Context Graphs: Understanding Real Enterprise Processes to Unlock the Next Generation Data Platform for Agentic Automation
If I asked you, “What is the actual process for signing a new contract at your company?” you might answer, “Oh, Sales submits a request, Legal reviews it, and then a leader approves it.” But that’s the “should” written in the policy manual. The reality is often this: Salesperson Zhang updates the deal stage in Salesforce, then messages Legal Specialist Li on Slack with a link to the latest Google Doc. Li leaves comments, schedules a calendar invite pulling in Product and Finance, the meeting notes are updated in Notion, and the final approval triggers an automated email. Throughout this, the “how” of the work is far more complex than the “should,” and is never fully documented.
This is the core bottleneck facing enterprise automation today. RPA and workflow tools can automate only those processes that are clearly defined and structured. The majority of work in an enterprise, however, is done in a distributed manner by individuals and small teams, its “process” existing only in employees’ heads and across fragmented communication tools—what’s known as “tribal knowledge.”
A fundamental question emerges: If we want AI agents to learn and automate this true mainstream work, what kind of “cognitive foundation” do we need to provide them?
The answer: A data platform that understands how your company actually works. And the core of this platform is increasingly being called the “Context Graph.”
Why “Context” is the Next Data Platform
Over recent decades, enterprise digitization has focused on data. Decisions and structured data are captured in Systems of Record (like ERPs, CRMs). Daily collaborative work unfolds across “Systems of Engagement” like communication tools (Slack), project management software (Jira), and code repositories.
We have vast amounts of data, but data does not equal understanding. An AI agent with access to all company databases and documents might still not know:
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When a critical server failure occurs for Customer A, who should be contacted? And who actually collaborates to handle it? - •
To approve a cross-department procurement contract, how many approvals are theoretically required? Where does it realistically get stuck, and due to what communication gap?
Traditional enterprise data platforms were designed for reporting and analytics. The next-generation data platform is designed for observation, learning, and automation. Its mission is not to show “what happened,” but to understand “how things happen,” and based on that, enable agents to reliably advance work.
This platform no longer needs isolated databases but a unified context layer. It sits across all your applications, data warehouses, and systems of record, integrating a shared understanding of enterprise data (structured and unstructured), relationships, and most critically, workflows.
Context Must Evolve as AI Advances
Initially, we believed great search was the foundation of context. This meant understanding content: indexing unstructured data so employees could search across the enterprise to quickly find the latest design doc, policy update, or customer record. It solved the “where is the information” problem.
But as AI begins to take on more complex work, we’ve learned this foundation must expand. Understanding enterprise data alone is insufficient; systems also need relational knowledge.
How work gets done in an enterprise is fundamentally relationship-driven. You need to know who owns an account, who approves a certain contract, which engineer is on call, or which teams collaborate when an incident escalates. Without this relational fabric, AI is like an alien with a library card but no understanding of social rules—unable to act effectively.
Context graphs are born precisely to capture this dynamic reality where data and relationships intertwine.
What Exactly is a Context Graph? It Captures the “How,” Not the “Why”
A crucial refinement regarding context graphs is this: “You can’t reliably capture the ‘why’; you can capture the ‘how.’”
The “why” is often a thinking step residing in someone’s head—you cannot actually model it. It might be hinted at in a meeting transcript or a Slack thread, but most of it is never recorded in a clean or durable way.
The “how,” on the other hand, leaves a rich digital trail: recurring steps, data updates, approvals, collaboration patterns, field changes, and cross-system behavior over time. Over many cycles, these process traces can approximate the “why”: you can infer rationales from patterns in how work is repeatedly done, not from trying to literally store every human thought.
Therefore, the goal of a context graph is to capture the “how” (the process) now and learn the “why” (the intent) over time. If agents are to automate real enterprise work, the path is to deeply model processes to understand the conditions under which work proceeds, pauses, or escalates. Then, the next time a similar situation arises, the agent can infer the correct actions to take.
Building Context Graphs: A Significant Technical Investment
Creating this level of knowledge and understanding is not simple. Building enterprise-grade context graphs is challenging and requires a stack of technologies working in concert.
1. Observability: Breadth and Depth Across Connectors & Apps
Gaining a full understanding of what happens in an enterprise requires more than clean, structured decision data from systems of record. It demands observability across the connectors and applications where work actually happens—both the breadth to capture activity across the many tools employees use and the depth to extract meaningful signals from each connector.
For example, a Salesforce connector may expose a deal stage change. True observability comes from also seeing related activity: a document edited in Google Docs, a message sent in Slack, a meeting created in Calendar, or a record updated in another system. These must be captured directly from each underlying system via its dedicated connector.
2. Understanding Activity Data: Starting with Atomic Events
Beyond indexing content, systems must capture low-level activity signals: discrete, timestamped actions taken within tools. These include events like a document edit, a field update, a comment added, a Slack message sent, or a file uploaded. Capturing these actions in chronological order and tracking state changes between them provides the raw activity data.
3. Deriving Higher-Level Understanding: Aggregating Tasks, Projects & Initiatives
Only after collecting this atomic activity data can systems begin to infer higher-level constructs. Patterns across many low-level actions—repeated document edits, coordinated Slack messages, frequent updates to the same records—combined with semantic understanding can be aggregated to indicate a task, a project, or a broader initiative.
For instance, a sequence of document creation, edits, Slack messages, and cross-system record updates over several days may collectively represent a customer onboarding or product launch, even if that work was never explicitly labeled as such in any single system.
Separating signal from noise is difficult, especially in an enterprise. Taking Glean as an example, their task understanding reaches approximately 80% accuracy—an indicator of how strong all the upstream technology needs to be to make this viable. This is more impressive given that enterprise context graphs aren’t built at internet scale. Data cannot be aggregated across customers, and the resulting datasets are both smaller and inaccessible to humans due to privacy constraints, requiring the graphs to be inferred algorithmically.
Context Graphs: A Core Component of the Next-Gen Data Platform
While context graphs are currently receiving significant attention, solving for context cannot rely on a single technology. Achieving true process understanding requires a collaborative technology stack:
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Connectors: To observe activity across tools. - •
Indexes: To enable fast retrieval. - •
Graphs: To model enterprise structure and relationships. - •
Memory: To capture what happens when agents actually execute work.
It is this stack that enables systems to evolve from raw enterprise data to actionable agents.
As agents begin to operate within the enterprise, learning becomes essential. What works for humans doesn’t always translate directly to agents. By capturing execution traces—how agents use tools, in what sequence, and with what outcomes—systems can learn from agentic work in practice. These traces form enterprise memory, capturing what actually works for agents over time.
Process understanding doesn’t come from the context graph alone; it emerges from the combination of structural understanding (the graph) and learned behavior (the memory).
When you step back and view all these layers together—connectors, indexes, graphs, and memory—you realize an entirely new data platform has been built. This platform is designed not for reporting or analytics, but as the backbone for agentic automation: a system that observes how work happens, learns from practical execution, and enables agents to reliably carry work forward across the enterprise.
FAQ: Common Questions About Context Graphs
Q: What’s the difference between a Context Graph and a Knowledge Graph?
A: A Knowledge Graph primarily focuses on entities (people, products, places) and the static relationships between them (e.g., “who manages whom,” “which product belongs to which department”). A Context Graph emphasizes dynamic activities and processes. It includes not just the “who” and “what,” but critically captures the “how”—how work is accomplished through a series of activities spanning different tools and people. It is essentially a digital mapping of workflows.
Q: Will the Context Layer replace existing Data Warehouses or Data Lakes?
A: No, it will complement and collaborate with them. Data Warehouses and Lakes excel at storing and batch-analyzing historical data. The Context Layer is a real-time, dynamic layer focused on understanding current activity, relationships, and process states. It ingests information from these existing data sources (and engagement tools), adding the dimension of “how it happens,” collectively forming the next-gen data platform that powers agentic automation.
Q: How do agents utilize Context Graphs for learning?
A: Primarily on two levels. First, the context graph provides the agent with a “map” and “rulebook” for action, informing it about the company’s people, data relationships, and existing process patterns (structural understanding). Second, once an agent begins to act, its execution traces (sequences of successful or failed operations) are captured and fed back into the system, forming “enterprise memory.” By analyzing these traces, the system can optimize process models, enabling the agent to make better decisions in future similar situations (learned behavior).
Q: What are the biggest challenges for an enterprise starting a Context Graph initiative?
A: Based on the source material, three main challenges exist: 1. Technical Complexity: Establishing observability across a wide range of tools and accurately inferring high-level tasks from low-level activity data is difficult (achieving ~80% accuracy is noted as a significant accomplishment). 2. Data Privacy & Isolation: Enterprise data cannot be aggregated across customers and involves sensitive information, requiring graphs to be built algorithmically under strict privacy constraints. 3. Cultural Shift: This is not just an IT project but a deep understanding and reshaping of how work is done, requiring deep involvement from business units.
Q: Is this just another term for “Agent Memory”?
A: Related, but not equivalent. Agent memory focuses more on storing an individual agent’s interaction history and experiences. A Context Graph has a broader scope. It forms the “world model” upon which agent memory relies. It encompasses the enterprise’s static structure, dynamic processes, and the shared environment for collaboration with other agents or humans. One could say the Context Graph is the public, shared “enterprise memory bank,” while an individual agent’s memory is its private operational experience based on that context.
Conclusion: Context is Foundational for Agentic Work
Returning to the initial question: How do we enable agents to successfully complete work within the enterprise? How can they learn, understand, and operate like your company does?
The answer is becoming increasingly clear: This opportunity depends on a context foundation. This foundation understands your enterprise data, your relationships, and most importantly, your actual work processes.
Context graphs are not just another trending tech concept. They represent a crucial evolution in our understanding of enterprise informatization: from recording the “what” to understanding the “how.” They are shaping not a better reporting system, but an intelligent systems platform capable of observing, learning, and ultimately autonomously driving business forward. This is the next-generation data platform, taking shape right before our eyes.
