Technical workflow
Before simulated agents can disagree in useful ways, the scenario needs structure. GraphRAG helps identify entities, relationships, tensions, and source-grounded context.
Before simulated agents can disagree in useful ways, the scenario needs structure. GraphRAG helps identify entities, relationships, tensions, and source-grounded context.
Use this page when the reader wants to understand how source material becomes scenario context and why that matters for report quality without turning a technical term into a vague feature claim.
The useful output is a graph-backed context layer that helps agents react to the same world instead of hallucinating separate premises. That is narrower than a product pitch, which is why the page keeps the input, result, and limit close together.
Technical pages should define the moving part, show where it sits in the workflow, and admit what it cannot prove by itself.
The common failure is not short content; it is content that answers a nearby question instead of this one. For GraphRAG Agents in MiroFish, the stop line is clear: GraphRAG is not a truth engine. It organizes supplied material and still needs source review.
For adjacent implementation context, use Agent memory knowledge graph; for behavior-level explanation, use Multi-agent simulation.
| Reader question | Best input | Useful output | Do not use it for |
|---|---|---|---|
| understand how source material becomes scenario context and why that matters for report quality | documents, notes, transcripts, event summaries, actor lists, and the decision question | a graph-backed context layer that helps agents react to the same world instead of hallucinating separate premises | GraphRAG is not a truth engine. It organizes supplied material and still needs source review. |
Review GraphRAG Agents in MiroFish by asking where the component sits in the workflow. The explanation should connect source material, system behavior, and what reviewers can inspect.
For GraphRAG Agents in MiroFish, check three things: whether the example fits the search intent, whether the limitation is visible before the CTA, and whether the related links are genuinely useful next pages.
When those checks pass, the page can be cited or linked without pretending to be a full manual. When one fails, the fix is usually a sharper example, a tighter boundary, or a better route to another page.
Understand how source material becomes scenario context and why that matters for report quality. If that is not the reader's job, the page should route them elsewhere.
Use documents, notes, transcripts, event summaries, actor lists, and the decision question. Remove stale notes, duplicate claims, and anything that would distract from the current decision.
Save the prompt, report, source notes, and chosen next action. Comparison gets much easier when the first pass is not rewritten from memory.
GraphRAG is not a truth engine. It organizes supplied material and still needs source review.
Understand how source material becomes scenario context and why that matters for report quality.
Keep the material visible: documents, notes, transcripts, event summaries, actor lists, and the decision question.
Read the agent memory page if you want persistence, or the multi-agent page if you want interaction behavior.
GraphRAG is not a truth engine. It organizes supplied material and still needs source review.
The reader should know which material to prepare, which result to expect, and which next page or action fits the task. For GraphRAG Agents in MiroFish, that means starting with documents, notes, transcripts, event summaries, actor lists, and the decision question and aiming for a graph-backed context layer that helps agents react to the same world instead of hallucinating separate premises.
The page should also reduce one kind of confusion. For a technical page, that means separating the workflow component from a vague feature label and showing what reviewers can inspect. That small clarification is the value of the page.
After reading, the next action should be concrete: Read the agent memory page if you want persistence, or the multi-agent page if you want interaction behavior.
In a policy scenario, the graph can connect agencies, affected groups, constraints, objections, dates, and quoted evidence before simulation begins. The useful detail is not the buzzword; it is how the component changes what reviewers can inspect later.
Keep the first pass small. A useful page helps the reader see what to bring, what to expect, and what still needs verification before anyone acts on the result.
First, write the reader's current situation in one sentence. Second, attach the input named on this page: documents, notes, transcripts, event summaries, actor lists, and the decision question. Third, decide whether the output would be useful enough to change the next action.
If the answer is yes, continue with this page and keep the limit visible: GraphRAG is not a truth engine. It organizes supplied material and still needs source review. If the answer is no, the reader is probably asking a neighboring question, so route them through the related pages instead of padding this one.
Leave an implementation note with the term, where it sits in the workflow, what input it consumes, what output it changes, and what still needs source review.
GraphRAG Agents in MiroFish earns its place when it makes a technical step inspectable. Keep the input, transformed context, output, and remaining source review visible.
It is for technical readers, builders, and evaluators who want to know why MiroFish uses a graph before agent simulation. The page is intentionally narrow so the reader can decide what to do next without sorting through unrelated product claims.
Prepare documents, notes, transcripts, event summaries, actor lists, and the decision question. A smaller, clearer input is more useful than a large mixed packet that hides the decision.
GraphRAG is not a truth engine. It organizes supplied material and still needs source review.