AI Hallucinations Are a Compliance Problem. Agentic Research Is the Answer.

Ask a general-purpose chatbot a question about a private company's capital structure and it will give you an answer. It will be fluent, confident, and formatted beautifully. Whether it is true is another matter entirely. Stanford researchers found that general-purpose large language models hallucinated on a majority of legal queries tested, with error rates ranging from 58% to 88% depending on the model, according to the study Large Legal Fictions. Even purpose-built professional research tools, a follow-up Stanford HAI benchmark showed, produced incorrect information on roughly one in six queries.

For most industries, that failure rate is an inconvenience. For an RIA operating under a fiduciary standard, it is untenable. A fabricated statistic that finds its way into a client memo, an investment committee deck, or a due diligence file is not just an embarrassment. It is a documented breach of the care a fiduciary owes, sitting in the firm's own records, waiting for an examiner to find it.

Why Generic LLMs Fail the Fiduciary Test

The problem is structural, not cosmetic. A standalone large language model is a prediction engine: it generates the most statistically plausible next word, not the most verifiably accurate one. When the model does not know something, it does not say so. It fills the gap with something that sounds right. In consumer applications, users shrug this off. In wealth management and alternatives due diligence, plausible-but-wrong is the most dangerous failure mode there is, precisely because it passes the eye test.

The stakes are rising on the regulatory side as well. The SEC's Division of Examinations made this explicit in its 2026 examination priorities, which state that examiners will assess whether firms have implemented adequate policies and procedures to monitor and supervise their use of AI technologies, and will review registrant representations about AI capabilities for accuracy. In other words, the SEC is no longer asking whether advisers use AI. It is asking whether they can prove their AI told the truth.

The SEC's 2026 exam priorities integrate AI oversight across cybersecurity, emerging technology, and automated investment tools, signaling that AI supervision will be a component of virtually every adviser examination.

That puts RIAs in a difficult position. The productivity gains from AI are real, and firms that ignore them will fall behind on speed and cost. But adopting a tool whose outputs cannot be traced to a source means importing an unquantifiable compliance risk into the heart of the advisory process.

What Agentic Research Does Differently

Agentic AI takes a fundamentally different approach. Instead of asking one model to answer everything from memory, an agentic system decomposes a research question into discrete tasks and dispatches autonomous agents to investigate each one: pulling regulatory filings, cross-referencing news coverage, checking financial disclosures, and reconciling conflicts between sources. The model is not the source of the facts. It is the orchestrator of a process that gathers, verifies, and cites them.

The difference shows up in the output. Consider Quoin's recent due diligence report on SpaceX, produced ahead of the company's anticipated IPO. Every material claim in the report is pinned to a numbered, linkable source: revenue figures traced to the company's IPO filing as reported by the financial press, launch market share traced to BryceTech industry data, and governance provisions traced to Reuters reporting on the dual-class share structure. Where sources conflict, such as employee headcount estimates that range from 13,000 to nearly 19,000 across databases, the report says so rather than picking a number that sounds authoritative.

Just as telling is what the report refuses to do. It closes with an explicit "Gaps and Limitations" section flagging what is not publicly knowable: segment-level revenue splits, Starlink unit economics, the breakdown of capital expenditure. A generative model would have papered over those gaps with plausible inventions. An agentic system trained to verify treats "this is not disclosed" as a finding, not a failure.

That distinction, between an answer that was generated and an answer that was investigated, is precisely what an RIA's compliance file needs. When a client or an examiner asks where a number came from, the answer is a click away.

Diligence Is Not a Moment. It Is an Obligation That Never Ends.

There is a second structural problem that one-time research, however accurate, cannot solve: the facts keep moving. The SpaceX report above illustrates the point within its own pages. In a span of weeks, the company introduced a new launch vehicle, suffered an FAA grounding, disclosed financials for the first time, and revealed a governance structure that drew formal concern from pension funds. An adviser who completed diligence in April was, by late May, working from a materially outdated picture.

For RIAs, staying current is not optional. Under the Investment Advisers Act, an adviser's fiduciary duty of care includes an ongoing obligation to monitor investments over the life of the relationship, and the compliance program rule requires written policies and procedures reasonably designed to keep the firm within the lines as conditions change. The SEC's 2026 Examination Priorities report reinforces the theme, with examiners focused on whether advisers' compliance programs keep pace with the products they recommend and the technologies they deploy.

The traditional answer, periodic manual refreshes of diligence files, was already straining under the volume of alternative investments flowing into advisory portfolios. Continuous monitoring is the work AI should be doing: tireless, systematic, and documented.

Quoin in Context

Quoin was built for exactly this combination of demands. Its research engine is agentic from the ground up: research questions are decomposed across specialized agents covering legal and regulatory exposure, ownership and capital history, management and governance, competitive position, and financial signals, with every claim in the resulting report cited to a verifiable source. The SpaceX report linked above is a live example readers can inspect, and Quoin's research on the top five ways RIAs can use AI in their day-to-day business maps the same capabilities onto the daily realities of an advisory practice, from client communication to portfolio research.

The platform's monitoring feature closes the loop on the ongoing obligation. Rather than treating a due diligence report as a static artifact, Quoin continuously tracks the entities an RIA cares about, surfacing regulatory actions, governance changes, litigation, and material news as they develop. The result is a diligence file that stays current between reviews, and a documented, timestamped record of ongoing oversight, which is precisely the kind of evidence the SEC's examination posture now rewards. For a deeper look at the architectural distinction between agentic systems and standalone LLMs, Quoin's earlier piece on why RIAs are turning to agentic AI for due diligence covers the ground in detail.

The Standard Is Verifiability

AI is coming to the advisory profession either way. The question each RIA faces is whether the AI it adopts can meet the standard the profession already lives by: show your work, cite your sources, and keep watching after the recommendation is made. Generative fluency does not meet that standard. Agentic verification does.

Firms evaluating how AI fits into their research and compliance workflows can explore live, fully sourced examples of agentic research at quoin.ai.