What does an AI agent audit cover?

An AI agent audit covers responsibility, authority, execution, oversight, and evidence across the production system around an agent. It examines the agent population, named owners, delegated access, policy decisions, tool effects, human review, outcomes, incidents, changes, retention, and evidence integrity. The engagement scope defines the audited systems, period, population, and criteria before sampling begins.

Can KLA audit agents built with different frameworks?

Yes. KLA Control Plane governs existing agents across frameworks and providers through OpenTelemetry SDK checkpoints or the REST API. The audit record uses stable identities, policy decisions, action references, approvals, outcomes, and integrity fields across those integration surfaces.

How is an audit trail different from LLM observability?

LLM observability records model and application behavior for engineering operations. An audit trail binds each consequential action to identity, delegated authority, policy version, tool effect, human rationale, business outcome, and retention state. Hashes, signatures, and manifests let a reviewer test the integrity of that evidence.

What evidence does an internal auditor need?

An internal auditor needs a reconciled agent population, ownership and authority records, policy and release versions, sampled Execution Lineage, approval evidence, outcomes, incident and change history, and retention records. Each artifact needs a source system, extraction method, stable identifiers, timestamps, and integrity checks that support reperformance.

Does KLA replace an external auditor?

KLA prepares evidence for audits and does not replace the auditor. The auditor sets scope, tests evidence, evaluates exceptions, and reaches the audit conclusion. KLA provides reviewable operational records and Independent Verification for the evidence package.

How long does an audit-readiness assessment take?

Duration depends on the number of agents, system boundaries, audited period, and evidence availability. A focused assessment starts with one consequential Process, named owners, and a defined evidence population. KLA confirms scope and fieldwork timing before a facilitated review.

Enterprise audit readiness

AI Agent Audit Readiness

Audit every agent across ownership, authority, execution, oversight, and evidence.

A reviewable evidence record for every consequential agent action.

KLA Control Plane governs existing agents in place and produces reviewable operational evidence across frameworks and vendors.

Assess agent audit readiness Assess agent audit readiness Review sample evidence Review sample evidence

System boundary: agents, identities, tools, actions, approvals, and evidence.

01Audit layers

Five layers make every consequential action reviewable

Each layer pairs the auditor’s question with the evidence produced and the KLA surfaces that make it available.

Five AI agent audit layers mapped to audit questions, evidence, and KLA surfaces
Audit layer	Audit question	Evidence produced	KLA surfaces
01Responsibility	Who owns the agent, its outcomes, control operation, risk acceptance, and remediation?	Agent inventory, named business and control owners, responsibility matrix, release state, and mapped controls.	Agent Registry / Control Mapping
02Authority	Was the action within the agent’s delegated purpose, tool permissions, resource scope, and policy?	Authority snapshot, effective grants, tool and data-boundary references, policy version, verdict, and matched rules.	Tool Catalog / Data Boundaries / KLA Policy Engine
03Execution	What happened from intent through tool calls, state changes, and the business outcome?	Lineage Record, correlated tool requests and responses, before-and-after state hashes, side effects, and outcome.	Lineage Explorer / Audit Trail
04Oversight	Did an authorized reviewer receive sufficient evidence, exercise judgment, and address assurance findings?	Decision Request, reviewer authority, evidence presented, rationale, timestamps, Assurance Alerts, and remediation.	Decision Desk / Assurance Center
05Evidence	Can a reviewer test completeness and integrity across the audited population?	Reconciled manifest, hashes, signatures, chain of custody, retention metadata, and offline verifier results.	Evidence Room / Sealed Evidence Bundle

02Audit file

Your team receives a reviewable audit file

The assessment produces an evidence-backed view of the agent estate, representative actions, control operation, and readiness gaps.

Seven artifacts enter the audit file

01Agent and ownership inventory
02Permission and Tool Catalog review
03Policy and approval evidence
04Sampled Execution Lineage
05Assurance and incident findings
06Sealed Evidence Bundle or Control Pack
07Prioritized readiness gaps

Evidence record excerptMinimum audit schema fields

{
  "record_id": "evr_01K0A7Y9WJ2F4M",
  "occurred_at": "2026-07-15T09:47:12Z",
  "environment": "production-eu",
  "process_id": "credit-review",
  "journey_id": "jrny_8d21c",
  "correlation_id": "corr_4a912",
  "agent_id": "credit-review-agent",
  "agent_release_id": "rel_2026_07_15_4",
  "agent_identity_id": "agtid_73f1",
  "sponsoring_principal_id": "credit-ops-emea",
  "authority_snapshot_id": "authz_91bc",
  "data_boundary_id": "db_credit_eu",
  "tool_id": "core-banking.update-decision",
  "action": "write_decision",
  "policy_id": "credit-decision-policy",
  "policy_version": "4.2.1",
  "policy_decision": "require_approval",
  "matched_rule_ids": ["manual_review_threshold"],
  "decision_request_id": "dr_01K0A80D",
  "reviewer_role": "senior_underwriter",
  "decision": "approved",
  "outcome": "decision_recorded",
  "before_state_hash": "sha256:1a77…9b0e",
  "after_state_hash": "sha256:7cd2…5e41",
  "evidence_hash": "sha256:9e8c…44a1",
  "bundle_manifest_hash": "sha256:ab31…02df",
  "signature_key_id": "kla-eu-prod-07",
  "sealed_at": "2026-07-15T09:47:14Z"
}

Illustrative values. Field names match the audit schema used for stable identity, authority, execution, human decisions, outcomes, and integrity.

03Trust and deployment

Agents remain in place while KLA governs the action boundary

The deployment, data, authority, and evidence boundaries stay explicit from integration through Independent Verification.

BoundaryCustomer systemsKLA Control Plane

Govern in Place: Customer systemsExisting agents, models, tools, and downstream actions continue running in the customer environment.; KLA Control PlaneSDK checkpoints request policy decisions before consequential actions. Redacted OpenTelemetry spans flow asynchronously into the evidence pipeline.
Data handling: Customer systemsRaw business records, runtime credentials, and source-system state remain under customer controls for Govern in Place deployments.; KLA Control PlaneTenant-scoped agent metadata, policy and approval records, redacted Lineage Records, and integrity metadata enter durable storage. PII redaction occurs before persistence.
Integration surfaces: Customer systemsAgent frameworks, custom runtimes, model providers, internal tools, APIs, and data sources keep their current interfaces.; KLA Control PlaneOpenTelemetry SDK checkpoints and the REST API connect those surfaces to the Agent Registry, Tool Catalog, Data Boundaries, and KLA Policy Engine.
Action boundary: Customer systemsThe customer defines purpose, delegated authority, policy, reviewer roles, retention, and the business outcome.; KLA Control PlaneKLA evaluates the proposed action, routes any Decision Request, records the verdict and rationale, and correlates the resulting effect.
Evidence integrity: Customer systemsAuthorized reviewers reconcile the bundle with source-system outcomes and apply their own audit procedures.; KLA Control PlaneSealed Evidence Bundles carry a signed manifest, artifact hashes, signatures, and a Merkle root for offline Independent Verification.

04FAQ

Six answers define the audit engagement boundary

Scope, integration, evidence, timing, and auditor responsibilities are established before fieldwork.

What does an AI agent audit cover?: An AI agent audit covers responsibility, authority, execution, oversight, and evidence across the production system around an agent. It examines the agent population, named owners, delegated access, policy decisions, tool effects, human review, outcomes, incidents, changes, retention, and evidence integrity. The engagement scope defines the audited systems, period, population, and criteria before sampling begins.
Can KLA audit agents built with different frameworks?: Yes. KLA Control Plane governs existing agents across frameworks and providers through OpenTelemetry SDK checkpoints or the REST API. The audit record uses stable identities, policy decisions, action references, approvals, outcomes, and integrity fields across those integration surfaces.
How is an audit trail different from LLM observability?: LLM observability records model and application behavior for engineering operations. An audit trail binds each consequential action to identity, delegated authority, policy version, tool effect, human rationale, business outcome, and retention state. Hashes, signatures, and manifests let a reviewer test the integrity of that evidence.
What evidence does an internal auditor need?: An internal auditor needs a reconciled agent population, ownership and authority records, policy and release versions, sampled Execution Lineage, approval evidence, outcomes, incident and change history, and retention records. Each artifact needs a source system, extraction method, stable identifiers, timestamps, and integrity checks that support reperformance.
Does KLA replace an external auditor?: KLA prepares evidence for audits and does not replace the auditor. The auditor sets scope, tests evidence, evaluates exceptions, and reaches the audit conclusion. KLA provides reviewable operational records and Independent Verification for the evidence package.
How long does an audit-readiness assessment take?: Duration depends on the number of agents, system boundaries, audited period, and evidence availability. A focused assessment starts with one consequential Process, named owners, and a defined evidence population. KLA confirms scope and fieldwork timing before a facilitated review.

05: Readiness assessment

Start with one readiness assessment

Score the five audit layers, identify the evidence gaps, and establish the next reviewable action for your agent estate.

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