gap between the two is exactly where agents get stuck without the full data picture. The groundcover eBPF sensor surfaces this data on its own, with no SDK, no instrumentation, no code change. The agent doesn't have to trust what the config claims. It can see what the kernel actually did.

Which makes BYOC the real question

The moment an agent runs outside your environment, your input leaves with it. And not just the telemetry. The questions you ask the agent, the context you feed it, the prompts you've tuned, your agentic IP, all of it crosses the boundary the instant the analysis happens somewhere else.

This is the part that catches people off guard. You adopted BYOC to keep your data in your cloud. Then you bolt on an AI feature that runs on the vendor's infrastructure, and the residency problem you solved walks right back in through the side door. The storage stayed put. The analysis didn't.

True BYOC in the AI era means the analysis comes to the data, not the other way around. Three things have to hold for that to be real:

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• Residency. The agent and the model run inside your environment, against telemetry that never crosses the boundary. groundcover's Agent Mode runs on Bedrock inside your own AWS account, so the reasoning happens where the data already is.
• A complete picture. Because eBPF captures everything at the kernel level with no sampling, the agent isn't reasoning from a thinned-out sample with blind spots baked in. You can't diagnose from data you dropped to save money.
• Pricing that doesn't punish volume. When you're billed per node instead of per gigabyte, you never have to drop or sample the exact data your agent needs just to keep the bill sane. The input stays whole because keeping it whole doesn't cost extra.

The honest part: BYOC isn't an airgap

Worth being straight about this, because it's easy to oversell. BYOC is for residency and cost control. It isn't an airgap, and groundcover is an observability platform, not a security perimeter. The data plane living in your cloud doesn't mean nothing can ever reach out of it.

When you connect an outside agent like a Cursor session, an IDE integration, or a connector over MCP, that telemetry can now leave your environment. That can be completely fine. Often the productivity is worth it. What you want to be deliberate about is how tightly that access is scoped, not whether the access exists at all.

And a connector doesn't automatically expand your exposure. A connector that behaves like a narrow, on-demand access path can stay consistent with BYOC: the customer authenticates directly, the session is bound to their tenant, the backend is explicitly selected and revalidated, results aren't cached outside policy, and every access is audited. You can scope an MCP connector tightly, with read-only access, tenant binding, no standing access, and a full audit trail. The contradiction was never that a public endpoint exists. The contradiction is saying "your data stays in your environment" while a connector quietly hands a shared control plane broad, ongoing access to it. Same word, very different architectures.

And if your BYOC model was already calling out to public APIs, a well-scoped connector isn't widening your exposure beyond what you'd already signed up for. It's using access you already had. The work is making sure that access stays the same shape, scoped the same way, rather than becoming a new, looser path you never reviewed.

Scoping is the work, and groundcover gives you the controls to do it. 

In groundcover, RBAC decides who can point the agent at what, so a given role can read traces for one namespace and nothing else. Separate controls govern agent spend, so a runaway investigation can't quietly burn through your token budget. Write-capable actions sit behind approval gates, so the agent proposes the fix and a human signs off before anything mutates. And PII gets parsed out before ingest with tagging and parsing rules, so the sensitive fields never land in the data plane in the first place, which means they're not there for an agent or a connector to reach later. None of this is automatic. It's the difference between "we scoped the access" as a claim and as a configuration you can point to.

The input deserves a home

A good engineer reaches for the system's own account of what happened before anything else. Not the bug report, not the screenshot, not the guess. The logs, the traces, the wire. Your agent should get to reach for the same thing. And it shouldn't have to leave your cloud to do it. Give groundcover a try with our playground or launch groundcover's eBPF sensor on your cluster for free.