MEMORY / Problem first
Why do professional Agents need object-based memory rather than just vector retrieval?
Because professional tasks require not only finding similar text, but also knowing which customer or case the material belongs to, what relationships exist between people, which piece of evidence supports a fact, under what conditions a rule applies, and who has the authority to make a conclusion. Object-based memory preserves these relationships and states, while vector retrieval serves as one of the recall methods.
Definition: What is object-based memory?
Object-based memory builds customers, opportunities, cases, parties, contracts, clauses, precedents, tasks, deliverables, and owners into business objects that can be referenced continuously, and stores attributes, relationships, events, sources, permissions, and status. Its goal is not to chunk the full text so that "everything can be searched," but to retrieve the correct objects and source evidence for the current task.
Four-layer support method
1. Object-based memory
Clarify who this fact belongs to and which matter and status it sits in, so clients with the same name, old versions or different cases don't get mixed up.
2. Ontology-based extraction
Extract objects, attributes, events, roles, conditions and evidence from materials while retaining source citations for verification.
3. Relationship graph
Express who participates in which matter, which piece of evidence supports which fact, which clause creates which obligation, and who is responsible for the task.
4. Task-level retrieval
Based on the task, user identity, permissions and item status, combine structured objects, relationships, vector recall and source evidence.
What is the Agent responsible for?
The Agent can extract candidate entities and relationships, merge confirmable aliases, locate relevant source text, list conflicts and gaps, generate a sourced working draft, and suggest entity status updates when new material appears. Output should distinguish known facts, model inferences, items pending verification and sources used.
What must people decide?
Professionals confirm whether the objects are the same, whether the evidence is valid, and whether the rules apply, and are responsible for legal opinions, sales judgments, consulting conclusions, and external deliverables. The data owner decides access permissions, retention periods, and handling of sensitive information; Agents should not expand the scope of materials on their own or write inferences back into factual fields.
When does it apply?
Suited to professional work that needs long-term context, role relationships, evidence chains and continuous state, such as complex sales opportunities, contract review, case research, consulting projects and training delivery. If it's just one-off retrieval of public information or a summary of a single document, lightweight vector retrieval may already be enough, and over-modeling isn't necessary.
Common misconceptions
Findable doesn't mean used correctly:Similar text may belong to another customer, an old contract, or inapplicable conditions.
A graph is not the same as facts:Automatically extracted relationships still need sources and confirmation status.
More memory does not mean better:Permissions, timeliness and task relevance determine what should be retrieved.
Having RAG doesn't mean having judgment:Retrieval supports judgment but does not bear professional responsibility.
Bring relationships and evidence back to the task
Objects, relationships, evidence and permissions need to enter the task flow together: first clarify the question to be answered, then locate the relevant objects and materials, mark what still needs confirmation, and finally let the responsible person make the professional judgment.
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WECHAT / Professional Knowledge System
Want to judge whether your materials need object-based memory?
When adding WeChat, you can say: "I want to organize the objects and relationships in a type of professional material." Bring one de-identified task and a few sample documents to first judge whether lightweight retrieval is sufficient, then decide how deep the modeling needs to go.
