System and ownership intelligence
Map the systems your teams actually operate.
AI infrastructure autodiscovery continuously maps the services, dependencies and ownership across your cloud accounts, then flags the relationships it is not confident about for a human to confirm. Discovery AI keeps uncertain links visible before they influence automation.
Discovery AI Agent is one of nine specialist agents in O2 AI, the agentic operations platform from OrbOps AI.
Discovery path
The operating map expands, then asks for validation.
Discover
Find systems, tools, services, and infrastructure signals.
Connect
Map dependencies, ownership, and delivery relationships.
Validate
Ask teams to confirm uncertain or operationally important links.
Living topology
A living operating map with uncertainty exposed.
The agent connects evidence from cloud accounts, repositories, delivery systems, identity, and observability into a readable topology. Platform teams can trace service paths, deployment targets, and ownership gaps while seeing which relationships are measured, inferred, conflicting, or still waiting for validation.
System map
Create a readable view of services and infrastructure.
Dependency context
Connect delivery and runtime relationships across tools.
Ownership gaps
Surface services that need a validated accountable owner.
What it discovers
The services, paths, and ownership behind the inventory.
Infrastructure autodiscovery combines cloud, repository, delivery, identity, and observability signals. The result is more than an asset list: it is a proposed operating map that shows how resources may support a service and where the evidence remains incomplete.
- 01
Compute workloads
Virtual machines, containers, Kubernetes workloads, serverless functions, and the environments where they run.
- 02
Managed services
Databases, queues, caches, object stores, gateways, and provider-managed services connected to an application path.
- 03
Networking
Ingress, egress, load balancers, private routes, public exposure, and the network relationships between services.
- 04
IAM relationships
Service identities, assumed roles, permission paths, and ownership signals that influence how workloads communicate.
- 05
Deployment targets
Repositories, pipelines, runtime destinations, release environments, and the delivery tools associated with each service.
- 06
Orphaned resources
Resources with missing ownership, weak dependency evidence, stale deployment context, or no validated operating purpose.
Uncertainty stays visible
A useful map shows what it does not know.
Many discovery tools turn weak signals into a confident diagram. Discovery AI keeps evidence, inference, and validation separate. A missing owner or ambiguous dependency remains unresolved until an accountable team confirms it, so an uncertain relationship cannot silently guide an incident, release, cost, or provisioning decision.
OBSERVED
Runtime traffic
The payments API publishes to a queue and a worker consumes from it.
PROPOSED
Service dependency
API → queue → worker appears to be one operating path.
UNRESOLVED
Conflicting ownership
Repository and cloud tags point to different teams.
Human checkpoint
The proposed dependency can support the map after validation; the owner remains unknown until the payments or platform team confirms responsibility.
Validated context in use
The map becomes useful when another workflow can trust it.
Discovery is not the final action. Its role is to give specialist agents and platform teams a shared, evidence-backed view of the environment while preserving the validation state of every important link.
Incident response starts with a dependency path
Responders can see which services appear connected, which team may own the path, and which relationship still needs confirmation before it shapes an investigation.
Explore Incident ManagementFinOps gains service and ownership context
Cost signals become more useful when a cloud resource can be related to a workload, environment, dependency, and accountable owner instead of remaining an isolated billing line.
Explore FinOpsInfrastructure plans start from what already exists
Platform teams can compare a request with current services, network paths, deployment targets, and ownership before preparing another resource or environment.
Explore Infrastructure RequestsAutodiscovery FAQ
Questions about topology, dependencies, and ownership.
How Discovery AI builds a current operating map while keeping weak, conflicting, or uncertain relationships open for human validation.
What is AI infrastructure autodiscovery?
AI infrastructure autodiscovery continuously maps the services, dependencies and ownership across your cloud accounts, then flags the relationships it is not confident about for a human to confirm. Discovery AI organizes cloud, repository, delivery, identity, and observability signals into a topology that other operational workflows can use.
How does it find service dependencies and owners?
Discovery AI connects available cloud, delivery, repository, identity, and observability evidence to propose how services depend on one another and who may own them. It shows the evidence behind important links and marks missing, conflicting, or ambiguous ownership. Platform teams can then confirm or correct the operating map.
How are incorrect relationships handled?
An uncertain relationship remains a proposal when supporting signals conflict or stay incomplete. Discovery AI marks important links for platform-team validation and preserves confirmed corrections as operating context. This prevents an unverified topology assumption from silently influencing incident, release, cost, security, or provisioning decisions.
How does autodiscovery improve AIOps and DevOps workflows?
Infrastructure autodiscovery supplies shared service, dependency, deployment, environment, and ownership context to incident response, cost analysis, release planning, and infrastructure requests. That common map helps specialist agents reason about the same operating environment. Teams still validate relationships that could materially influence automation or production decisions.
What is the difference between infrastructure autodiscovery and a CMDB?
Infrastructure autodiscovery continuously proposes a current map from connected operational evidence, while a CMDB manages approved configuration records and organizational processes. Discovery AI can help identify changes, gaps, and candidate relationships for a CMDB, but it does not treat every observed signal as an approved record or automatically replace CMDB governance.
How often does the infrastructure map refresh?
The infrastructure map refreshes according to the connected source and its available event or polling cadence rather than one universal interval. Each relationship should retain source freshness and validation state. Important changes can update the proposed map quickly, while uncertain dependencies and ownership still wait for accountable human confirmation.
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