Memory and context
MiroFish uses structured context so a scenario can be reviewed. A knowledge graph helps keep entities, relationships, sources, and assumptions visible.
Understand why memory should point back to sources and how it shapes agent behavior. If that is not the reader's job, the page should route them elsewhere.
Use source documents, entity names, relationship notes, prior scenario reports, and the current 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.
Memory can preserve bad assumptions if the source packet is wrong. Review the graph before trusting downstream reports.
entity, relationship, source, assumption
MiroFish uses structured context so a scenario can be reviewed. A knowledge graph helps keep entities, relationships, sources, and assumptions visible.
Use this page when the reader wants to understand why memory should point back to sources and how it shapes agent behavior without turning a technical term into a vague feature claim.
The useful output is a context map that supports follow-up questions and helps reviewers see why agents reacted as they did. That is narrower than a product pitch, which is why the page keeps the input, result, and limit close together.
| Reader question | Best input | Useful output | Do not use it for |
|---|---|---|---|
| understand why memory should point back to sources and how it shapes agent behavior | source documents, entity names, relationship notes, prior scenario reports, and the current question | a context map that supports follow-up questions and helps reviewers see why agents reacted as they did | Memory can preserve bad assumptions if the source packet is wrong. Review the graph before trusting downstream reports. |
Review Agent Memory Knowledge Graph by asking where the component sits in the workflow. The explanation should connect source material, system behavior, and what reviewers can inspect.
For Agent Memory Knowledge Graph, 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.
The reader should know which material to prepare, which result to expect, and which next page or action fits the task. For Agent Memory Knowledge Graph, that means starting with source documents, entity names, relationship notes, prior scenario reports, and the current question and aiming for a context map that supports follow-up questions and helps reviewers see why agents reacted as they did.
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: Pair this page with the GraphRAG agents guide, then test a small scenario before using a larger document set.
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 Agent Memory Knowledge Graph, the stop line is clear: Memory can preserve bad assumptions if the source packet is wrong. Review the graph before trusting downstream reports.
For adjacent implementation context, use GraphRAG agents; for behavior-level explanation, use Multi-agent simulation.
Understand why memory should point back to sources and how it shapes agent behavior.
Keep the material visible: source documents, entity names, relationship notes, prior scenario reports, and the current question.
Pair this page with the GraphRAG agents guide, then test a small scenario before using a larger document set.
Memory can preserve bad assumptions if the source packet is wrong. Review the graph before trusting downstream reports.
First, write the reader's current situation in one sentence. Second, attach the input named on this page: source documents, entity names, relationship notes, prior scenario reports, and the current 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: Memory can preserve bad assumptions if the source packet is wrong. Review the graph before trusting downstream reports. 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.
Agent Memory Knowledge Graph earns its place when it makes a technical step inspectable. Keep the input, transformed context, output, and remaining source review visible.
A product scenario can preserve customers, objections, features, dates, competitor claims, and prior report notes as a reviewable graph. 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.
It is for builders and analysts who care about traceable context rather than black-box simulation output. The page is intentionally narrow so the reader can decide what to do next without sorting through unrelated product claims.
Prepare source documents, entity names, relationship notes, prior scenario reports, and the current question. A smaller, clearer input is more useful than a large mixed packet that hides the decision.
Memory can preserve bad assumptions if the source packet is wrong. Review the graph before trusting downstream reports.