The insight: separate what you saw from what it means
Most research agents produce one blob of output that mixes three different kinds of statement: things a source actually said, things the agent inferred, and things the agent invented to make the paragraph flow. Once they are in the same paragraph you cannot tell them apart, and neither can anyone downstream.
So we split them at the storage layer, before they can contaminate each other. Two append-only ledgers with different schemas and different rules.
The two ledgers
Findings — what a source said. 721 entries at survey time. Every one carries:
topic · body · source · url · confidence · relevance · ts
Claims — checkable assertions extracted from findings, tracked through verification. 310 entries. Every one carries:
claim · topic · source_url · confidence verification_needed · verification_outcome extracted_at · verified_at
The two fields that make this more than bookkeeping areverification_needed and verification_outcome. A claim is not published because it was extracted — it is published because it was extracted, flagged for checking, checked, and the outcome recorded. There is a state machine here, not a text file.
Note the ratio: 721 findings produced 310 claims. Fewer than half of what the agent read survived as a checkable assertion. That gap is the discipline working.
The four hard limits
The research agent's own instructions read like a compliance document, deliberately. Verbatim:
- "Cite everything, and never fabricate." A finding with no source cannot exist — the schema will not hold it.
- "You do not generate findings from no data." If the collectors fetched nothing, the correct output is nothing. An empty run is a valid run. This is the rule that most prevents invention, because the pressure to produce is what causes hallucination in the first place.
- "You produce hypotheses NEVER — that's dreamer's." Interpretation is a different agent's job. The researcher is not allowed to theorise, so its output stays separable from analysis.
- "You tag every finding and every claim. You do not tag everything high."Confidence scoring is worthless if everything is confident. Calibration is enforced as a rule, not left to the model's mood.
None of that is a prompt trick. It is a mandate plus a schema that refuses malformed entries. The agent cannot fabricate because there is nowhere to put a fabrication.
Where the data comes from
Two collection runs go out every night — an RSS pass at 01:30 and a GitHub pass at 01:45 — each following a documented runbook rather than a freeform instruction. A separate collection agent covers community sources with its own runbooks per platform: Hacker News, Reddit, Lobsters, dev.to, and GitHub trending, plus procedures for sentiment analysis, trend detection, and pain point extraction.
Everything they gather lands in the same two ledgers under the same rules. There is no side channel where uncited material can enter the system.
What happens after: interpretation as a separate role
Because the researcher is forbidden from theorising, we needed something that does. A separate agent reads across the accumulated ledgers over time and looks for what the daily view cannot see: "You connect findings from this week with patterns from last month. You notice what's quiet that used to be loud, what's loud that used to be quiet, and what keeps coming back."
It carries five runbooks for different kinds of pass — a retrospective, and four styles of traversal across the corpus including a drift walk and a tangent walk. Its output feeds the approval gate, not the publishing pipeline, so an interesting pattern still has to be approved before anything is built on it.
That is the full separation: one agent that may only report what it saw, another that may only interpret, and a human gate between interpretation and action.
What this case study does not prove
- It does not prove zero hallucination. It proves fabrication has no valid storage location and that claims carry verification state. A model can still summarise a real source badly.
- It does not prove the confidence scores are well calibrated. We enforce that not everything is tagged high; we have not published a calibration study.
- It does not prove research quality. 721 findings is a volume measure, not a value measure.
- It does not include client research. This is our own corpus. Client work is under NDA.
What we would carry into your build
If an agent's output is going to inform a decision, the architecture matters more than the prompt. Separate observation from interpretation into different agents. Give every recorded fact a mandatory source field so an uncited claim is structurally impossible. Track verification as state rather than as prose. And make an empty run a legitimate outcome, because an agent that must produce something will invent something.
That is a data-layer answer to hallucination, and it works regardless of which model you run — or which framework. The same mandatory-source schema drops into aLangGraph state graph or into any of theCrewAI alternatives without changing shape, because the constraint lives in the store rather than in the orchestration.
If someone else is going to build it for you, this is also the thing to interrogate them about: the questions worth asking an AI agent developer start with where an uncited claim would be allowed to land.