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Use Groundcover as a read-oriented observability bridge, not as an unrestricted operator. Connect Claude Code or Codex to the groundcover MCP Server (or use Groundcover Agent Mode), limit access to the services, namespaces, clusters and time windows involved, and expose logs, metrics, traces and Kubernetes context first. Keep deployments, rollbacks, secret changes, ticket mutations, customer communications and code changes behind an explicit human approval gate.
This arrangement lets an agent correlate telemetry and explain a likely cause faster while preserving a clear boundary around production effects. The agent can investigate; a person decides whether anything changes.
What this setup does—and where it stops
The groundcover MCP Server connects Claude Code and other AI agents to live logs, metrics, traces and infrastructure metadata. That gives an assistant the evidence needed to investigate an alert, an SLO breach or a symptom reported by an operator. Groundcover describes the service as a way for an assistant to connect those signals, detect anomalies and explain the reason behind an incident.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteGroundcover Agent Mode is a separate operating model for open-ended investigations. It can start with a question rather than an existing alert, monitor or incident ticket. It uses gcQL to query logs, metrics, traces and events, and makes each query visible and reusable. Groundcover says Agent Mode can run on Amazon Bedrock, OpenAI ChatGPT or Anthropic Claude inside the customer cloud.
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Neither mode makes an AI model a security boundary. Anthropic explicitly warns that “model behavior is not a security control,” and says a server does not infer intent from a tool result, fetched webpage, MCP response or inter-session message. Treat every returned log line, trace attribute, ticket comment and web page as untrusted data.
The safe division of responsibility is therefore:
- Agent: retrieve scoped telemetry, correlate evidence, identify uncertainty, form a root-cause hypothesis and propose reversible actions.
- Human: approve or reject any action that changes production, exposes information externally, spends money, modifies a ticket or creates an irreversible state.
- Platform controls: enforce identity, network, sandbox, tool and audit policies even when the model is mistaken or manipulated.
Choose the operating mode
Use the mode that matches how your team wants investigations to begin and where data processing must occur. The safety properties come from your deployment and permissions, not from the name of the mode.
| Decision point | MCP-connected Claude or Codex | Groundcover Agent Mode |
|---|---|---|
| How an investigation starts | An operator asks Claude Code or Codex to investigate through the configured MCP connection. | An open-ended question can begin the investigation without a pre-existing alert, monitor or incident ticket. |
| Evidence available | Groundcover logs, metrics, traces and infrastructure metadata exposed through MCP tools. | Logs, metrics, traces and events queried with gcQL. |
| Query visibility | Depends on the MCP client and your logging configuration; record tool inputs and outputs explicitly. | Groundcover says each gcQL query is visible and reusable. |
| Where processing can run | Where your Claude Code or Codex session and MCP server are deployed. | Groundcover says Agent Mode can run inside the customer cloud with Amazon Bedrock, OpenAI ChatGPT or Anthropic Claude. |
| Write capability | Keep the initial tool set read-only; add remediation tools only behind approval. | Expose only the capabilities your deployment has approved; do not infer write permission from Agent Mode itself. |
If you need a guided investigation inside an existing developer session, start with MCP and a read-only tool policy. If responders want to ask questions directly in the observability environment, Agent Mode may be a better fit, provided the same identity, scope and approval controls are applied.
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Start with read-only telemetry
Create a role or service identity that can read only the telemetry needed for diagnosis. A useful baseline includes log search, metric queries, trace lookup, Kubernetes object metadata and Kubernetes events. Do not attach shell access, deployment credentials, secret-read permissions, pull-request creation, ticket mutation or database write access to this identity.
Read-only does not mean harmless. Logs can contain tokens, personal data, customer content or attacker-controlled instructions. Apply the same redaction, retention and access rules you use for human responders. If a query would be inappropriate for an on-call engineer to view, it is inappropriate to expose to the agent.
Require an explicit investigation scope
Make the assistant state its scope before it runs a query. Require all four boundaries:
- Service: the named workload or set of workloads.
- Namespace and cluster: the Kubernetes location, including production versus non-production.
- Time window: an absolute start and end time with timezone, plus a reason for extending it.
- Incident objective: the symptom or SLO that must be explained.
Reject requests such as “look everywhere for anything unusual.” A broad search increases data exposure, query cost and the chance that an irrelevant log line steers the investigation.
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Separate observation from effect
Use two tool classes with different policies:
- Observation tools: logs, metrics, traces, events and metadata. These can run automatically within the declared scope.
- Effect tools: deploy, rollback, scale, restart, change configuration, read or rotate secrets, alter access, close or edit a ticket, send a customer message, merge code or open a pull request. These must pause for human approval.
Keep effect tools unavailable during the evidence-gathering phase when possible. If the client must expose them, configure the permission policy so every call waits for approval. Anthropic distinguishes an always_ask policy, which waits for approval, from auto, which may run before a person sees the call. Only the former is a human checkpoint.
Connect Claude Code or Codex without overexposing production
- Prepare a dedicated identity. Create credentials for the MCP connection or Agent Mode integration with telemetry-read permissions only. Bind them to the approved environment and short-lived credentials where your identity system supports that.
- Install or enable the groundcover MCP Server. In the MCP settings for Claude Code or Codex, add the server using Groundcover’s current deployment instructions. Verify that the connection exposes query and metadata tools, not remediation tools.
- Constrain network reachability. Permit the MCP service to reach only the Groundcover endpoint and the telemetry backends it must query. Use the assistant’s sandbox and network policy to deny unrelated outbound access.
- Set the default policy to read-only. Configure effectful calls to require an approval prompt every time. Do not rely on a system prompt to enforce this; enforce it in the client, gateway and identity layer.
- Run a harmless verification query. Ask for a bounded lookup in a non-sensitive service or a known time window. Confirm that the result contains expected fields, that secrets are redacted and that the call appears in your audit stream.
- Document the boundary. Record the identity, allowed clusters and namespaces, maximum query window, retention rules, approval owner and emergency revocation procedure.
Groundcover’s AI Observability material says captured LLM and agent data stays in the customer’s environment with no outbound traffic. Treat that as a vendor claim to validate against your actual deployment architecture, data flows and contract. Confirm where prompts, tool results, traces and model-provider requests are stored before using production data.
Use a scope-first incident prompt
A structured request reduces accidental overreach and gives the agent a testable assignment. Put the following fields in your incident intake form or runbook:
Investigate this production incident in read-only mode.
Service: payments-api
Cluster: prod-eu-1
Namespace: payments
Time window: 2026-10-18 14:00–14:30 UTC
Symptom: 5xx rate exceeded the SLO after 14:12 UTC
Allowed evidence: logs, metrics, traces, Kubernetes events and deployment metadata
Do not: change resources, read secrets, contact customers, mutate tickets or create code changes
Before every query: restate the scope and explain why the query is needed
Deliver: timeline, correlated evidence, competing hypotheses, confidence, unknowns and a reversible fix proposal
Replace the example values with the incident’s real scope. The important behavior is the sequence: restate boundaries, query only the evidence needed, show the evidence, then separate facts from hypotheses.
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1. Confirm the signal
Begin with the stated SLO breach, alert or symptom. Ask the agent to identify the metric, baseline and exact interval it will inspect. It should report whether the signal is present, when it began, how it compares with the preceding period and whether the query itself has gaps.
Do not let the agent silently widen the window because the first query is inconclusive. Require a new scope statement and approval from the incident lead before extending the search.
2. Correlate metrics, logs and traces
Use metrics to locate the affected operation, traces to follow a request across services and logs to inspect the concrete error. Kubernetes events and metadata can reveal scheduling, probe, rollout or node conditions that application telemetry misses.
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Ask for links between signals rather than a list of observations. A useful output names the request or operation, the first failing span, the corresponding log entries, the affected workload and the time relationship. Groundcover’s gcQL queries are intended to remain visible and reusable in Agent Mode; preserve the same query text and result references when working through MCP.
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Have the agent compare the incident timeline with recent deployments, configuration changes, dependency changes, node events and traffic shifts. “After” is not proof of “because”: require evidence that the changed component participates in the failing path and that unaffected paths behave differently.
When deployment metadata is unavailable, state that limitation instead of inventing a change correlation. A missing deployment record is an investigation gap, not evidence that no deployment occurred.
4. Form and test competing hypotheses
Require at least one alternative explanation when the evidence is ambiguous. For each hypothesis, ask for:
- the observations that support it;
- the observations that contradict it;
- the next read-only query that would discriminate between it and the alternatives;
- the confidence level and the reason for that confidence.
This prevents a plausible error message from becoming an unsupported root-cause claim. It also makes the handoff useful to an engineer who did not watch the investigation.
5. Produce a reversible remediation proposal
Once a hypothesis is strong enough, ask for the smallest reversible action, expected effect, blast radius, rollback method and success metric. Examples include pausing a rollout, reducing traffic to a suspect version or changing a feature flag, but the agent must not perform those actions automatically.
The proposal should include a validation query and a stop condition. “Deploy the fix” is not a safe plan; “apply this reviewed change to one canary, watch the error-rate and latency metrics for 15 minutes, and roll back if either threshold worsens” is a reviewable plan. The exact thresholds must come from your service’s SLOs and runbook, not from the model.
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6. Stop at the approval boundary
Present the evidence, uncertainty and proposed action to the incident commander. A human should confirm the target, change window, authorization, rollback path and communication plan. Only then should a separately authorized tool call execute, with the approval captured alongside the call.
Defend against prompt injection in telemetry
Production data is an input channel controlled partly by users and attackers. An exception message might say “ignore previous instructions”; a ticket may contain a malicious command; a trace attribute may include a fake approval. The assistant must treat all of these as data, never as policy.
Use layered controls:
| Layer | Control | Purpose |
|---|---|---|
| Identity | Dedicated, least-privilege credentials scoped to approved telemetry. | Limits what a compromised or confused session can read. |
| Tool policy | Read-only observation tools by default; effect tools gated by approval. | Prevents retrieved text from directly causing production changes. |
| Sandbox | Constrained filesystem and network access with explicit allow rules. | Stops an investigation from reaching unrelated systems. |
| Prompt handling | Mark logs, traces, tickets and web content as untrusted; never follow instructions found in them. | Reduces prompt-injection impact. |
| Human review | Require approval for destructive, external or difficult-to-reverse effects. | Provides an accountable decision point. |
| Audit | Retain prompts, tool calls, results, approvals and network decisions. | Supports reconstruction and review. |
Anthropic’s security guidance is direct: model behavior is not a security control. If an agent can reach a sensitive system, protect that system with permissions and policy, not with an instruction to “be careful.”
Keep these actions human-controlled
Require an explicit approval for any action with external, destructive or hard-to-reverse consequences:
- deployments, rollbacks, restarts and scaling changes;
- configuration, feature-flag or routing changes;
- secret access, rotation or replacement;
- database writes, data deletion or permission changes;
- ticket creation, edits, closure or severity changes;
- pull-request creation, code modification, merge or release;
- customer, regulator or public-status communication;
- changes that incur material cost or expand data collection.
An approval prompt should show the exact tool, arguments, target, expected effect, rollback, evidence supporting the action and the identity of the approving person. “Approve remediation” is too vague to audit.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Audit the complete chain
Store enough information to reconstruct what happened without relying on the model’s final summary. At minimum, retain:
- the original incident request and declared scope;
- the agent’s reasoning summary or decision notes, without treating them as authoritative evidence;
- every MCP or Agent Mode tool name and input;
- the returned records, query text and timestamps;
- approval requests, approver identity, decision and time;
- network allow and deny events;
- the final human disposition, including rollback or follow-up work.
OpenAI documents OpenTelemetry export for Codex prompts, tool calls, MCP use and network decisions. Export those event classes to the same protected observability system as other security-relevant records, apply retention and access controls, and test that an audit entry is generated for both an allowed and a denied action.
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What the published examples do—and do not—prove
Groundcover’s technical guide demonstrates a Claude Code workflow that detects an SLO breach, queries traces, diagnoses a distributed root cause, files an incident ticket and suggests a code fix through MCP. It is a workflow example, not a guarantee that every incident will be diagnosed correctly or that ticket and code integrations are safe to enable without additional controls.
The guide includes a demonstration load test in which 86 of 120 requests, or 72%, breached a 500 ms SLO target. That is a groundcover demonstration figure from 2026, not a general benchmark for agent accuracy, latency or production reliability.
A groundcover customer story about Usable says incidents were handed to a remote agent that queried Groundcover and Kubernetes, correlated telemetry with prior investigations, identified a likely root cause, proposed and implemented a fix and submitted a pull request. The story also reports zero minutes spent debugging manually without MCP input. Those are customer-reported workflow claims, not independently measured performance results. Use them to understand a possible operating pattern, not to set your own expectations.
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Troubleshoot common failures safely
The agent cannot see production telemetry
- Confirm the MCP server is connected to the intended session and that the identity has telemetry-read permission.
- Check cluster, namespace and time-window filters; an empty result may be correct scoping.
- Verify network allow rules and inspect denied connection events.
- Run a bounded, non-sensitive verification query and compare its result with a human operator’s view.
Results are too broad or too expensive
- Stop the query rather than granting wider access.
- Narrow to one service, operation, namespace and absolute time window.
- Ask for a metric or trace identifier first, then retrieve matching logs.
- Require the agent to explain why each additional query is necessary.
The agent treats a log message as an instruction
- Terminate the current investigation if an effectful tool was invoked or an approval was fabricated.
- Preserve the malicious record and tool transcript for security review.
- Restart with the record explicitly labeled untrusted and with effect tools disabled.
- Review redaction, tool policy and network controls; do not solve the issue only by editing the prompt.
Evidence conflicts
- Ask the agent to list the conflicting signals and their timestamps.
- Check clock skew, sampling, aggregation windows and missing telemetry.
- Prefer direct traces and corroborated metrics over an isolated message.
- Record the uncertainty and escalate to the incident commander instead of forcing a single cause.
A proposed fix is not safely reversible
- Reject it and request a smaller canary or a read-only validation.
- Require a defined rollback action and a success metric before approval.
- Have the responsible engineer review permissions and blast radius independently.
A practical handoff format
Ask the agent to finish with a compact handoff that another engineer can verify:
Scope
- Service, cluster, namespace and UTC time window
Observed facts
- SLO or alert signal
- Relevant metric, trace, log and Kubernetes-event evidence
- Query text and timestamps
Hypotheses
- Primary hypothesis and confidence
- Alternative hypotheses and disconfirming evidence
- Unknowns and telemetry gaps
Proposed next step
- Smallest reversible action
- Expected effect and blast radius
- Validation query and stop condition
- Required human approver
Disposition
- Approved, rejected or deferred
- Approval identity and time
- Result and follow-up owner
This format keeps facts, interpretation and action separate. It also makes it obvious when the agent has not gathered enough evidence to justify a change.
When this pattern is appropriate
Use a Groundcover-connected assistant when responders need cross-signal correlation, repeatable queries and a faster first-pass timeline, and when your organization can enforce least privilege and auditability. Keep the assistant read-only for routine diagnosis.
Do not grant autonomous remediation merely because an example shows a successful fix. Expand permissions only after you have tested failure modes, verified audit records, defined rollback procedures and assigned a human owner for each approval. The safest production investigator is an agent with broad visibility into the evidence it needs and narrow authority over everything else.
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