KLA vs Holistic AI
Holistic AI is positioned around EU AI Act readiness and governance workflows. KLA is the runtime control plane for agent workflows with evidence exports tied to real executions.
Tracing is necessary. Regulated audits usually ask for decision governance + proof: enforceable policy gates and approvals, packaged as a verifiable evidence bundle (not just raw logs).
For ML platform, compliance, risk, and product teams shipping agentic workflows into regulated environments.
Last updated: Dec 17, 2025 · Version v1.0 · Not legal advice.
Who this page is for
A buyer-side framing (not a dunk).
For ML platform, compliance, risk, and product teams shipping agentic workflows into regulated environments.
What Holistic AI is actually for
Grounded in their primary job (and where it overlaps).
Holistic AI is built for EU AI Act readiness and governance work: helping teams structure classification, readiness assessments, and stakeholder reporting across AI systems.
Overlap
- Both support audit readiness — Holistic through program structure and reporting, KLA through runtime decision evidence and exports.
- Both can be used together: governance dashboards for breadth, and workflow decision governance for depth in high-risk paths.
- Both align to “deliverables” thinking; the difference is whether deliverables are assembled from process declarations or generated from execution evidence.
What Holistic AI is excellent at
Recognize what the tool does well, then separate it from audit deliverables.
- Structuring EU AI Act readiness work (registration, classification, reporting).
- Helping stakeholders coordinate governance and assurance programs.
Where regulated teams still need a separate layer
- Decision-time workflow controls: policy checkpoints + role-aware queues for approvals and overrides.
- Evidence generation from actual executions (actions, approvals, sampling outcomes), not only declared processes.
- Verifiable export bundles (manifest + checksums) that map evidence to Annex IV deliverables for auditor handoff.
Out-of-the-box vs build-it-yourself
A fair split between what ships as the primary workflow and what you assemble across systems.
Out of the box
- Readiness and governance workflows (classification, registration, reporting).
- Dashboards and artifacts for communicating compliance posture to stakeholders.
- Program coordination across teams and systems.
Possible, but you build it
- Runtime capture of workflow execution evidence (actions, approvals, overrides) tied to production versions.
- Policy checkpoints that can block/review/allow high-risk actions in production.
- A packaged evidence export mapped to Annex IV/oversight deliverables with verification artifacts.
- Retention and integrity posture for long-lived audit evidence.
Concrete regulated workflow example
One scenario that shows where each layer fits.
EU AI Act readiness + a governed pilot workflow
A team completes readiness assessments and reporting across multiple AI systems. For a single high-risk agent workflow (e.g., claims payout recommendations), auditors still ask for runtime evidence: who approved, what policy applied, and how integrity is verified.
Where Holistic AI helps
- Coordinate readiness work, owners, and reporting across the organization.
- Generate dashboards and artifacts for program management.
Where KLA helps
- Enforce decision-time controls in the pilot workflow (checkpoints + approvals + overrides).
- Capture evidence from actual executions (including sampling outcomes) with policy/version context.
- Export a verifiable evidence pack for auditors and internal reviewers.
Quick decision
When to choose each (and when to buy both).
Choose Holistic AI when
- You need readiness reporting, dashboards, and program coordination across many systems.
Choose KLA when
- You need to govern agent workflows at runtime and produce evidence packs automatically.
- You need Annex IV-style documentation backed by execution evidence and integrity proofs.
When not to buy KLA
- You only need governance planning artifacts and are not yet shipping governed workflows.
If you buy both
- Use readiness tools to structure program work and ownership.
- Use KLA to generate runtime evidence and deliver exportable audit packs.
What KLA does not do
- KLA is not designed to replace governance program tooling for inventories, readiness assessments, and enterprise reporting.
- KLA is not a request gateway/proxy layer for model calls.
- KLA is not a prompt experimentation suite.
KLA’s control loop (Govern / Measure / Prove)
What “audit-grade evidence” means in product primitives.
Govern
- Policy-as-code checkpoints that block or require review for high-risk actions.
- Role-aware approval queues, escalation, and overrides captured as decision records.
Measure
- Risk-tiered sampling reviews (baseline + burst during incidents or after changes).
- Near-miss tracking (blocked / nearly blocked steps) as a measurable control signal.
Prove
- Tamper-proof, append-only audit trail with external timestamping and integrity verification.
- Evidence Room export bundles (manifest + checksums) so auditors can verify independently.
Note: some controls (SSO, review workflows, retention windows) are plan-dependent — see /pricing.
RFP checklist (downloadable)
A shareable procurement artifact (backlink magnet).
# RFP checklist: KLA vs Holistic AI Use this to evaluate whether “observability / gateway / governance” tooling actually covers audit deliverables for regulated agent workflows. ## Must-have (audit deliverables) - Annex IV-style export mapping (technical documentation fields → evidence) - Human oversight records (approval queues, escalation, overrides) - Post-market monitoring plan + risk-tiered sampling policy - Tamper-evident audit story (integrity checks + long retention) ## Ask Holistic AI (and your team) - Can you enforce decision-time controls (block/review/allow) for high-risk actions in production? - How do you distinguish “human annotation” from “human approval” for business actions? - Can you export a self-contained evidence bundle (manifest + checksums), not just raw logs/traces? - What is the retention posture (e.g., 7+ years) and how can an auditor verify integrity independently? - How do you demonstrate runtime enforcement and workflow decision evidence (not just program documentation) during an audit?
Sources
Public references used to keep this page accurate and fair.
Note: product capabilities change. If you spot something outdated, please report it via /contact.