What is a Context Lake?
A Context Lake is a graph-backed data substrate that aggregates operational data from across your engineering environment — cloud providers, code repositories, CI/CD pipelines, incident management, observability tools, ticketing systems — into a single queryable layer.
Unlike a data warehouse (optimised for analytics) or a data lake (optimised for storage), a Context Lake is optimised for real-time operational queries. The question it answers is: "what is the current state of my production environment, right now?"
The graph structure matters. Services have owners. Owners have on-call schedules. Services have dependencies. Dependencies have incidents. These relationships are what make context useful — flat data tables can't represent them.
Why AI agents need a Context Lake
When an engineer troubleshoots an incident, they pull context from memory and institutional knowledge: they know which team owns payment-service, they remember the deploy from Tuesday, they know the database has been flaky lately. This knowledge lives in their head — and it took years to accumulate.
When an AI agent troubleshoots an incident, it has none of that institutional memory unless it's provided explicitly. Without a Context Lake, agents face three failure modes:
- Stale context. The agent works from data that was accurate when it was inserted into the prompt but has since changed. It proposes actions based on a state that no longer exists.
- Incomplete context. The agent knows about the service but not about the active incident on its upstream dependency. It fixes the symptom, not the cause.
- No context. The agent has no access to production data and makes decisions based on general knowledge alone — which is not the same as knowing your specific environment.
What goes into a Context Lake
| Source | What it contributes |
|---|---|
| AWS / GCP / Azure | Resource inventory, health, costs, security groups, recent changes |
| GitHub | Repos, recent commits, open PRs, deployment events, code owners |
| CI/CD (GitHub Actions, etc.) | Build status, deploy history, test coverage trends |
| Kubernetes | Pod status, resource usage, events, service topology |
| Incident management | Open incidents, postmortems, on-call assignments, SLAs |
| Observability (Datadog, etc.) | Alerts, metrics, anomaly flags, SLO status |
| Jira / Linear | Open tickets, sprint state, escalations linked to services |
| Snyk / security tools | Vulnerability findings, policy violations, exposure severity |
Context Lake vs service catalog
A service catalog answers: what services do we have, who owns them, and how do they connect? It's an inventory.
A Context Lake answers: what is happening right now across our entire environment, and how does it relate to everything else? It's a live operational graph.
The service catalog is typically a node in the Context Lake — it provides ownership and dependency relationships that give live operational data its meaning. An alert on payment-service is more useful when you know the team, the on-call engineer, the recent deploys, and the open vulnerability from Snyk — all from the same graph query.
How the Context Lake serves both agents and engineers
The same data layer should power both the AI agent in your IDE and the dashboard your on-call engineer has open at 2am. If they have different views of production state, agents make decisions that contradict what the human sees — creating confusion and distrust in the system.
Shared context means:
- The agent proposes a rollback; the engineer can verify the recommendation against the same data the agent used
- Policy enforcement is consistent — the agent can't do something the console would block
- Audit trails reference the same entities — the service, the incident, the deploy — regardless of who or what triggered the action
Frequently asked questions
Is a Context Lake the same as a knowledge graph?
Related but not identical. A knowledge graph is a general data structure for representing entities and relationships. A Context Lake is specifically an operational knowledge graph optimised for real-time production queries — with a bias toward current state rather than historical analysis.
How is a Context Lake different from a data lake?
A data lake stores large volumes of raw data cheaply, optimised for batch processing and analytics. A Context Lake stores operational relationships, optimised for low-latency queries about current state. Different workload, different architecture.
Do I need to build a Context Lake myself?
You can, but it requires substantial integration work — connectors for each source, a graph database, query APIs, and a freshness strategy. Platforms like Exemplar provide a Context Lake as part of the product, pre-integrated with common engineering tools.
How fresh does context need to be for agents?
Depends on the task. For incident response, context should be near-real-time (seconds to minutes). For cost optimisation, hourly or daily is usually sufficient. The freshness strategy should match the time-sensitivity of the workflows you're enabling.
Related: what is agentic DevOps, agents, context, and guardrails, what is MCP.