The checkpoint your AI agents
pass through first
When an agent moves money, changes a record, or messages a customer, KLA Runtime evaluates the action before it commits — policy-as-code, human approval where the stakes demand it, and signed lineage of the governed decision path. Govern in place or run through KLA; either way, compliance becomes a byproduct of execution.
What KLA Runtime actually does
KLA is not an agent builder and it is not a documentation warehouse. It is the runtime layer that helps existing AI systems earn permission to run in production.
Live intercept path
Request, decision, escalation, downstream execution, signed lineage.
- An agent requests a tool call, API write, customer response, or internal workflow transition.
- KLA evaluates policy, identity, business thresholds, and runtime context.
- The system allows, blocks, or routes the action to a human reviewer.
- The decision and outcome are written into signed execution lineage.
decision = checkpoint.evaluate( actor="treasury-copilot", action="wire_transfer.create", policy="payments.high_value_requires_human", ) if decision.status == "requires_human_review": route_to_approver(decision) elif decision.status == "approved": execute()
Policy-as-code checkpoints
Evaluate identity, risk tier, tool access, thresholds, and workflow context before an agent acts.
- Block over-permissioned actions before downstream systems are touched
- Centralize runtime policy without forcing one application architecture
- Apply the same controls across pilots, production, and shadow AI catch-up work
Decision routing
Escalate high-stakes decisions to the right reviewer with the exact context they need to approve or reject quickly.
- Maker-checker flows for payments, claims, releases, and customer-facing actions
- Approver identity and decision reason bound to the execution record
- Slack, email, queue, and internal workflow integration patterns
Continuous assurance
Measure the runtime behavior of governed AI instead of relying on self-attested design documents.
- Near misses, approval latency, blocked actions, and drift surfaced in one place
- Model, tool, and workflow metadata attached to every governed decision
- Useful to platform, risk, security, and audit without building four separate systems
Execution lineage
Every governed action produces signed, queryable lineage that can later be mapped to internal controls and external frameworks.
- Action, policy decision, reviewer, downstream effect, and retention state in one bundle
- Framework mapping happens after capture, not instead of capture
- Turns compliance into the byproduct of runtime control
Deployment patterns for real stacks
The first question technical evaluators ask is whether KLA forces a re-platform. It does not. Choose the control surface that matches your architecture.
Govern in place
Instrument existing frameworks with SDKs or OpenTelemetry and insert checkpoints around the moments that matter.
- Best when you already have agent frameworks, queues, and workflow engines in place
- Lightweight integration that works with your existing infrastructure
- Keeps KLA focused on control, approval, and proof
Run through KLA
Adopt a managed execution path when you want KLA to own more of the runtime surface for fast standardization.
- Useful for greenfield workflows or teams consolidating fragmented automation
- Gives a tighter control surface with less local integration work
- Still preserves the same policy, approval, and lineage model
Compliance as a byproduct of runtime control
Framework mappings, trust artifacts, and regulatory reporting matter — but they work best when generated automatically from governed execution, not assembled manually after the fact.
Reuse the same lineage for every review
Once KLA governs a runtime action, the resulting lineage can support internal controls, trust reviews, Annex IV packages, quality-system documentation, and audit preparation without asking engineering to reconstruct history later.