Live intercepts for AI agent actions
A live intercept is a decision point placed between an agent’s proposed action and the system that would execute it. The intercept normalizes the action, evaluates authority and policy, and enforces the result early enough to control the side effect.
KLA Runtime can govern existing agents through SDK checkpoints or receive execution through a managed endpoint. Both patterns apply a decision before the tool call and correlate the resulting effect.
- 01Agent intentExport customer dataset
- 02Live interceptdata.export · production-eu
- 03Enforced outcomeblock · boundary exceeded
- 04Target systemNo export request received
- Control point
- Before the side effect
- Integration
- SDK checkpoint or Executions API
- Decision inputs
- Identity, authority, policy, and context
- Correlated output
- Tool result or prevented action
01: Concept
How live intercepts works in operation
An intercept becomes a reliable control when its action boundary, failure behavior, and evidence contract are explicit.
A live intercept is a decision point placed between an agent’s proposed action and the system that would execute it. The intercept normalizes the action, evaluates authority and policy, and enforces the result early enough to control the side effect.
- Place the checkpoint at the side effect
- Useful boundaries sit immediately before tool calls, writes, payments, messages, Process transitions, and access to sensitive data.
- Describe the proposed action consistently
- KLA evaluates a stable Decision Request containing the actor, action, resource, parameters, environment, and business context.
- Enforce the outcome in the request path
- Allow continues, warn continues with a recorded signal, require approval holds, and block stops the governed action before the target system is called.
- Correlate the downstream effect
- The tool response and resulting state attach to the same Lineage Record so operations can verify the control and the actual outcome.
02: KLA implementation
How KLA implements live intercepts
KLA supports two integration patterns that share the same policy, approval, and evidence model.
- 01
Instrument the action boundary
Govern in Place adds SDK checkpoints to existing agent code. Run through KLA sends execution through the managed runtime surface.
Output · Governed boundary
- 02
Open a Decision Request
The checkpoint sends a normalized action and its runtime context to the KLA Policy Engine.
Output · Correlated request
- 03
Apply the runtime outcome
The integration continues, signals, holds, or stops the action according to the returned policy outcome.
Output · Enforced decision
- 04
Close the execution record
KLA links the tool response, human decision when present, resulting state, timing, and integrity metadata.
Output · Completed Lineage Record
Example · Customer data export
A prohibited export ends at the action boundary
An operations agent requests a bulk export that includes records outside its approved regional Data Boundary.
The warehouse receives no export request. The agent receives a structured block outcome and a remediation reason it can use to narrow the request.
- Tool call proposedreceived
warehouse.export · 18,420 customer records · production-eu
- Boundary check failedblocked
1,308 records outside data-boundary-eu-customer-support
- Action blockedblocked
warehouse.export prevented · remediation returned to agent
- Intercept recordedrecorded
Policy, authority snapshot, parameters, and prevented outcome sealed
04: Evidence record
What KLA records for review
The intercept record proves which action was proposed, which control evaluated it, and whether any side effect reached the target system.
| Record layer | Captured evidence | Review purpose |
|---|---|---|
| Proposed action | Agent, tool, operation, parameters, resource, and business context | Define the exact side effect requested |
| Runtime boundary | Tenant, environment, Process, Release, checkpoint, and correlation IDs | Locate the intercept in the deployed execution path |
| Control decision | Authority snapshot, policy version, outcome, reason codes, and timing | Show how the runtime resolved and enforced the action |
| Operational effect | Tool invocation state, response reference, before and after state, or prevented outcome | Verify what reached the downstream system |
05: Connected controls
Follow the complete governed action path
The four concepts operate together across one action. Continue with the control layer closest to your next question.
06: FAQ
Questions about live intercepts
Definitions, runtime behavior, integration, and evidence boundaries for this control layer.
- What is a live intercept for an AI agent?
- A live intercept is a runtime decision point between a proposed agent action and the tool or system that would execute it. It evaluates the action and enforces an allow, warn, hold, or block outcome before the side effect.
- How is a live intercept different from observability?
- Observability records system behavior for analysis. A live intercept participates in the execution path and can control a proposed action before it reaches the downstream system. KLA also records the intercept as Execution Lineage.
- Where should a team place checkpoints?
- Place checkpoints immediately before consequential boundaries such as tool calls, database writes, payments, outbound messages, Process transitions, and sensitive data access.
- Can KLA govern an existing agent in place?
- Yes. Govern in Place uses SDK checkpoints around selected actions in the current runtime. Teams can also Run through KLA using a managed execution endpoint. Both patterns use the same policies and evidence model.
- What happens when the KLA Policy Engine returns block?
- The integration prevents the governed tool call or side effect and returns a structured outcome with reason codes. The blocked request and prevented outcome remain in the Lineage Record.
Start with one action
Find the action boundary that needs a live intercept
Trace one consequential Process from agent intent to downstream effect and place the first KLA checkpoint with the team.