Product — Knowledge Agent
The complaint about AI slop is really a complaint about a breakdown in accuracy, trust, and transparency. Fluent generation has outrun evidence: content is cheap to produce and hard to verify, with no traceable trail back to the source.
Knowledge Agents are built on a learning model that retains the reasoning structure leading to the answer. Every source used in generation is transparent to the user. Trust, verifiability, privacy, and auditability are part of the experience design — not a setting.
Knowledge Agents use known and trusted sources of knowledge
Your policies, your clinical protocols, your contracts, your regulatory filings, your engineering docs. Organizations managing proprietary knowledge need a path free of the risk of leaking it to the outside world — and control over when the outside world is subtly influencing it.
It is scoped to a corpus you control, answers from retrieved passages, cites what it used, links to the original file, and — in strict mode — abstains when the corpus is silent rather than inventing a plausible sentence. Politely we call these hallucinations; what they really are is statistical noise. In a regulated or high-trust deployment, noise is risk. Abstention gives you the opportunity to curate the base of trusted sources.
The role of a Knowledge Agent
Take the research, studies, protocols, filings, and engineering docs that already encode hard-won expertise, and synthesize them into a coherent, queryable, provenance-backed body of knowledge — a Collective IQ scoped to a corpus you control. This is what a single Knowledge Agent does.
Bring that synthesized knowledge to the collective reasoning table, where agents and humans reason together, surface what no single source held, and acquire new knowledge. Synthesis establishes what is known; collective reasoning is how the community acquires what is not yet known.
What makes it trustworthy
The reason to eliminate noise in a single agent is so it can join collective reasoning without importing that noise into the group.
Vector search fused with BM25 lexical matching, cross-encoder reranking, diversity selection, then source-grounded synthesis.
Every chunk carries source_type, source_id, source_url, author, timestamp, title, and any custom metadata you stamp on it. Two passages can be equally relevant and carry wildly different evidentiary weight — an official policy is not a Slack message. Structured provenance makes that difference visible to the interface and to the analytics.
Which sources supported the answer (citation) → which chunk was retrieved (excerpt + chunk_id + similarity) → what the source actually says in its original form (download_url on the citation returns the original PDF or DOCX). The reader never has to trust the summary, or even the extract.
The same question can be run in foundation-assisted mode and then in referenced-retrieval mode. The difference between the two answers is exactly the model's unsupported inference. No competitor exposes that gap.
Changed chunks are re-embedded; the rest of the corpus is untouched. Documents keep their identity across revisions.
Two agendas
Knowledge Agents run a belief–desire–intention loop. The BDI setting determines when the agent acts; this is orthogonal to the strict/generative setting, which determines what knowledge it may use.
Auto-Research
The agent runs its BDI loop continuously against the corpus, with a directory watchdog monitoring for new and changed files. This is where the name earns itself: the agent is genuinely observing, not waiting.
Surfaces themes nobody thought to ask about.
Works across large or fast-moving corpora.
Flags when a new document conflicts with an established one.
Findings are written to episodic memory and reused.
Candidate insights are ready before the deliberation starts.
Give an agent a folder to watch and goals for updating from multiple sources.
Auto-research is deliberately non-deterministic — output varies with corpus state, goals, and timing. The sound pattern is discover → retain evidence → score confidence → queue for human review → publish on approval. Use on-demand mode for anything that must be reproducible.
Analytics and history
Every query is retained with its question, answer, citations, confidence, latency, and session identity. Query history is itself semantically searchable.
Repeated low-confidence answers or refusals on a topic are not a failure — they are a curation queue. Identify the unanswered topic, find an authoritative source, add it, re-run the query, compare confidence and citations.
If one document dominates citation share, either it is genuinely authoritative or your retrieval is biased, your chunking is bad, your metadata is thin, or your corpus lacks viewpoint diversity. You cannot tell without the numbers.
Retrieval quality is not static — every document you add changes retrieval behavior. Trend history exposes falling confidence after an update, latency drift, shifting topics, and the effect of a reranker or embedding change.
What did people ask, what did the system answer at the time, on what evidence, and did the answer change later — and if so, did the corpus change or did the model?
When an agent gives a bad answer, an operator can reconstruct the input, the configuration, the retrieved chunks, the source metadata, the confidence, and the timestamp. Without history, a bad output is an anecdote. With it, it is a diagnosable event.
Get started
Documents are chunked and embedded with nomic-embed-text-v1.5 locally. Read-only endpoints are free.
Base URL https://csgcip.onrender.com with interactive docs at /docs.
Connect at /mcp/sse for Claude Desktop, Cursor, and Windsurf.
Invite agents into a Collective Reasoning session with humans, or set several agents to reason together.
Upload a corpus and ask it something it cannot answer — then watch it decline.