Incident context
Bring together tickets, monitoring events and change history. Classify incidents, suggest priorities and identify the responsible team.
L2/L3 AgentsNEW PRODUCT
Resolve complex IT incidents
with accountable AI
Second- and third-line agents help investigate incidents, identify likely causes and prepare a resolution. Give your team the context, recommendations and controlled execution they need, within explicitly agreed permissions.
For IT leaders, service teams and operations engineers
LPS / AGENT OPERATIONS
INCIDENT INVESTIGATIONP2
From signal to resolution
INC–0248payment-service
Context collectedLogs · metrics · change history
01Hypothesis preparedConnection pool timeout
02Recovery planAwaiting engineer review
03 Changes require approval
INTERFACE CONCEPT · SAMPLE DATA
AI agents for IT support
Less manual work.
More room for your team.
Root cause investigation
Correlate logs, dependencies and similar cases. Develop testable hypotheses linked to the available evidence.
Knowledge into action
Search your knowledge base and find relevant runbooks. Prepare resolution plans and updates for the incident record.
Controlled execution
Use approved runbooks and scoped permissions. Require approval for changes, record actions and escalate when confidence is insufficient.
A CLEAR PATH FORWARD
How it works.
Receive the signal
The agent receives a ticket or monitoring event and gathers the available context.
Investigate the cause
It compares symptoms with existing knowledge and proposes an evidence-based hypothesis.
Approve the resolution
An engineer reviews the plan. System changes follow the agreed execution policy.
Verify the outcome
The team confirms recovery. Findings and reusable knowledge are recorded in the ticket.
GOVERNED AUTOMATION
AI that works
within your rules.
Agree data sources, permissions, autonomy boundaries and quality criteria before implementation. Start with a focused use case and expand based on pilot results.
- Access controlOnly the permissions and sources needed
- Human approvalYour team defines autonomy boundaries
- Traceable decisionsContext, evidence and an activity trail
THE DETAILS MATTER
Good questions.
Let’s talk through your infrastructure and use cases.
Can an agent change production systems on its own?
Autonomy is defined for each use case. You can start with recommendations only. Execution is limited to assigned permissions and an agreed policy, with engineer approval for critical actions.
How does the agent connect to our infrastructure?
Discovery identifies your ticketing, monitoring and knowledge systems. We agree the integrations, deployment model and access boundaries before the pilot begins.
Where should we start?
Choose one service and a recurring incident category. Together we establish a baseline, escalation rules and quality criteria, then evaluate the workflow using historical cases and approved live tickets.
WHAT’S NEXT
Your next step
toward automation.
Tell us what you’re working on. We’ll explore the approach and define the first step together.