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CATEGORY DEFINITION · DECISION GRAPHS

Decision Graphs:
The System of Record
for AI Agent Decisions

Every enterprise AI agent makes hundreds of decisions daily. Who approved them? Which policy applied? What context did the agent have? Decision graphs are the infrastructure layer that captures, links, and cryptographically seals those decisions — making AI accountability possible at scale.

INTENTCONTEXTPOLICYDECISIONSHA-256SEALED

INTENT → CONTEXT + POLICY → DECISION → SHA-256 SEALED NODE

What is a Decision Graph?

A decision graph is a structured, queryable record of every decision an AI agent makes in production. Each node in the graph represents one decision event, capturing:

INTENT
What the agent was asked to do
CONTEXT
What information was available at decision time
POLICY
Which governance rules applied
OUTPUT
What the agent decided or acted on
APPROVAL
Whether human oversight was triggered
SEAL
SHA-256 cryptographic integrity proof

Unlike flat observability logs, decision graph nodes are linked — a multi-step agent workflow creates a connected graph where you can trace the full causal chain from initial intent to final outcome. Unlike periodic audit reports, decision graphs are generated at execution time, automatically, for every decision — creating a continuous, tamper-proof record that regulators and auditors can query on demand.

MARKET OPPORTUNITY

Decision Graphs: The Next Trillion-Dollar Infrastructure Layer

In 2023, every enterprise needed a vector database for RAG. In 2024, every enterprise needed an agent framework to orchestrate LLMs. In 2025–2026, the missing layer becomes undeniable: a system of record for what those agents actually decided.

Consider the scale: 80% of Fortune 500 companies now have active AI agents (Microsoft Security, Feb 2026). Each agent makes dozens to thousands of decisions per day — credit approvals, clinical routing, legal review, financial planning. In regulated industries, every one of those decisions must be auditable.

The EU AI Act (August 2026), HIPAA, SEC, and FDA are converging on the same requirement: if an AI agent made a consequential decision, you must be able to prove what happened. Decision graphs are the infrastructure that makes that proof possible. This is not a compliance checkbox — it is the accountability substrate that unlocks trillion-dollar AI adoption in regulated industries.

READ: THE TRILLION-DOLLAR DECISION GRAPH MARKET →

How Mala Implements Decision Graphs

Mala is the decision graph platform for the agentic era. Three components work together to capture, govern, and seal every agent decision:

AMBIENT SIPHON
Zero-refactor instrumentation
Wraps any agent framework — LangChain, CrewAI, Retell AI, AutoGen, custom — without changing a line of agent code. Every decision point is automatically captured as a graph node.
WISDOM GRAPH
Context + policy at decision time
At each node, the agent's intent is matched against your organizational policy library and precedent graph. The right policy is applied at execution time — not audited after the fact.
INTEGRITY SEAL
SHA-256 cryptographic proof
Each decision node is sealed with a SHA-256 hash of its full context. The seal is immutable — if any detail of the decision is altered, the hash breaks. This is what makes decision graphs legally defensible.

Decision Graph vs. Alternatives

Decision graphs are often confused with adjacent concepts. Here's how they differ:

ConceptWhat it capturesDecision Graph (Mala)
Decision TreeStatic, pre-defined branching logicDynamic runtime record of actual agent decisions
Knowledge GraphEntities and their relationships (facts)Decision events and their causal chain (accountability)
Workflow GraphProcess steps and their orderDecision nodes with context, policy, and cryptographic seal
Reasoning ChainInternal LLM reasoning steps (debug)Governance-grade sealed record (compliance)
Observability LogsMutable performance metrics and tracesImmutable SHA-256 sealed decision certificates
Agent TracingSpans and latency for debuggingPolicy-enforced decision graph for audit

Who Needs Decision Graphs

🏥 Healthcare
Clinical Triage & Voice Agents
AI nurse lines, symptom routing, and call center triage agents must log every decision under HIPAA. Decision graphs create the clinical AI audit trail — who was routed where, why, and with what clinical policy.
🏦 Financial Services
Credit, AML & Trading
SR 11-7, ECOA, and SEC AI guidance all require explainable decision records. Decision graphs seal every credit approval, AML flag, and trading decision with immutable, regulator-ready proof.
⚖️ Legal & Compliance
AI-Assisted Legal Review
When AI agents draft contracts, flag clauses, or recommend settlements, decision graphs create the accountability record. Who approved the AI recommendation and under which policy?
🏗️ Enterprise / Agentic AI
Multi-Agent Orchestration
For enterprises running agent swarms — planning, execution, review — decision graphs link each agent's decisions into a unified system of record. The full reasoning chain, queryable and sealed.

Explore the Decision Graph Cluster

Deep guides for every use case, vertical, and integration.

Decision Graph FAQ

What is a decision graph?
A decision graph is a structured, queryable record of every decision an AI agent makes — capturing the intent (what was asked), context (what information was available), policy (what rules applied), output (what was decided), and a cryptographic integrity seal. Unlike flat logs, a decision graph links decisions together, showing cause and effect across multi-step agent workflows.
How is a decision graph different from a knowledge graph?
A knowledge graph represents entities and their relationships (people, places, concepts). A decision graph represents decision events and their causal chain — what an AI agent decided, why, under which policy, and what happened next. Knowledge graphs answer "what exists." Decision graphs answer "what was decided and why."
How is a decision graph different from a decision tree?
A decision tree is a static, pre-defined model for categorizing inputs into outputs. A decision graph is a dynamic runtime record of actual decisions made by AI agents in production. Decision trees are planning tools; decision graphs are accountability infrastructure.
Why are decision graphs the next trillion-dollar market?
Every enterprise deploying AI agents must answer to regulators, auditors, and boards about what those agents decided. As autonomous AI handles credit approvals, clinical triage, legal review, and financial planning, the audit trail of those decisions becomes as valuable as the decisions themselves. Decision graphs are the system-of-record infrastructure that makes AI adoption defensible at scale — the missing layer between AI capability and AI accountability.
How does Mala implement decision graphs?
Mala's Ambient Siphon instruments any agent framework (LangChain, CrewAI, Retell AI, custom) without code changes. At each decision point, it captures the full context, applies configured policies, optionally triggers human approval, and seals the decision with a SHA-256 hash. All decision nodes are linked into a queryable decision graph — your AI's system-of-record.
Do decision graphs work for healthcare AI triage?
Yes — and this is one of the most urgent use cases. When an AI voice agent routes a patient call (nurse line, symptom triage, escalation), every routing decision must be auditable under HIPAA and clinical governance requirements. Mala's decision graph captures each triage decision: the patient's stated symptoms, the routing policy applied, the escalation decision, and the sealed timestamp — creating a compliance-ready clinical AI audit trail.
READY TO BUILD YOUR DECISION GRAPH

Every agent decision.
Sealed. Queryable. Defensible.

Mala's decision graph platform instruments in hours — no refactoring, no migration. Your agents keep running. Every decision they make becomes evidence.

EXPLORE THE DECISION GRAPH PLATFORM →