Think of the last time a complex decision was made in your company. Maybe a sales VP approved a steep discount for a renewal. If you look in your CRM today, all you’ll see is the final price. What you won’t find is the “why” that led to it. You won’t see the Slack message about the client’s budget freeze, the ticket regarding their service outage, or the email thread debating the exception.
The reasoning – the "decision trace" – remains invisible to everyone not personally involved in the negotiation.
Now, imagine asking an AI agent to handle that renewal next year. Without that essential context, your agent would blindly follow your pricing rulebook and reject the discount, potentially losing you your hard-won client. This amnesia problem is a key obstacle for successful enterprise AI deployments and autonomous AI systems.
So, what if there was a way to give agents a perfect memory of not just what happened, but also why it happened? A knowledge graph that captures the logic, exceptions, and human judgment behind every business action?
That system is a context graph – the missing infrastructure likely to play an oversized role turning simple automation into truly intelligent, agentic workflow orchestration.
What is a Context Graph? Beyond Static Data
At its core, a context graph is a living record of decision traces stitched across time and systems. While traditional databases store the end state (the final result: "Invoice Paid"), a context graph stores the "trace" (the journey: "Invoice paid after VP override due to service delay").
In the era of agentic AI, this distinction is everything. Just like junior employees, AI agents don't just need access to data; they need access to precedent in order to de-risk AI decision making. Here’s why:
- Rules tell an agent what should happen in general.
- Context graphs tell an agent what actually happened in specific cases, capturing the nuance of how humans in the loop resolved ambiguity.
Technically, this can be achieved by combining a process graph, a form of knowledge graph that maps the relationships between tasks, decisions, and dependencies, with an immutable audit log, which records the step-by-step reasoning of every action. Together, they form a queryable history of enterprise decision-making that can power agentic workflow orchestration.
Why This Matters: The Brain of Agentic AI
For agentic AI to survive in the enterprise, it will need to move beyond simple tasks to complex workflows that ensure rigorous AI security and governance. The context graph is a key enabler for this shift.
- Transparent Traces: Current AI agents can be opaque, making decisions that are impossible to trace or defend. A context graph provides transparency and explainable AI, allowing you to audit exactly why an agent took a specific action.
- Turning Exceptions into Precedent: In the real world, business runs on exceptions. By capturing the trace of a human decision, the context graph allows future agents to learn from that precedent rather than getting stuck.
- Regulatory Survival: In industries like banking or healthcare, you cannot deploy autonomous agents without proof of compliance. A context graph acts as an automated defense, providing verifiable evidence of compliant AI needed to satisfy regulators.
Under the Hood: Where the Context Graph Sits within the Orchestration Layer
Building a context graph requires a fundamental shift in the architecture of your enterprise GenAI. It doesn't happen in the database; it happens in the orchestration layer, where most of the work actually gets done.
- The Map (Knowledge Graph): First, the system models the business workflow; not as a flat list of tasks, but as a dynamic network of steps, rules, and dependencies. These workflows ensure the agent understands the shape of the process.
- The Capture (Orchestration): As the agent executes a task, triaging a ticket or drafting a quote, the system sits in the flow, observing every input. It sees the data from the CRM, the policy from the document store, and the prompt sent to the LLM.
- The Memory (Immutable Log): Every step is recorded in an immutable log. This creates a chain of custody for the decision, linking the final outcome back to the specific data and reasoning that produced it.
Context Graphs: Myth vs. Reality
Myth: "We have a Data Warehouse, so we have all the context we need."
Reality: Warehouses are incredible truth registries, but they are often updated after the fact. They store the result of the decision, not the context of the moment it was made. A context graph sits in the execution path to capture the live inputs and reasoning states that vanish once the transaction is over.
Myth: "Governance slows down AI innovation."
Reality: The opposite is true. Without deep governance and auditability, AI projects stall. Gartner predicts 40% of agentic AI projects will fail due to immature governance. A context graph provides the "guardrails" that give enterprises the confidence to let agents increasingly run more and more autonomously.
Myth: "Context graphs are just for training models."
Reality: They are for operating agentic workflows. They answer the question "Why did we do that?" for auditors, managers, and future agents alike.
Beyond the Basics with Squirro
At Squirro, we recognized early that auditability is critical for AI risk management and successful enterprise adoption of agentic AI. That’s why we’ve engineered our platform to serve as the system of record for decisions.
- Compliance as Infrastructure: What we built to satisfy the strictest regulatory audits inherently supports the needs of a context graph. Our audit logs serve as the decision trace, ensuring that all actions by our platform are traceable and explainable.
- Knowledge Graph Native: We don't just rely on vector search; we structure workflows using knowledge graphs. This provides the semantic structure needed to ensure that agents follow business logic and don’t just hallucinate new processes.
- Orchestration with Memory: Squirro sits in the search / agentic layer, orchestrating the retrieval of information and the execution of tasks. This gives us easy access to the full context of a decision as it happens.
The Barrier: The "Why" Gap
The biggest barrier to adopting Agentic AI isn't a lack of intelligence; it's a lack of memory. Agents freeze in ambiguous situations because the reasoning behind previous human decisions – the vital context of why an exception was granted – was never captured. Without this library of past overrides to serve as precedent, agents are forced to operate without the nuance required for real-world business.
The Final Word: The New System of Record
The "trillion-dollar opportunity" in software, described by Jaya Gupta on X, is shifting. Value is moving from systems that hold data (Systems of Record) to systems that hold decisions (Context Graphs).
By implementing auditable AI workflow automation, you aren't just ticking a compliance box. You are building the institutional memory of your future organization. You are building a system that doesn't just know what happened, but understands why. And that makes all the difference.
