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An AI “sanity agent” should flag possible policy or fact drift for review, not decide on its own that a policy is wrong. The available evidence does not establish how a specific Sanity AI Agent was built or what it caught, so this guide focuses on a defensible design: compare approved, versioned material with current facts or observed agent behavior, explain each alert with traceable evidence, and require a person to approve changes.
What does “fact drift” mean for policies and AI agents?
Fact drift occurs when a fact, source, or assumption supporting a policy changes, making the policy potentially inaccurate. Policy drift is different: it occurs when agents have inconsistent controls or when a deployed configuration no longer matches the approved baseline. A monitoring system should distinguish these cases because they call for different evidence and different fixes.
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For example, a policy could still be configured exactly as approved while relying on an outdated external fact. Conversely, an agent could be using a current policy but have a different prompt or guardrail configuration from the organization’s approved version. Treating both as a generic “drift” alert obscures what needs investigation.
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Use it as a monitoring and review workflow, not an oracle. The workflow needs a known-good baseline, a measurable way to detect change, evidence attached to every finding, and human approval before policy changes are adopted. AWS guidance describes statistical signals for detecting drift and semantic analysis to help classify sampled changes; it cautions that a statistical alert shows that drift happened, not why. Its guidance concerns production application data, so it is a design reference—not proof that an LLM can reliably fact-check policy text.
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- Preserve the approved baseline. Store policy text, supporting fact sources, prompts, and behavioral configuration with identifiable versions and approval records. Record which version is deployed to each agent.
- Watch for defined changes. Compare current source material or observed behavior with the baseline. Specify in advance what counts as a signal—for example, an exact policy edit, a changed source version, a statistical shift, or a meaning-level discrepancy—and document alert thresholds.
- Explain the signal. A detector can identify an exact difference or unusual distribution; semantic analysis can help characterize what a sampled change might mean. Neither alone proves that the policy is stale. Attach the relevant baseline and current versions, source evidence, and the reason the review was triggered.
- Route the finding to a reviewer. A person should decide whether the evidence changes the policy’s meaning or applicability, whether the source is authoritative, and whether a correction is warranted.
- Approve, test, and roll out changes safely. Update the approved baseline only after review. Evaluate the revised prompt or configuration, stage its rollout, and keep a tested rollback path. AWS’s agent lifecycle guidance recommends version history, evaluation gates, staged rollout, attribution, and tested rollback for prompts and configurations: Prompt and configuration lifecycle management.
Rules-based detection or semantic review?
These approaches serve different purposes. A rules-based or statistical detector can signal a change; semantic or LLM-assisted analysis can help explain sampled changes. A useful design may combine them, but neither method removes the need for a documented threshold and human approval.
| Design dimension | Rules-based or statistical detector | Semantic or LLM-assisted review |
|---|---|---|
| What it detects | Exact edits, configured conditions, or statistical distribution changes, depending on the detector. | Possible meaning-level discrepancies in the material it analyzes. |
| Explainability and provenance | Can point to the matched rule, changed field, or measured signal; preserve the compared versions and inputs. | Can offer a natural-language characterization; preserve the analyzed text, source, model/version, and output so a reviewer can verify it. |
| False alerts and missed changes | May miss changes outside configured rules or thresholds and alert on harmless changes that meet them. | May misinterpret a change or overlook one; its explanation is a lead for review, not proof. |
| Review burden | Depends on threshold quality and how many low-value signals it produces. | Can help triage sampled changes, but a human still needs to verify the interpretation and source. |
| Latency and cost | Depends on the detector and monitoring cadence; the cited guidance does not establish a universal cost or latency. | Depends on the model and review scope; the cited guidance does not establish a universal cost or latency. |
| Who approves a policy change? | A human reviewer should approve it. | A human reviewer should approve it. |
| Rollback | Detection does not provide rollback by itself; keep an approved prior version and a tested recovery procedure. | Semantic analysis does not provide rollback by itself; keep an approved prior version and a tested recovery procedure. |
AWS presents statistical detection as a way to signal change and LLM-assisted semantic analysis as a way to classify sampled changes, followed by human review. It does not establish one approach as universally better: Detecting drift in production applications.
Shared governance templates or individual agent settings?
Individual settings can fit specialized risks, but they create more opportunities for controls to diverge and make organization-wide auditing harder. Microsoft says, “Configuring each agent separately produces drift.” Its Agent 365 guidance describes shared policy templates and custom templates as ways to apply governance at scale; availability and applicability depend on the organization’s environment.
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| Approach | Consistency | Fit to local risk | Audit and maintenance |
|---|---|---|---|
| Individual agent configuration | Controls can diverge unless changes are centrally tracked. | Can be tailored agent by agent. | Reviewing and maintaining each configuration can increase effort. |
| Shared templates, with documented exceptions | Helps apply common controls consistently. | Custom templates or controlled exceptions can address organization-specific needs. | Central baselines make governance easier to review, though templates still need ownership and updates. |
For implementation details and the limits of the product context, see Microsoft’s guidance on enforcing agent policies at scale and its broader guidance on governing and securing AI agents across an organization.
What should an alert log so people can explain it?
A useful alert lets a reviewer reconstruct what happened without relying on the agent’s summary alone. Link the finding to the identities, versions, evidence, and actions involved. Microsoft’s monitoring and forensics guidance describes records spanning identity, prompts, retrieved context, model/version, guardrail decisions, tool calls, outputs, resource use, and downstream actions.
- Finding: unique identifier, timestamp, detector or rule, threshold, and exact signal that triggered review.
- Baseline and current material: policy and configuration versions, relevant source versions, and the specific text, field, or behavior compared.
- Execution context: agent and user or service identity, prompt, retrieved context, model/version, guardrail decisions, and tool calls.
- Result and impact: agent output, resource use, downstream actions, reviewer decision, and any approved correction or rollback.
- Provenance: source URLs or identifiers, retrieval time, and enough retained evidence for the reviewer to check whether the cited material supports the alert.
Access to these records should be controlled appropriately for the sensitivity of prompts, retrieved data, and outputs. Microsoft’s Monitoring, Detection, and Forensics guidance provides the operational context.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which operational signals are useful—and what can’t they prove?
Operational metrics can reveal where to investigate, but a metric is not a verdict on policy accuracy. Google Cloud’s semantic-governance documentation lists measures such as request and evaluation counts, latency distributions, evaluation token counts, and allow/deny outcomes. A sudden rise in denials, for example, may indicate changed inputs, policy behavior, configuration, or another operational condition; it does not by itself identify the cause or establish that the policy is stale.
Set alert conditions for your own environment, preserve the time window and relevant versions, and connect the metric signal to the detailed records a reviewer needs. See Google Cloud’s Monitor semantic governance policies for its product-specific metric definitions.
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How do I know if our policies are out of date?
Start with a reviewable inventory rather than asking an AI to pronounce policies current or outdated. For each policy, identify its owner, approved version, supporting sources or assumptions, agents that use it, and the date or event that should prompt review. Then look for concrete discrepancies: a supporting source has changed, deployed configuration differs from the approved version, or a documented behavioral signal crosses its threshold.
Require the reviewer to record the evidence considered, whether the policy remains valid, what changed if it does not, and who approved the correction. This creates a defensible decision trail while avoiding the unsupported leap from “the agent detected a change” to “the policy is wrong.”
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