Beyond Systems of Record: The Rise of the Decision Graph
For decades, enterprise software has been defined by “systems of record” – Salesforce for customers, Workday for employees, SAP for operations. These platforms won by owning the core data and workflows. But a new paradigm is emerging, driven by AI agents, that challenges this established order. The future isn’t just about *what* happened, but *why* it happened, and that requires a fundamentally different kind of system.
The Agent Revolution and the Missing Context
AI agents promise to automate complex tasks across multiple systems. However, they’re hitting a wall. It’s not a data problem; it’s a context problem. Existing systems excel at storing data, but they fall short in capturing the nuances of human decision-making – the exceptions, overrides, and precedents that shape real-world outcomes. Think of a sales deal approved with a VP exception due to a long-standing customer relationship. The CRM records the final price, but not the rationale behind it.
This gap is where the opportunity lies. The key isn’t just better access to data, but the ability to record and analyze the “decision traces” – the complete history of how a decision was reached. These traces, when aggregated, form a “context graph,” a living record of organizational reasoning.
Rules vs. Decision Traces: A Critical Distinction
Traditional systems operate on rules: “Use official ARR for reporting.” These are general guidelines. But real-world scenarios often require deviation. Decision traces capture those deviations: “We used X definition, under policy v3.2, with a VP exception, based on precedent Z, and here’s what we changed.”
Consider a customer support escalation. A support agent doesn’t just follow a script. They consider the customer’s lifetime value (from Salesforce), open tickets (from Zendesk), recent churn risk signals (from Slack), and past successful resolutions. This synthesis happens in their head, and the resulting action – escalation to Tier 3 – is often documented with minimal context. A decision trace would capture the entire reasoning process.
Why Incumbents Struggle to Capture the Context Graph
Existing enterprise giants like Salesforce and Workday are attempting to integrate AI agents. However, their architecture is fundamentally limited. They are built for storing current state, not historical context. They lack the ability to replay the state of the world at the moment a decision was made, hindering auditability, learning, and precedent setting.
Data warehouses like Snowflake and Databricks offer a time-based view, but they receive data *after* decisions are made, losing crucial context. They are in the “read path,” not the “write path” where decisions are captured.
Did you know? According to a recent McKinsey report, companies that effectively leverage decision intelligence see a 10-20% improvement in decision-making speed and quality.
The Startup Advantage: Orchestration is Key
Startups building agent orchestration layers have a structural advantage. They sit in the execution path, witnessing the full context at decision time. By persisting these decision traces, they can create a queryable record of how decisions were made – the context graph. This isn’t just about automation; it’s about building a new system of record for decisions, not just objects.
Startup Approaches: Three Paths to Success
Several strategies are emerging:
- Full System Replacement: Rebuilding core systems (CRM, ERP) around agentic execution. This is challenging but viable during market transitions.
- Modular Replacement: Targeting specific workflows with high exception rates (e.g., deal desks) and becoming the system of record for those decisions. Maximor is a prime example, automating finance workflows while integrating with existing GLs.
- New System of Record Creation: Starting as orchestration layers and persisting decision-making traces, eventually becoming the authoritative source for “why” decisions were made. PlayerZero is building a context graph for production engineering, capturing the complex interactions between code, infrastructure, and customer behavior.
Observability: The Critical Infrastructure Layer
As context graphs grow, observability will become essential. Tools like Arize are emerging to monitor, debug, and evaluate agent behavior at scale, ensuring decision quality and identifying potential biases.
Key Signals for Founders
Where should startups focus their efforts?
- High Headcount Workflows: Manual processes involving 50+ people suggest complex decision logic ripe for automation.
- Exception-Heavy Decisions: Workflows requiring frequent deviations from standard rules are ideal candidates for decision trace capture.
- Intersection of Systems: Roles and organizations that bridge multiple systems (RevOps, DevOps, Security Ops) often hold critical contextual knowledge.
FAQ
- What is a context graph?
- A context graph is a structured, queryable record of decision traces, showing not just *what* happened, but *why* it was allowed to happen.
- Why are existing systems of record struggling with AI agents?
- They are designed to store current state, not historical context, and lack the ability to capture the nuances of human decision-making.
- What is the role of observability in this new paradigm?
- Observability tools are crucial for monitoring, debugging, and evaluating agent behavior at scale, ensuring decision quality and identifying potential issues.
The future of enterprise software isn’t about simply adding AI to existing data. It’s about capturing the decision-making process itself. The startups that build the context graphs of tomorrow will be the ones to unlock the true potential of AI and define the next generation of trillion-dollar platforms.
Pro Tip: Focus on workflows where human judgment is currently essential. These are the areas where decision traces will provide the most value.
What are your thoughts on the future of AI and decision-making? Share your insights in the comments below!
