Cloud inventory / Infrastructure autodiscovery
Map the systems your teams actually operate.
Discovery AI Agent is OrbOps AI's cloud inventory and infrastructure autodiscovery agent. It maps resources, services, dependencies and ownership from connected sources, then exposes uncertain relationships for a platform owner to validate before they guide operational decisions.
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
Cloud inventory that explains dependencies and ownership.
Evaluate Discovery AI when an asset list cannot explain which service a resource supports, who owns it or what a proposed change may affect. Inventory becomes useful operating context when those relationships can be checked. OrbOps reports testing inventory and autodiscovery on AWS, Google Cloud (GCP) and Microsoft Azure. Private-beta coverage depends on connected accounts, source permissions and resource types; confirm the discovery scope for your environment. A cloud name does not prove complete inventory coverage. Missing permissions, stale evidence and unresolved ownership must remain visible. Discovery proposes a map; it does not authorize deleting an apparently unused resource or replacing an approved CMDB record.
Cloud asset inventory
Relate discovered resources to services without treating an unconnected account as an empty estate.
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 cloud inventory and infrastructure discovery.
How Discovery AI builds a current operating map while keeping weak, conflicting, or uncertain relationships open for human validation.
What is AI infrastructure autodiscovery?
Discovery AI Agent is OrbOps AI's cloud inventory and infrastructure autodiscovery agent. It maps resources, services, dependencies and ownership from connected sources, then exposes uncertain relationships for a platform owner to validate before they guide operational decisions.
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.
Can Discovery AI build cloud inventory across AWS, Google Cloud and Azure?
OrbOps reports testing inventory and autodiscovery on AWS, Google Cloud (GCP) and Microsoft Azure. Private-beta coverage depends on connected accounts, source permissions and resource types; confirm the discovery scope for your environment. A cloud name does not prove complete inventory coverage. Missing permissions, stale evidence and unresolved ownership must remain visible. Discovery proposes a map; it does not authorize deleting an apparently unused resource or replacing an approved CMDB record.
Understand the workflow and evaluate the evidence.
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