Graph-native reasoning

Agents That Plan. Not Prompt.

The core question for any agent is: what should I do next? A reasoning graph makes the answer explicit - queryable action space, scored paths, inspectable traces.

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Prompting isn't planning.

Most agent frameworks answer "what next?" by prompting the LLM to reason in natural language consuming tokens, producing variable results, and offering no guarantees. A reasoning graph makes the action space explicit and traversable.

Reasoning graphs.

Nodes represent states or decision points. Edges represent available actions. Properties encode scores, expected value, feasibility, historical success rate. The planning problem becomes a graph traversal.

Shortest path

Most efficient route to a goal.

Centrality

Identify critical intermediate states worth validating.

Community detection

Group related sub-tasks for parallel execution.

Weighted traversal

Evaluate multi-step plans without consuming LLM calls.

CUSTOMER SUPPORT · AGENT REASONING GRAPH

Ticket receivedTicket received

Assess severityAssess severity

score0.31

Auto-resolveAuto-resolve

score0.87

Escalate to agentEscalate to agent

score0.54

Request more infoRequest more info

Done

Ticket receivedTicket received

Assess severityAssess severity

score0.31

Auto-resolveAuto-resolve

score0.54

Request more infoRequest more info

score0.87

Escalate to agentEscalate to agent

Done

Chosen path

Alternative path

Auditability as structure.

When the agent acts, the traversed path is an inspectable trace. Alternative paths can be scored and compared. The basis for the decision is examinable through graph structure and edge scores, not through interpretation of token probabilities.

Capabilities

Why Memgraph.

Sub-millisecond traversals. Native vector search. Real-time writes. Built for AI pipelines that can't afford to wait.

Sub-millisecond scoring and traversal
Agent reasoning loops are latency-sensitive. Every millisecond querying state is a millisecond added to response time.
Rapid concurrent reads and writes
State evolves with every action. Memgraph handles the read/write velocity of an active agent without queuing or batching.
Multi-agent coordination
Multiple agents or sub-agents working in parallel, accessing shared state concurrently.
Real-time state updates
The reasoning graph isn't static. As the agent acts and new information arrives, the graph updates in real time.