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AI agents can safely use observability data for bounded, read-oriented work—such as correlating events in a defined incident window, grouping similar errors, comparing a metric with a baseline, and identifying trace spans associated with a failure. The safe boundary depends on what data the agent can see, which tools and credentials it has, and whether it can cause side effects. Give it the minimum data and permissions required, treat telemetry as untrusted and potentially sensitive, and require human approval before consequential actions.
What “safe” means for an AI agent reading telemetry
Observability data is not automatically harmless because it comes from logs, metrics, or traces. Agent telemetry can include user inputs, model responses, retrieval sources, tool names and arguments, account identifiers, permissions, session state, and the output of external systems. A production trace may therefore contain personal data, credentials accidentally printed by an application, confidential business information, or text designed to manipulate the agent.
Safety has four dimensions:
- Data exposure: whether the agent receives aggregates and redacted metadata or raw prompts, responses, identifiers, and tool payloads.
- Permission: whether it can read a filtered dataset, query broad records, or write and administer systems.
- Action impact: whether its output is a reversible suggestion or can change configuration, contact people, delete evidence, or affect production.
- Evidence quality: whether agent activity, policy decisions, and tool calls are correlated and protected from tampering.
Read-only access is lower risk than write access, but it is not risk-free. A read query that exposes every customer conversation or a long-lived credential can still create a serious incident. “Read-only” should describe a narrow, enforced capability, not a blanket exemption from security review.
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Microsoft Learn’s observability guidance also cautions that uptime and error rates alone do not indicate quality and reliability in AI systems. An agent investigating an AI application needs execution traces, retrieval provenance, tool activity, evaluation results, and relevant security events—not just a dashboard of availability numbers.
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Tasks that are usually appropriate
Start with a written question and a bounded time range, service, tenant, or incident. The following jobs are useful examples of low-impact analysis when the underlying data is filtered and the result is reviewed:
Correlating one run or incident
Provide a run identifier, trace identifiers, timestamps, and the relevant service boundary. The agent can arrange events chronologically, connect parent and child spans, and identify where latency or errors first appeared. Correlation is safer when the agent cannot search unrelated tenants or extend the time range on its own.
Summarizing failures
An agent can turn a bounded set of error events into a concise incident summary: affected component, first and last occurrence, common error classes, and links or identifiers for representative traces. Keep the summary tied to observed records. It should distinguish an observed symptom from a hypothesis about the cause.
Grouping similar errors
Clustering stack traces, status codes, or normalized messages can help an engineer see that many apparently different events share one failure mode. Redact identifiers and secrets before sending messages to the model, and preserve a way for a human to inspect the original record under normal access controls.
Comparing a metric with a baseline
The agent can compare a selected metric with an approved baseline—for example, the current error rate against the same service’s recent normal range—and flag an unusual change. Define the comparison window and baseline in the request so the agent does not silently choose a misleading period.
Finding trace spans associated with a failure
Given an error signature or request identifier, an agent can point to relevant spans, tool calls, and retrieval steps. This is a navigation aid, not proof of causation. An engineer should verify the trace and any proposed explanation.
Drafting, rather than executing, an investigation
It is generally safer to let the agent draft a query, an incident timeline, or a proposed next diagnostic step. A separate reviewer can inspect the query and run it through an approved gateway. The agent should not be able to turn a draft into an unrestricted search merely by changing its own parameters.
Tasks that require approval or stronger controls
Microsoft Agent Framework and AWS guidance use side effects, sensitivity, reversibility, and scope of impact as approval factors. Apply that test to observability operations:
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- Changing alert rules, dashboards, sampling, retention, or access controls.
- Editing production configuration or invoking remediation scripts.
- Sending incident notices, customer messages, or tickets to external systems.
- Deleting, redacting, or bulk-exporting logs, traces, prompts, or session records.
- Making purchases, scaling infrastructure, or restarting or disabling services.
- Querying sensitive records outside the incident’s approved scope.
The agent may prepare the change and explain its expected effect, but an authorized person or separately controlled policy should approve execution. For irreversible or broad-impact operations, require an explicit confirmation that names the target, scope, and rollback plan. A model’s statement that an action is safe is not an approval.
Design the access boundary before connecting data
Choose the smallest useful data set
Begin with aggregate metrics, redacted metadata, normalized error types, timestamps, and opaque run identifiers. Add raw message text or tool payloads only when the diagnostic question cannot be answered without them. Microsoft’s guidance recommends collecting identity context, timestamps, run identifiers, execution details, retrieval provenance, tool invocations, token use, latency, errors, and tool-call volume for oversight; that recommendation describes what a team may observe, not a reason to expose every field to an agent.
Separate collection from agent access
Store complete telemetry under the organization’s normal retention, residency, encryption, and access policies. Expose a filtered view or query service to the agent. This separation lets you mask fields, enforce tenant boundaries, rate-limit searches, and revoke access without changing application instrumentation.
Use short-lived, task-specific credentials
The UK National Cyber Security Centre recommends restricting network access by default and limiting credentials to the permissions and lifetime required for a task. A proxy or trusted gateway can inject credentials without revealing them directly to the model. Do not place a broad production token in a prompt, environment variable visible to the agent, or log record.
Enforce restrictions outside the model
OpenTelemetry’s Open Agent Management Protocol (OpAMP) security guidance recommends zero-trust remote configuration, minimum privilege, local restrictions on directories and scripts, validation or sanitization of remote settings, and opt-in remote configuration. These are recommendations for OpAMP-managed telemetry agents, but the control pattern applies broadly: a model should not be able to override a locally enforced allowlist by producing different instructions.
Constrain network destinations
Permit only the observability backends, policy services, and approved gateways required for the task. Block arbitrary outbound connections by default. Network policy should be enforced by the runtime or proxy, not inferred from the agent’s natural-language plan.
Telemetry is untrusted input
Logs, traces, issue text, retrieved documents, and tool results can contain indirect prompt injection. A malicious or simply malformed log line may tell the agent to reveal data, disable a control, or run a command. Microsoft Agent Framework warns that retrieved data and chat history can carry such instructions and that model output can contain payloads that are dangerous when rendered or executed.
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Give the agent an explicit instruction hierarchy: telemetry is evidence, never authority. A string inside a log cannot grant permission, change the query scope, or approve a tool call. Treat all model output as untrusted until validated. Sanitize text before inserting it into HTML, shell commands, SQL, configuration files, ticket bodies, or security tooling. Use structured schemas and allowlisted values where possible instead of passing free-form output directly to an executor.
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Keep analysis and execution separate. A safe pattern is: the agent proposes a query or change; a validator checks syntax, target, scope, and policy; a human or deterministic control approves; only then does an executor run it.
Protect prompts, responses, and session data
AI observability may capture conversation content, retrieval source provenance, tool arguments, tool results, and account identity. Microsoft advises balancing forensic value with privacy, data minimization, residency, retention requirements, access controls, and encryption. Decide these properties before enabling detailed capture, and document who can retrieve each class of data.
Understand product-specific defaults
GitHub’s documented Copilot OpenTelemetry feed excludes identifying attributes, prompts, responses, tool arguments, tool results, and raw policy details by default; managed settings can enable capture classes separately. That is a Copilot-specific behavior, not a universal OpenTelemetry default. Inspect the equivalent settings for every product and framework you deploy.
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Choose storage for deletion and retention needs
Google Cloud recommends storing prompts and responses in a Cloud Storage bucket rather than log entries when fine-grained deletion control is needed. Cloud Logging documents a maximum log-entry size of 256 KiB. That limit applies to Cloud Logging specifically, so check the size, retention, residency, and per-record deletion behavior of your own backend before designing a capture format.
Redact before collection when feasible
Mask credentials, access tokens, payment data, personal identifiers, and secret-bearing headers at the application or collector boundary. Redaction after an agent has already read the raw value does not prevent exposure. Maintain a documented exception process for cases where investigators genuinely need unredacted content.
Compare access designs by risk
| Design | Data scope | Permission and action | Typical use | Required controls |
|---|---|---|---|---|
| Filtered read-only view | Aggregates, redacted metadata, bounded traces | Read only; no external side effects | Incident summary or error grouping | Tenant and time-range filters, short-lived credential, query logging |
| Controlled query service | Selected raw fields on approved datasets | Read queries through a gateway | Trace navigation and baseline comparison | Allowlisted query shape, rate limits, masking, network restriction, human review for scope expansion |
| Draft-and-approve workflow | Data needed to prepare a change | Agent drafts; separate approver executes | Alert or dashboard change proposal | Policy validation, named approver, target and rollback shown before execution |
| Administrative or mutative access | Broad operational data and controls | Writes, deletion, configuration, or remediation | Only exceptional automation | Strong isolation, least privilege, explicit approval, immutable audit trail, tested rollback |
Move down this table only when the diagnostic or operational requirement justifies the added exposure. A more capable design is not automatically a better design.
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What to log about the agent itself
Oversight requires both the evidence the agent reads and a record of what the agent did. Microsoft recommends linking execution steps with traces and recording relevant tool invocation details, token use, latency, errors, and tool-call volume. AWS similarly recommends tracking invocation rates, execution duration, and errors and alerting on deviations.
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At minimum, record:
- Agent identity, version, run identifier, initiating user or service, and timestamps.
- Datasets, tenant scope, time range, and query or retrieval identifiers.
- Tools requested, policy decisions, approvals, arguments after redaction, results, and exit status.
- Network destinations and proxy allow or deny events.
- Model errors, retries, latency, token usage, and unusual tool-call volume.
- Human corrections, rejected actions, and the final disposition of the incident.
Protect these records from modification and deletion. The NCSC says agent activity should be observable near real time and after the fact, including traces or transcripts, access logs, proxies, and network traffic. It also advises treating agent activity as user activity for security operations and incident response, while recognizing that the logging infrastructure itself is an attack surface.
Use append-only or otherwise tamper-evident storage where possible, restrict who can administer the logging system, and monitor attempts to disable or bypass collection. Keep enough context to reconstruct a decision without retaining unnecessary message content.
A practical rollout sequence
- Define one question and one boundary. Specify the service, tenant, incident window, fields, and expected output.
- Classify the data. Mark prompts, responses, identifiers, tool payloads, credentials, and personal data. Decide what can be redacted or excluded.
- Create a filtered view. Expose only the records and fields required for the question. Enforce filters in a gateway or data service.
- Issue a short-lived credential. Grant read access only to the approved view and block unnecessary network destinations.
- Define forbidden actions. Explicitly deny writes, deletion, external messaging, configuration changes, and scope expansion unless a separate approval path exists.
- Validate untrusted content. Treat retrieved text as data, sanitize output, and prevent model-generated strings from becoming executable commands or policy changes.
- Add approval gates. Require a named reviewer for sensitive-data access, irreversible actions, broad-impact changes, or any side effect.
- Instrument and alert. Correlate the run, tools, policy events, network activity, latency, errors, and unusual call volume.
- Test failure and revocation. Confirm that a denied query, expired credential, malformed log, prompt injection, or unavailable backend fails closed and can be investigated.
- Review retention and contracts. Document storage location, residency, encryption, deletion, access review, and incident-response responsibilities.
Common failure modes and fixes
“The agent only has read access, so it is safe.”
Failure: The agent can read every tenant’s raw prompts or a long-lived secret.
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“We enabled detailed traces for troubleshooting and forgot to turn them off.”
Failure: Full chat messages, function calls, and results accumulate in production telemetry.
Fix: Use redaction and sampled, time-limited diagnostic capture. Apply documented framework warnings to equivalent settings and verify the resulting records.
“The log told the agent to run a command.”
Failure: An indirect prompt injection in retrieved content changes the agent’s plan.
Fix: Mark telemetry as untrusted, enforce tool allowlists outside the model, validate arguments, and require approval for execution.
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“The model’s explanation became the incident record.”
Failure: A plausible hypothesis is stored as fact, obscuring what was actually observed.
Fix: Require separate fields for evidence, uncertainty, and proposed explanation, with links to the original trace identifiers for human verification.
“The logs can be edited by the same automation they monitor.”
Failure: An attacker or faulty workflow removes evidence.
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How to decide whether to grant access
Approve an agent connection only when you can answer these questions in writing:
- What precise diagnostic or operational question is the agent solving?
- Which fields and records are necessary, and which are excluded?
- Which identity, tenant, time-range, and network boundaries are enforced outside the model?
- Can the agent change data, contact anyone, execute code, or delete evidence?
- Where is the human approval point, and who is authorized to use it?
- How are prompts, responses, traces, and audit records encrypted, retained, deleted, and reviewed?
- Can investigators reconstruct the run, including tool calls, policy decisions, and network activity?
- What happens when the agent encounters an injection, malformed record, denied query, expired credential, or unavailable service?
If the answers are incomplete, keep the agent in a narrower read-only pilot rather than granting broader production access.
What current guidance shows
The NCSC’s central operational principle is that “Agentic AI activity should be treated as a form of user activity.” Microsoft’s safety guidance states, “Do not treat LLM output as authoritative without verification.” Together, these principles rule out treating an agent as either a trusted observer or an autonomous administrator.
OpenAI’s May 8, 2026 description of Codex OpenTelemetry events includes prompts, tool approval decisions, tool results, MCP server use, and network proxy allow or deny events. It describes an internal security triage agent that analyzes unusual activity and presents the analysis to security staff for review. That is an example of a review-centered deployment, not a guarantee that every agent or telemetry setup is safe.
The practical answer is therefore conditional: let an agent inspect the minimum observability data needed for a defined, read-oriented task; make every boundary enforceable without relying on the model; and place a human or deterministic policy gate before sensitive, irreversible, or broadly impactful actions.
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