IncidentAgentCorrelates signals and proposes safe remediation
96
Product
AI specialists that understand ITSM, infrastructure, and cloud context
Design, ground, approve, and operate IT agents that work across NuralAI, ITSM tools, infrastructure telemetry, observability, cloud, and collaboration systems.
Autonomous AI Agents
Governed AI active
Agent control plane
Deploy specialists with graph context, approvals, and audit controls.
ChangeAgentScores blast radius and rollback readiness
88
KnowledgeAgentGrounds responses in approved runbooks
99
How it works
Built on NuralAI's shared AI, graph, workflow, and governance layer.
Every product shares the same operational graph, policy controls, integrations, AI agents, and audit model. That keeps context consistent from detection to resolution.
AI agent lifecycle
Graph-grounded
Policy-gated
Audit-ready
Design
Define the agent role, service scope, data sources, guardrails, and escalation boundaries.
Ground
Bind decisions to graph context, runbooks, knowledge, ownership, policy, and prior outcomes.
Test
Simulate production scenarios, confidence thresholds, rollback paths, and exception handling.
Approve
Route high-risk actions through human approval, change gates, and service-owner review.
Run
Operate continuously with telemetry, evidence capture, learning loops, and audit-ready history.
Shared NuralAI control plane
One model for agent design, operational context, governed execution, and evidence.
96confidence
100%audited
5stages
Capabilities
Enterprise-grade depth for real operations.
IncidentAgent
Investigates incidents, correlates signals, proposes fixes, and executes approved runbooks.
ChangeAgent
Scores risk, predicts blast radius, schedules safer windows, and rolls back when policies require.
KnowledgeAgent
Retrieves precise answers from runbooks, KBs, docs, and prior incidents.
FinOpsAgent
Finds cloud waste, rightsizing opportunities, and policy drift.
ComplianceAgent
Monitors controls and builds evidence packages for audit readiness.
Agent control plane
Govern identity, permissions, model usage, approval thresholds, and audit trails.
Customer outcomes
Proof buyers can inspect, defend, and share.
Package customer evidence by industry, workflow, stakeholder, and measurable business outcome so every evaluator sees the proof that matters to them.Financial services
Cloud waste remediation across regulated multi-cloud estates.
$2.3Menvironment-based ROI modelExplore use caseHealthcare
AI-assisted incident response for clinical system availability.
65%MTTR improvementExplore use caseManufacturing
Predictive service health across plant and enterprise systems.
78%downtime reductionExplore use caseResources
Guidance for evaluation and implementation.
Autonomous AI Agents overview for enterprise buyers
Built for CIO, IT operations, architecture, and security review.
OpenSee Autonomous AI Agents in action
Built for CIO, IT operations, architecture, and security review.
OpenImplementation and migration checklist
Built for CIO, IT operations, architecture, and security review.
OpenModel savings and payback
Built for CIO, IT operations, architecture, and security review.
OpenFAQ
Common evaluation questions.
How is NuralAI different from legacy ITSM?
NuralAI is built around AI agents, graph context, evidence-backed governed autonomous action, and governance rather than manual ticket queues alone.
Can NuralAI work with existing tools?
Yes. The platform story should emphasize connectors and workflow coexistence before full migration.
How are AI actions governed?
Agents operate within permissions, policy checks, confidence thresholds, approvals, and immutable audit trails.
How should proof be handled?
Publish only verified metrics, approved enterprise use cases, and documented security or compliance claims.
Platform AI Control Plane
The platform exposes how NuralAI senses, reasons, governs, acts, and proves.
NuralAI combines data ingestion, graph context, agent reasoning, policy engine, approval gates, execution connectors, audit/model trace, security boundaries, and deployment controls.
NuralAI AI RuntimeModel trace active
Tickets, alerts, telemetry, cloud events, IAM, cost, changes, and knowledge are normalized.
DataAgents use graph-grounded recommendations instead of isolated prompt responses.
AgentPolicy gates, approval thresholds, identity scope, and rollback plans control action.
PolicyEvery prompt, model result, human approval, connector call, and outcome is auditable.
Trace