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Monitor the action path, not merely the agent’s final answer. Record every proposed tool call, authorization decision, approval, execution result and downstream change; enforce permissions in a policy layer outside the model; pause hard-to-reverse actions for human approval; and alert on suspicious sequences. Logs make an incident reconstructable, but logging alone cannot stop an over-permissioned agent.
What production monitoring must observe
An agent can produce a harmless-looking answer after it has already queried sensitive systems, modified files, called an external service or attempted an unauthorized operation. The useful monitoring unit is therefore an action with a complete lifecycle:
- Proposal: what the model asked a tool or downstream service to do.
- Policy decision: whether a deterministic control allowed, denied or held the request.
- Approval: whether a human or an approved workflow authorized this specific action.
- Execution: what actually ran, where it ran and with which identity.
- Outcome: the returned result, error, side effect or resulting change.
Every record should be attributable to an agent version, initiating principal, session and policy version. Without those relationships, a stream of tool logs is difficult to investigate and almost impossible to use for accountability.
OWASP’s guidance on excessive agency recommends recording tool calls, commands, file writes and network requests with agent identity, initiating user, session and resulting diff, then storing the records outside the agent’s control. That separation matters: an agent that can rewrite or delete its own audit trail is not being independently monitored.
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Instrument the tool and side-effect boundaries
Capture proposed, blocked and completed actions
Place instrumentation in the wrapper, gateway or service that receives a tool request, not only in the chat or orchestration layer. Emit an event when the request is proposed, another when policy evaluates it, another when approval is requested or granted, and a final event when execution succeeds or fails. A denied request is valuable evidence and must be logged just as a successful request is.
| Event | Minimum useful context | Why it matters |
|---|---|---|
| Proposal | Event ID, timestamp, agent and version, initiating principal, session, tool, normalized arguments and requested scope | Shows what the model attempted before any control changed it |
| Policy decision | Allow, deny or review result; policy and rule version; reason; transformed or constrained arguments | Proves which deterministic control made the decision |
| Approval | Approval ID, reviewer or workflow identity, displayed arguments, decision time, expiry and action hash | Binds consent to the exact operation that will execute |
| Execution | Tool or service identity, destination, start and end time, attempt number, status and error class | Distinguishes an approved request from what actually ran |
| Outcome | Result classification, resource identifiers, transaction or change ID, and resulting diff where applicable | Allows investigators to measure the side effect rather than just the request |
Normalize tool names and argument fields so the same operation cannot evade detection by appearing under several aliases. Store a correlation ID across model turns, retries, child agents and downstream requests. Record parent and child session IDs when one agent delegates work to another.
Use a structured event envelope
A practical event can look like this, with sensitive values replaced by references or redacted representations:
The Tool Desk
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"event_id": "8f2c...",
"event_type": "tool_execution",
"timestamp": "2026-10-16T12:34:56Z",
"agent": {"id": "billing-agent", "version": "2026.10.4"},
"principal": {"type": "user", "id": "user-482"},
"session_id": "sess-91a",
"parent_event_id": "evt-77d",
"tool": "issue_refund",
"arguments": {"order_id": "ord-193", "amount": "[redacted]"},
"policy": {"version": "payments-12", "decision": "review"},
"approval": {"id": "apr-55", "status": "approved"},
"execution": {"status": "succeeded", "change_id": "refund-804"},
"result_class": "financial_change"
}The fields are an implementation pattern, not a universal telemetry standard. Adapt names to your logging platform, but preserve the relationships. Keep the original request and the effective request if a wrapper strips fields, limits scope or resolves an alias.
Centralize records outside the agent’s authority
Export immutable or append-only events to a log platform, security information and event management system or compliance store controlled by a separate security boundary. Restrict who can read sensitive arguments and who can alter retention settings. Monitor failures in the export path itself; a silent telemetry outage creates a dangerous period in which actions appear unobserved.
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OpenTelemetry can carry this information into existing observability systems. An official Codex security report describes exporting user prompts, tool approval decisions, tool execution results, MCP server use and network-proxy allow or deny events through OpenTelemetry before centralizing them in SIEM or compliance systems. That is an example of one implementation, not a guarantee that every agent framework emits the same event types or fields.
Keep authorization outside the model
A model can propose an operation, but its text is not an authorization decision. OWASP’s LLM06:2025 guidance states: “Implement authorization in downstream systems rather than relying on an LLM to decide if an action is allowed or not.” Enforce that principle with a gateway, tool wrapper or downstream service that evaluates the authenticated principal, agent identity, tool, arguments, target resource and requested scope.
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Use a deterministic decision sequence
- Authenticate the caller. Resolve the human or service principal that initiated the session and the identity of the agent version making the call.
- Normalize the request. Resolve aliases, expand implicit defaults and canonicalize destinations so policy evaluates the operation that will really execute.
- Check capability and scope. Verify that this agent is permitted to use the tool and that its credential covers only the necessary resources, operations and time window.
- Evaluate arguments and context. Check resource ownership, data classification, destination allowlists, transaction limits, environment and current workflow state.
- Return allow, deny or review. Include a rule and policy version in the decision event. A review result must halt the side effect until approval is complete.
- Re-authorize at execution. Recheck the action immediately before it reaches the side-effecting service, because permissions, arguments or resource state may have changed while a human was deciding.
For custom tools, attach validation at the side-effecting tool boundary rather than relying only on agent-level input and output checks. A wrapper that validates the final command, query or API request is harder to bypass than a check that inspects an earlier natural-language plan.
Apply least privilege to tools and credentials
Give an agent separate credentials for separate capabilities. A support agent that reads order status should not share a token that can issue refunds, alter account roles or export customer data. Prefer resource-scoped, short-lived credentials and explicit destination allowlists. Disable unused tools instead of merely telling the model not to call them.
For multi-agent systems, treat every handoff as a trust boundary. Pass the initiating principal, original session, permitted scope and policy context to the child agent. Do not let a child infer authority from a message that says another agent approved the action; require a verifiable approval or signed decision tied to the operation.
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Classify actions and place approval before harm
Risk tiers make automation predictable. The exact classification depends on your workload, but the control should be based on reversibility, impact, affected people and privilege—not on whether the model sounds confident.
| Tier | Typical actions | Default control |
|---|---|---|
| Low impact | Read an approved public source, calculate a value, format a draft or query non-sensitive status | Allow automatically with normal logging and rate limits |
| Moderate impact | Read internal records, create a draft ticket, update a reversible field or call an approved internal API | Allow only within narrow scope; add anomaly detection and, where appropriate, user confirmation |
| High impact | Deploy or merge code, delete data, send an external message, spend money, change permissions or access a new network destination | Hold for human approval or block by policy; show exact arguments and target |
| Prohibited | Use credentials outside assigned scope, bypass an approval, disable monitoring or exfiltrate restricted data | Deny, record and alert; do not offer an automatic retry |
OWASP’s DevSecOps guidance recommends keeping a human decision where harm is hard to undo, including pushing code, merging, deploying, deleting data, sending messages, spending money, changing permissions and network access to a new destination.
Make approval specific and resistant to replay
An approval prompt should show the actual command or API arguments, target resource, requested amount or data scope, agent and initiating user. Bind the approval to a hash or immutable identifier for that exact request, give it a short expiry and invalidate it if arguments, destination or policy version changes. Never treat a general statement such as “the user approved refunds” as approval for every future refund.
For asynchronous jobs, persist the approval state and recheck authorization when the job resumes. Make side effects idempotent where possible, so a retry after a timeout cannot create duplicate payments, messages or deployments. If the service cannot guarantee idempotency, route retries back through review.
Detect patterns instead of isolated errors
A single failed call may be normal. A sequence of unusual calls can reveal prompt injection, credential theft, privilege escalation or a runaway loop. Correlate events by agent, principal, session, destination and time window.
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| Pattern | Signals to correlate | Possible response |
|---|---|---|
| Unexpected destination | New domain, IP, MCP server or external endpoint; destination not on the agent’s allowlist | Deny or hold the request, alert security and preserve the full decision context |
| Credential access | Reads of secret stores, credential files, environment variables or tokens outside the assigned workflow | Block access, revoke the relevant credential and inspect preceding prompts and tool calls |
| Bulk extraction | Rapid pagination, unusually large reads, repeated exports or a jump in records per session | Throttle or stop the session and require an explicit export approval |
| Approval bypass | Execution event without a matching unexpired approval, changed arguments after approval or repeated denied attempts | Reject execution, alert on the policy failure and suspend the agent identity if necessary |
| Privilege escalation | Role changes, administrative endpoints, use of a broader credential or access to a new tenant | Require elevated review and verify the initiating principal through an independent channel |
| Runaway behavior | Abnormal call frequency, deep chains, repeated retries, rising token or cost consumption and growing high-impact activity | Apply circuit breakers, cap retries and pause the session for investigation |
| Instruction or pipeline tampering | Changes to prompts, memory, retrieval sources, CI configuration or tool definitions during a run | Block the change, preserve before-and-after versions and route it to change control |
Track failed calls, retries, chain depth, latency and token or cost consumption as operational signals. Thresholds must be tuned to the workload; illustrative values in security examples are not validated industry benchmarks. Start with a baseline for each agent and alert on meaningful deviations, then review false positives with the service owner.
Build the monitoring program step by step
- Inventory the action surface. List every built-in and custom tool, downstream API, database operation, file system capability, network destination and child-agent handoff. Mark which operations create, alter, disclose or delete data.
- Identify principals and sessions. Decide how a user, service account, agent version, job and delegated child agent will be represented. Ensure one correlation ID survives retries and asynchronous work.
- Define the event contract. Require proposal, policy, approval, execution and outcome events. Specify normalization, redaction, retention, access control and export failure behavior before writing dashboards.
- Assign risk tiers. Document automatic, review and prohibited operations. Include limits for amount, record count, destination, environment and privilege.
- Insert enforcement points. Put policy checks in a gateway or tool wrapper and repeat the check at the downstream side-effect boundary. Keep model instructions as guidance, never as the sole control.
- Design the approval workflow. Show exact arguments, bind decisions to an action identifier, set expiry and define who can approve each tier. Provide a safe rejection and cancellation path.
- Connect telemetry to operations. Send structured events to the organization’s log or SIEM platform, create alerts for control failures and assign an owner and response target to each alert.
- Exercise containment. Verify that an operator can pause a session, revoke an agent credential, disable a tool and prevent queued jobs from executing.
- Re-test after changes. Repeat abuse and authorization tests after changes to prompts, tools, memory, retrieval, policies, model providers or downstream services.
Protect sensitive information in telemetry
Detailed logs can become a second data-exposure channel. Capture the evidence needed to reconstruct an action without indiscriminately copying prompts, documents or tool payloads.
- Redact API keys, passwords, session cookies, payment data and regulated identifiers before events leave the execution boundary.
- Prefer typed metadata, hashes, resource IDs and result classifications over raw confidential content.
- Store sensitive argument fragments in a separately protected vault only when an investigation or legal requirement justifies it.
- Encrypt logs in transit and at rest, restrict query access and audit access to the audit data itself.
- Set retention by risk and obligation; delete or aggregate records when they are no longer needed.
- Document whether prompts, retrieved documents, model outputs and tool arguments are retained, and give operators a way to locate related records.
Do not enable raw prompt or payload capture by default without a data-handling plan. Redaction should be tested against alternate encodings, nested JSON, error messages and values returned by downstream systems.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Test controls before and after production changes
Security testing should prove both prevention and observability: the action is stopped or held, and the attempted bypass is recorded with enough context to investigate.
| Test case | Expected control | Evidence to verify |
|---|---|---|
| Prompt override asks for a forbidden tool | Gateway denies the request regardless of model wording | Denied proposal, policy rule, principal, session and no execution event |
| Arguments change after approval | Approval no longer matches; execution is held | Action hash mismatch and a new review decision |
| Credential or secret-file read | Access is blocked or narrowly scoped | Denied resource access, alert and credential identifier without secret value |
| Bulk read or export | Rate or volume control trips and the session is paused | Count, threshold decision, affected resource and operator notification |
| Privilege escalation | Downstream authorization rejects the broader operation | Effective and requested scopes, denial reason and no privilege change |
| Recursive tool loop | Depth, time or retry circuit breaker stops further calls | Parent-child chain, retry count and containment action |
| Multi-agent trust-boundary abuse | Child agent cannot inherit unverified authority | Delegation record, principal continuity and denied unauthorized call |
| Telemetry outage | Side effects fail closed or enter a controlled degraded mode | Export failure alert and proof that unobserved execution did not continue |
Run these tests before launch and after material changes to prompts, tools, memory, retrieval, policies or model providers. Include realistic failure modes such as partial network failures, expired approvals, duplicate delivery and downstream timeouts.
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Respond when a risky action is detected
Contain first
Pause the affected session or job, revoke the agent credential if misuse may continue and disable the specific tool or destination. Cancel queued high-impact work. Avoid deleting logs or resetting the environment before evidence is preserved.
Reconstruct the action chain
Use the correlation ID to join the initiating request, model proposal, policy verdict, approval record, tool execution, downstream audit record and resulting diff. Check whether the executed arguments differ from the approved ones, whether another agent delegated the action and whether credentials were reused elsewhere.
Recover and prevent recurrence
Reverse the side effect where safe, rotate exposed credentials, restore modified data from a trusted source and notify affected owners according to the organization’s incident process. Then correct the control that failed: narrow the tool scope, add a downstream check, improve argument normalization, change the approval binding or add a new detection rule. Record the test that will prove the fix works.
Choose an implementation layer with the right criteria
There is no product ranking implied by the available guidance. Compare an agent framework’s built-in telemetry, a gateway or tool-wrapper design, and downstream service controls against the same requirements.
| Criterion | Questions to ask |
|---|---|
| Coverage | Are proposed, denied, approved and completed actions captured for every tool and downstream system? |
| Attribution | Can each event connect agent version, principal, session, tool, policy version, approval and result? |
| Enforcement | Can the layer deny or pause a request before the side effect, or does it only report afterward? |
| Data handling | Can secrets be redacted, access be restricted and retention be enforced by policy? |
| Operations | Are alerts actionable, events exportable to existing observability or SIEM systems, and identity revocation practical? |
| Workflow fit | Does review occur at the actual side-effect boundary without forcing approval for every harmless read? |
| Change resilience | Do event schemas, policy versions and tests remain traceable when tools, models or providers change? |
Production readiness checklist
- Every tool call produces proposal, policy, approval, execution and outcome records as applicable.
- Events identify the agent, version, initiating principal, session, destination and policy version.
- Authorization is enforced by a gateway, wrapper or downstream service rather than model text.
- Credentials and tools are least-privilege and resource-scoped.
- Deployments, deletion, external messaging, spending, permission changes and new destinations have an explicit review or block policy.
- Approvals display exact arguments and cannot be replayed for a changed request.
- Alerts cover unexpected destinations, credential access, bulk reads, approval bypass, elevated privileges, abnormal frequency and runaway chains.
- Secrets and confidential payloads are redacted, while investigators retain enough structured evidence.
- Logs are centralized outside the agent’s control with tested retention and access controls.
- Denied actions and telemetry failures are visible to operators.
- Abuse tests cover prompt override, unauthorized tools, exfiltration, escalation, loops and multi-agent boundaries.
- Operators can pause sessions, revoke identities and stop queued side effects.
The central design rule is simple: observe and control the request where it becomes an action. A final answer may conceal a dangerous sequence, while a complete, independently stored action trail lets policy stop the harm and lets responders explain exactly what happened.
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