What are GraphRAG agents?

GraphRAG agents are AI agents that use graph-structured context, relationships, and retrieved evidence to support reasoning or simulation.

Why does graph context help scenario simulation?

Graph context helps keep entities, incentives, relationships, and evidence connected so simulated agents do not rely only on a flat prompt.

Is GraphRAG the same as a normal vector search workflow?

No. GraphRAG emphasizes entities and relationships in addition to retrieved text, which can help scenario reports explain why actors are connected.

GraphRAG agents

GraphRAG agents give scenario simulation a map of entities and relationships.

MiroFish uses graph-oriented context to help simulated agents reason from seed material, connected entities, remembered assumptions, and scenario evidence.

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Why graph-grounded agents matter

Scenario simulation is hard when the context is just a long prompt. Important links between actors, incentives, claims, facts, and risks can disappear as the conversation grows.

A graph-oriented workflow gives agents a more explicit map. That map can support better questions: who is connected, what fact changes which actor, and where the scenario depends on one fragile relationship.

Where GraphRAG helps MiroFish

Grounded roles

Agents can inherit context from the graph instead of relying only on generic personas.

Traceable reports

Reports can explain which relationship, claim, or assumption influenced a branch.

Better reruns

Changing one node or assumption can make scenario comparison cleaner across simulation runs.

Practical interpretation

GraphRAG agents do not remove uncertainty. They make uncertainty easier to inspect by tying simulated reactions back to entities, relationships, and evidence. For high-stakes decisions, combine the report with fresh data and expert review.

Updated: 2026-06-23.