Shared agents
AI Evals
The quality gate for AI agents, built into your Agent Development Lifecycle.
- AI Quality & Reliability
//Knowledge Graph
AI is only as powerful asthe context behind it.
The Software Delivery Knowledge Graph gives AI agents a continuously updated, relationship-aware model of your delivery environment, so automation aligns with reality.
Integrations
Connect the tools you already use
Start with Git and CI/CD. Layer in cloud, telemetry, security, and cost data as use cases expand.
Identity
One entity. Many names. One truth.
Teams keep their naming conventions; the graph matches entities across systems.
Reasoning
Why did it fail, and what to do about it
Agents traverse artifact, deployment, environment, policy and access to answer why, in seconds.
//Overview
What is a software delivery knowledge graph?
A software delivery knowledge graph is a continuously updated model of the entities in your delivery environment, such as services, builds, deployments, incidents, owners and costs, and the relationships between them.
DataObserve Developer's Knowledge Graph connects your existing tools, resolves one entity known by many names into one record, and exposes a semantic layer that agents use to generate pipelines, find root causes, roll back safely and trace cost anomalies.
//From raw data to reasoning
A semantic layer betweenyour tools and your agents.
Git, CI/CD, K8s, cloud billing, incidents, policies
Data sources
Every tool in your delivery environment feeds the graph.
//From AI-assisted to AI-operational
The difference between helping and operating
Pipeline generation
Pipelines aligned to org standards and governance rules, not guesswork.
Root-cause analysis
The full dependency chain traversed automatically, with no manual correlation across tools.
Safe rollbacks
Validated against the downstream dependency graph first.
Cost anomaly tracing
Spend spikes traced to the deployment decision that caused them.
//Explore more withDataObserve
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Resilience Testing
Agents predict where resilience will break, recommend the fix, and generate the test that proves it.
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Runtime Configuration
The runtime management platform.
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//Where initiatives fail
Where knowledge graph initiatives fail
Modeling everything before solving anything
Pick one or two use cases first, and model only the 10 entities that answer one real question.
Missing the relationships that create value
Map the relationships your use case needs, like a service's owning team, before expanding.
Perfectly modeled data, a week old
During an outage you need to know why it's failing now. Near real-time sync is non-negotiable.
//FAQs
Knowledge Graph: questions teams ask
Why do AI agents need a knowledge graph?
Agents act on context. A graph tells them how services, changes and owners relate, so their actions fit how your systems actually work.
How current is the knowledge graph?
It updates in near real time from connected tools, because during an outage week-old data can't explain what is failing now.
Do we need to model everything first?
No. Start with one or two use cases and the entities they need, then extend the graph as more agents use it.
What is a semantic layer?
A semantic layer gives agents consistent, business-friendly definitions of the entities in the graph, so they ask for 'the payments service' rather than raw IDs.
