Platform/Company Brain
Your systems get profiled where they sit, and the profile is kept honest.
An agent is only as correct as the slice of context it was handed. This is what decides that slice, and it holds the meaning of your data rather than a copy of it.
What actually moves
The rows never leave your systems.
What SynOS holds is the statistics, the semantics and the bindings. The answer is assembled by querying your systems at the moment the question is asked.
What it holds
Eight kinds of memory, because an agent needs more than documents.
Most context tools give you one shape, chunks of text with embeddings, which covers a policy document and none of the rest.
Status is per capability and stated the same way everywhere on this site.
Getting the right slice
The long context window made retrieval more important, not less.
Most answers that get blamed on the model are a retrieval problem. A router picks the mode per query.
mode
what it does
when it fires
Auto routermode = auto
A selector picks the mode and the source per query.
the default, mixed query types over mixed sources
LexicalBM25
Sparse retrieval on exact term overlap. Fast, transparent, and it needs no GPU.
order IDs, account numbers, SKUs, policy codes
Semanticdense embeddings
Ranks by meaning, so paraphrase, synonyms and a second language all still land on the right passage.
natural language questions, varied vocabulary
Hybrid with rerankBM25 + vector + RRF
Runs lexical and semantic in parallel, fuses the two rankings, then a cross encoder reorders the top of the list for precision.
production default, prose mixed with literal identifiers
Agenticplan, retrieve, reason
Decomposes a hard question into sub questions, runs each, merges and reranks what came back.
multi part questions, long threads, follow ups
Graph traversalentity hops
Walks linked entities and their relationships. Traversal happens at query time, so there is no giant persisted graph to keep in sync.
multi hop reasoning across connected entities
Whatever the mode, what comes back is a typed context pack: scoped to the caller's role and project, ranked by authority and freshness, fitted to a token budget, and carrying citation IDs so an answer can be traced to the row it came from.
The part that decays
Schemas drift and definitions change. Agents keep answering anyway.
The failure nobody demos, because it takes a quarter to appear: the agent carries on sounding exactly as confident as it did in week one.
01
Re-profile
Agents re-read schemas, samples, documents and APIs. New tables, dropped columns, renamed fields, distributions that have shifted.
02
Diff and score
Compare what is there now against what the brain believed. Each mapping gets a confidence score and the date it was last confirmed.
03
Apply or escalate
High confidence changes apply themselves. Anything ambiguous stops and asks a person, which is the whole point of scoring it.
STALE
7 tables not re-profiled in 30 days, and 2 of them feed a live agent.
DRIFT
orders.status gained 3 enum values, so the existing mapping no longer covers them.
CONFLICT
Two tables both claim revenue. Which one is authoritative?
LOW CONF
4 metric bindings sit below threshold, which means the agent is guessing on them.
PROPOSED
Profiling found a new entity, Gift Recipient, spanning 3 tables. Add it to the ontology?
Illustrative queue. Your systems produce your own.
What it is and is not
Three things people expect it to be, and what it does instead.
Bring one system and see what the profile finds.
The fastest version of this conversation is a walkthrough against a schema you already know well, because you will spot immediately where a profile is right and where it is guessing.