AGENTIC INTELLIGENCE REMEDIATION ENGINE FRAMEWORK

Close the window on remediation.

AI proposes. Humans approve.
Automation executes. Evidence proves.

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SCROLL TO STEP INSIDE

01 / OVERVIEW

The patch is only part of the work.

The real challenge is coordinating risk, approvals and execution across a changing estate.

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WHAT SLOWS TEAMS DOWNHOW WE MOVE IT FORWARD

Fragmented context

Inventory, advisories and priorities live apart.

AI-assisted planning

Correlate exposure and propose a scoped plan.

Manual coordination

Approvals and maintenance windows cross teams.

Human-approved automation

Execute approved work within policy.

Proof assembled after the fact

Validation and audit evidence become extra work.

Validation with evidence

Verify the result and retain the record.

02 / WORKFLOW

A clear plan.
Before a single change.

AI agents connect the evidence and propose the next action. Your team decides what runs.

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A shared view of exposure

Illustrative example
Advisory contextConnect the advisory to affected products and software versions.

Start with the vendor advisory, CVE, or PSIRT notice and its applicability criteria.

Affected assetsMatch the advisory against inventory and current device state.

Use inventory context to identify affected assets and surface information that needs review.

Business priorityBring service criticality and ownership into the assessment.

Help teams prioritize the exposed estate using operational context, not severity alone.

Exposure ready for planning

AI connects the context so teams can make an informed decision.

Proposed remediation plan

Illustrative example
ScopeAffected assets, grouped by service criticality.

Propose eligible cohorts and a bounded change scope for human review.

Maintenance windowAligned to ownership and availability.

Include the proposed window and sequencing so teams can coordinate the work.

RollbackRecovery steps and validation checks included.

Define the checks, stop conditions, and recovery approach before execution.

Awaiting human approval

Approved plans run through your existing automation.

Human review and approval

Illustrative example
Review the changeInspect the scope, timing, and recovery plan together.

Keep the proposed work and its supporting context available in the ServiceNow change.

Apply the controlsCheck policy, blast radius, and required approvals.

Preserve CAB review, credential controls, and the human gates required for the change.

Record the decisionYour team approves the work that may run.

Approval authorizes the defined scope; the AI proposal alone does not trigger execution.

Human approval is the execution gate

AI recommends. Your existing change process remains in control.

Controlled patch execution

Illustrative example
Check readinessValidate device state before making changes.

Robot Framework runs pre-checks such as version, capacity, and service health against the planned requirements.

Run approved workAutomation orchestrates the patch within the agreed scope.

Ansible Automation Platform executes the approved cohorts and tracks progress through the existing automation.

Handle exceptionsApply the plan’s stop and recovery conditions.

Surface failed checks for operator attention and follow the approved recovery approach.

Execution stays within the approved plan

Automation performs the change; operators retain visibility and control.

Verified results. Retained evidence.

Illustrative example
Validate the resultCompare post-change checks with the expected state.

Robot Framework checks outcomes such as software version, interfaces, and routing health.

Attach the evidenceKeep validation results with the change record.

Retain execution reports and structured results so reviewers can trace what ran and what passed.

Close the loopRecord the outcome and update inventory state.

On successful validation, complete the ServiceNow change and update NetBox with the resulting state.

The outcome is supported by evidence

Evidence is captured as part of the workflow, not assembled afterward.

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03 / BUSINESS CASE

Put a value on the capacity you could unlock.

Model the engineering effort your team could redirect through governed automation.

Your patching scenario
3,000
90 min
15 min
4

Illustrative scenario

15,000 hours / year

Potential engineering capacity released

$1.275M / year

Modeled capacity value at $85 / hour

Based on 3,000 devices × (90 − 15) minutes × 4 cycles / year ÷ 60.

Capacity value is not cash savings.