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The Problem
Why RAG Isn't Enough
Traditional RAG (Retrieval-Augmented Generation) has a fundamental limitation: It finds documents that are similar to the query. It doesn't understand how things are connected.
The Customer's Contract
The agent must verify the specific terms and conditions that apply to this customer's agreement and understand the contractual relationships.
Recent Incidents
The agent needs to understand any service issues or problems that may impact the discount decision and review incident history.
Prior exceptions for Customer
The agent must review historical discount approvals and exception patterns for this specific customer to understand precedent decisions.
VP Approval Authority
The agent must determine if a 20% discount requires VP-level approval based on the discount amount and established policy thresholds.
RAG might find a similar contract or a similar incident. But it can't traverse the actual relationship path that proves whether this exception is justified.
What Are Decision Graphs
Structure + Meaning + Governance
Decision Graphs are three things combined that work together to create a comprehensive framework for intelligent decision making
01
Structure: Connected Entities and Relationships
Entities and relationships across systems connected by traversable paths that enable structured navigation and context assembly.
Resolves identities, entities, and relationships
Creates traversable paths connecting entities across systems
Enables path traversal from Customer to Contract, Incident, and Decision
Outcome: Structured graph of connected entities and relationships
02
Meaning: Semantic Structure Through Ontology
Ontology provides semantic structure defining what entities mean, how they relate, and what rules apply.
Defines what entities mean in the business domain
Specifies how entities relate to each other
Establishes what rules apply to entities and relationships
Outcome: Semantic understanding of entities, relationships, and business rules
03
Governance: Access Controls and Validation
Access controls, freshness requirements, and integrity constraints ensure context is valid for the decision at hand.
Enforces access controls and authorization policies
Validates freshness requirements for context data
Maintains integrity constraints and data validation
Outcome: Context validated and ready for trustworthy decision-making
RAG VS DecisionGraphs
The Fundamental Difference
Guardrails filter outputs and block violations after they occur, whereas Deterministic Enforcement validates conditions before execution, making violations structurally impossible
Similarity-Based Retrieval
RAG finds documents similar to queries but cannot understand relationships or traverse entity paths. It returns chunks ranked by similarity without temporal awareness or access control, where similarity does not equal relevance.
Find documents similar to this query
Returns: chunks ranked by similarity
No temporal awareness
No access control per query
Similarity ≠ relevance
Outcome: Reactive compliance only
Relationship Path Traversal
Decision Graphs traverse structured paths connecting entities and return structured context with relationships, temporal awareness, and governed access control.
Traverse the path: Customer → Contract → Incidents → Prior Exceptions
Returns: structured context with relationships
Temporal context: "as of decision time"
Governed: access control on every edge
Relationships = meaning
Outcome: Structural enforcement guaranteed
Ontology
The Formal Model of Meaning
Ontology is the formal model that gives structure to Decision Graphs:
Ontology prevents Context Confusion — the failure mode where AI can't distinguish rules from examples, policies from precedents
The Ontology knows that 'exception threshold' means 3+ SEV-1 incidents, not just 'some incidents.
Entity definitions
Defines what entities mean in the business domain, such as what is a Customer, Contract, Incident, or Decision, establishing clear semantic boundaries for each entity type.
Relationship types
Specifies how entities connect to each other through defined relationship types such as owns, manages, and approves, creating a structured network of connections.
Authority models
Encodes business constraints and authority rules as traversable paths that can be navigated and validated during decision making processes.
Data Insights Acceleration
Defines who can approve what actions and under which specific conditions, establishing clear governance boundaries for decision making authority.
Preventing Failure Modes
How Context Graphs Address Four Failure Modes
Enterprise reality is fragmented across systems
Context Rot
Decision Graphs maintain freshness metadata on every edge, tracking when data was last updated and ensuring accuracy.
Stale context is automatically flagged before execution, preventing decisions based on outdated information and data integrity.
Decisions always use current, validated context with automatic freshness validation
Context Pollution
Graph traversal returns only the minimal relevant context needed, following the specific path required for this decision.
By traversing structured paths instead of searching broadly, agents receive precisely the context they need without noise.
Precise context retrieval through path traversal eliminates information overload
Context Confusion
The ontology layer provides semantic structure that distinguishes rules from examples and policies from precedents.
Typed edges prevent semantic confusion by ensuring AI systems understand the meaning and purpose of connections.
Clear semantic boundaries prevent AI from misinterpreting context and relationships
Decision Amnesia
Every decision trace automatically becomes a new node in the graph, preserving the complete context and reasoning of decisions.
Prior decisions are stored as traversable precedent nodes, allowing future decisions to reference and learn from patterns.
Complete decision history accessible as precedents for consistent, informed future choices
The Compounding Loop
Graphs That Get Smarter
Decision Graphs participate in the Compounding Loop
Decision made using context from graph
Agents traverse the graph to gather structured context from connected entities, relationships, and prior decisions before making informed choices.
Decision Trace generated with full lineage
Every decision automatically generates a complete trace documenting the context used, relationships traversed, and reasoning applied.
Trace becomes new node in the graph
The decision trace is added as a new node connected to relevant entities, creating a permanent record of the decision and its context.
Future decisions can traverse to this precedent
Subsequent decisions can reference and learn from previous traces, using them as precedents to inform similar situations and maintain consistency.
Graph becomes progressively more valuable
As more decisions are made and traces added, the graph accumulates knowledge, relationships, and precedents that enhance future decision quality.
FAQ
Frequently Asked Questions
Knowledge graphs describe what exists. Decision Graphs also enforce what's allowed — access controls, freshness requirements, and traversal constraints are first-class citizens.
Connectors to enterprise systems (CRM, billing, support, contracts) synchronize data. Identity Resolution unifies entities. Ontology provides structure. The graph builds incrementally.
Unstructured data (documents, emails, tickets) is attached to structured entities. The contract PDF is linked to the Contract node. Retrieval is still graph traversal.
Yes. Graph traversal finds the right context. RAG-style retrieval can then search within that context. But the graph provides the relationships that RAG alone cannot.
Similar documents don't prove anything. Relationships do.
Decision Graphs traverse structured paths connecting entities across systems, providing semantic structure through ontology, unified identity resolution, and governed access control. Unlike RAG which finds similar documents, Decision Graphs understand relationships and enable agents to navigate context through traversable paths.