A context graph is a permission-aware map of your organisation that links people, documents, tools, and activity over time. It gives AI agents the operational context they need to answer accurately and take the right actions—grounded in what’s relevant, current, and allowed—rather than relying on generic search or static knowledge bases.

Most “enterprise AI” fails in the same place: context.

Models can write fluently, but they don’t automatically know who owns a project, which policy is current, what changed last week, or which data a user is permitted to see. That’s why the idea of a context graph is getting real attention — it’s a way to give AI agents a living, permission-aware view of how work actually happens.

What is a context graph?

A context graph connects entities (people, teams, documents, tickets, dashboards, customers, systems) with relationships and activity signals (who edited what, which incident triggered which action, what got approved, what shipped, and when).

Unlike a static knowledge base, it’s designed to stay current. And unlike a simple search index, it captures relationships and process traces that agents can reason over.

Why context graphs matter for AI agents

An AI agent becomes valuable when it can do more than generate text — it needs to:

• find the right source of truth,

• interpret it in the right business context,

• respect permissions and governance,

• and take (or recommend) actions that match how your organisation really operates.

In Glean’s framing, “context” is becoming a new kind of data platform that blends content with activity and organisational signals so agents can deliver reliable outcomes across workflows.

Context graph vs knowledge graph (quick clarity)

A knowledge graph typically models “what things are” and how they relate conceptually.

A context graph builds on that by adding the operational layer: temporal activity traces, owners, approvals, tool interactions, and governance signals — the details agents need to act safely inside real enterprise systems.

What a context graph connects

A useful context graph usually includes four kinds of nodes and edges:

1. People and identity – org structure, teams, roles, expertise signals.

2. Content – documents, wikis, decks, chats, emails, tickets, code.

3. Systems and objects – projects, repos, dashboards, CRM accounts, incidents.

4. Actions and traces – edits, approvals, handoffs, deployments, escalations.

The goal is simple: when an agent answers a question or proposes an action, it can cite the path it followed — and your organisation can audit what happened.

How to build a context graph (practical approach)

You don’t “draw a graph” once. You build a pipeline that continuously assembles context.

1) Start with connectors that capture both content and activity

Content alone is rarely enough. You also need signals about what’s changing and how work flows between tools — the digital traces that show how decisions are made.

2) Resolve identity and permissions early

The most important feature of an enterprise context graph is not clever modelling — it’s permission awareness.

If your graph cannot faithfully inherit access controls from source systems (and keep them updated), the agent built on top of it will either be unsafe or useless.

3) Define your core entities and relationships

Keep this pragmatic. The first iteration should cover the entities you need for a real workflow:

• People, teams, roles

• Documents and policies

• Projects and tickets

• Customers or products

• Metrics and dashboards

Then add relationships that matter: ownership, membership, dependencies, approval chains, and “recently changed” links.

4) Add temporal signals and “what happened next” traces

This is where context graphs become more than a knowledge map. Capturing sequences (ticket → approval → deployment → incident) gives agents something they can reason over — not just search.

5) Make updates automatic

A context graph must be continuously refreshed — otherwise it becomes yesterday’s truth.

In practice, that means incremental updates from each connector, event streams where available, and re-processing only what changed.

6) Put governance and observability around it

Treat the context graph like a critical data product:

• logging and audit trails,

• drift detection (schemas, permissions, connectors),

• quality checks (stale sources, broken links, missing owners),

• and an operating model for change.

What this enables (real outcomes)

When you have a working context graph, you can build agents that:

• answer questions with grounded citations and permission-safe retrieval,

• summarise project status across tools without manual chasing,

• detect workflow bottlenecks (“where approvals stall”),

• route work to the right owner based on responsibility and expertise,

• and recommend the next step using precedent from past work.

Summary & next steps

Context graphs aren’t a buzzword. They’re the missing substrate that turns “AI that talks” into AI that understands your organisation — safely.

Next step: If you want help scoping the first workflow, designing governance, or connecting tools into a context-ready architecture, Generation Digital can help.

FAQs

What is a context graph?

A context graph is a permission-aware structure that links enterprise entities (people, documents, systems) with relationships and activity over time so AI agents can stay grounded and actionable.

Why are context graphs important for AI agents?

They reduce hallucinations and irrelevant outputs by giving agents the organisational context they need — including ownership, approvals, and the current source of truth.

How do context graphs maintain privacy?

By inheriting and enforcing permissions from source systems and only assembling context a user is authorised to access.

Can context graphs be updated in real time?

Yes. Effective implementations use incremental syncs or event-driven updates so the graph reflects changes quickly.

What systems benefit most from context graphs?

Organisations with many tools, complex approvals, and fast-changing knowledge — especially where accurate status, ownership, and compliance matter.