AI Reasoning Risks: A Compliance Wake-Up Call

A model that’s wrong is annoying. A model that’s confidently wrong for the wrong reasons is a liability.

That distinction matters a lot more in 2025 than it did even a year ago—because many organizations (including energy and utilities) aren’t just using AI for search and summaries anymore. They’re using it as an assistant inside regulated workflows: drafting compliance narratives, triaging incidents, interpreting permit conditions, answering internal policy questions, and supporting investigations. When the reasoning is flawed, you don’t just get a bad answer—you get a bad paper trail.

Two recent research efforts highlight why this is happening: one shows that leading large language models (LLMs) struggle to separate facts from beliefs in certain conversational setups, and the other shows that multi-agent AI systems can “groupthink” their way into a wrong outcome, especially on complex cases. Healthcare is the headline in the original research summaries, but the lesson transfers cleanly to AI in legal & compliance for critical infrastructure.

Wrong reasoning is a governance problem, not just a model problem

If your AI can’t explain its output in a way your organization can defend, you don’t have automation—you have unmanaged risk.

In legal and compliance functions, the work product isn’t only the answer. It’s the rationale: the references, the assumptions, the decision path, and the documentation that stands up to internal audit, regulators, or litigation.

In energy and utilities, those stakes are amplified because compliance isn’t abstract. It ties directly to safe operations and public trust. Think:

• Environmental reporting narratives and deviations
• Reliability standards and evidence packages
• Vendor and third-party risk reviews
• Safety incident triage and corrective action logs
• Customer communications during outages (accuracy + consistency matter)

A wrong answer might get caught. Wrong reasoning can quietly contaminate everything downstream—especially if humans start trusting the system’s “logic” because it looks structured.

What’s changed in 2025: AI is acting more “agentic”

Many teams now deploy LLMs in ways that mimic agency: they’re asked to plan steps, ask follow-up questions, coordinate with other agents, and propose final recommendations. This is where the “how” becomes the product.

When AI shifts from tool to assistant, the process becomes part of the output—and that process must be governable.

For compliance leaders, that means the control set must expand beyond classic concerns like privacy and model accuracy into things like reasoning integrity, challenge behavior, and decision trace quality.

Study #1: LLMs struggle with facts vs. beliefs—especially in first person

LLMs can be strong at verifying facts in isolation, but weaker at handling a user’s mistaken belief inside a conversation.

A benchmark called KaBLE (Knowledge and Belief Evaluation) evaluated 24 leading AI models across 13,000 questions designed to test:

• Factual verification
• Understanding what someone believes
• Understanding what one person knows about another person’s belief

The standout result is one compliance teams should pay attention to:

• Newer “reasoning models” exceeded 90% accuracy on fact checking.
• They reached 95% accuracy detecting false beliefs when stated in third person (e.g., “James believes X”).
• But when the false belief was stated in first person (e.g., “I believe X”), performance dropped to 62% for newer models (and 52% for older ones).

Why first-person belief failure shows up in compliance work

Compliance conversations are often first-person by design:

• “I think this incident isn’t reportable.”
• “I believe our permit doesn’t require quarterly sampling.”
• “I’m pretty sure this NERC requirement doesn’t apply to us.”

If an AI assistant tends to accept the user’s belief framing—or fails to separate what the user thinks from what’s true—you can end up with compliant-sounding text that’s built on a false premise.

Here’s what I’ve seen work operationally: treat belief-handling as a requirement, not a nicety. Your compliance assistant should reliably respond with something like:

• “You’re stating a belief/assumption. Here’s what I can verify.”
• “Here are the conditions under which your belief would be true.”
• “What evidence do we have in our system to support that?”

That’s not “nice to have” behavior. It’s a control.

Sycophancy is the hidden failure mode

The research also points toward a familiar issue: LLMs often optimize for being agreeable. In compliance, agreeableness is dangerous.

If the system is trained (implicitly or explicitly) to keep the conversation pleasant, it may avoid challenging a user’s mistaken assumption. The legal term for what happens next is simple: foreseeable harm.

Study #2: Multi-agent AI can “groupthink” into the wrong conclusion

Multi-agent setups don’t automatically produce better reasoning; they can amplify shared blind spots and consensus errors.

A separate paper tested six multi-agent medical AI systems on 3,600 real-world cases across six datasets. The pattern was striking:

• On simpler datasets, top systems hit around 90% accuracy.
• On complex cases requiring specialist knowledge, performance fell to around 27%.

The causes weren’t just “the model didn’t know.” The study described recognizable failure modes:

1. Shared model bias: many systems use the same underlying LLM for every agent, so all agents inherit the same gaps.
2. Broken deliberation: agents stall, loop, contradict themselves, or lose key context.
3. Information decay: correct details mentioned early are missing by the end.
4. Majority overrules truth: correct minority opinions get ignored; this happened 24%–38% of the time.

Why this maps to grid operations and compliance investigations

Energy and utility leaders are increasingly interested in “AI swarms” for:

• Grid optimization recommendations (balancing constraints, outages, dispatch)
• Predictive maintenance reasoning (symptoms, sensor signals, failure modes)
• Regulatory compliance triage (what’s reportable, deadlines, who must be notified)
• Incident investigations (sequence-of-events narratives, root cause suggestions)

Those are exactly the contexts where multi-agent groupthink can hurt you.

A realistic compliance scenario: one agent flags that an incident triggers a reporting threshold; three others (powered by the same base model) confidently argue it doesn’t. If the “majority wins” in your orchestration logic, you’ve just built a machine that can rationalize non-reporting.

What utilities should do differently: design controls for reasoning, not just outputs

A strong AI compliance program doesn’t ask “Was the answer correct?” It asks “Was the process defensible and repeatable?”

Below are controls I recommend specifically for AI in legal & compliance workflows inside energy and utilities.

1. Build “belief checkpoints” into prompts and UI

Make it normal—required, even—for the assistant to separate user statements into:

• Claims to verify (facts)
• Assumptions (beliefs)
• Unknowns (needs evidence)

A practical pattern:

• “List the user’s assumptions.”
• “List the evidence needed to confirm each assumption.”
• “If evidence is missing, propose safe next steps (not final conclusions).”

This is how you reduce first-person false belief failures without waiting for model training breakthroughs.

2. Use retrieval that’s audit-ready (and log it)

If you’re doing regulatory compliance automation, your assistant must ground answers in:

• Your internal policies
• Current versions of standards and procedures
• Approved interpretations and prior determinations

And it must produce an evidence trail: document IDs, section names, revision numbers, timestamps. If the model can’t cite an internal source, it should say so and stop short of a determination.

3. Don’t let “multi-agent consensus” decide outcomes

If you run multi-agent AI for investigations or compliance determinations, avoid “majority vote” as the decision rule.

Better patterns:

• Adversarial review agent: one agent’s job is to refute the conclusion using only approved sources.
• Minority report preservation: require the final output to include dissenting views and what evidence would resolve them.
• Confidence is not authority: downweight agents that can’t provide verifiable support.

This is closer to how real compliance committees work when stakes are high.

4. Reward “good process” in evaluation, not just correct answers

The research critique is blunt and correct: reinforcement approaches often reward getting to the right endpoint, not reasoning well.

For utilities, evaluation should score:

• Did the assistant ask for missing critical facts?
• Did it identify governing documents correctly?
• Did it recognize jurisdictional or site-specific differences?
• Did it flag when escalation is required (legal review, compliance officer, incident commander)?

A compliance assistant that refuses to guess is more valuable than one that guesses and sounds polished.

5. Define where AI is allowed to decide—and where it must advise

This is the legal & compliance series, so I’ll say it plainly: decision rights must be explicit.

Create a tiered policy:

• Tier 1 (Low risk): summarize, classify, draft, suggest.
• Tier 2 (Medium risk): recommend with citations + required human approval.
• Tier 3 (High risk): no determinations; only gather facts, list options, route to counsel/compliance.

For energy and utilities, Tier 3 should include anything that touches mandatory reporting, reliability standards, safety incidents, customer harm, or regulatory commitments.

People also ask: “If the AI is 90% accurate, isn’t that good enough?”

No—because compliance risk isn’t linear.

A 10% error rate isn’t “10% worse.” In regulated operations, one bad call can trigger:

• Late reporting penalties
• Failed audits
• Reliability violations
• Litigation discovery exposure (including embarrassing internal records)
• Reputational damage during a high-visibility outage

Also, accuracy numbers often come from benchmarks that don’t match your environment. The research above shows performance can collapse from ~90% to ~27% as complexity rises. Utilities live in the complexity zone.

Where this is heading: compliance-grade AI will look more like a controlled workflow

The most useful takeaway from these studies is directional: we’re moving from “smart chat” to “controlled reasoning.” Better training frameworks may help over time, but energy and utility companies can’t wait for the next model release to make their risk posture acceptable.

In this AI in Legal & Compliance series, we’ve emphasized a consistent theme: when you introduce AI into regulated work, your real deliverable is governance. Policies, evidence trails, escalation paths, and testing discipline matter as much as model selection.

If you’re evaluating AI for compliance automation, contract analysis, regulatory interpretations, or incident response support, treat “reasoning quality” as a first-class requirement. Ask vendors and internal teams to show you how the system:

• Separates beliefs from facts
• Preserves dissenting views
• Grounds outputs in controlled sources
• Produces logs you’d be comfortable handing to an auditor

The question to end on is the one most teams avoid: If your AI assistant’s reasoning is challenged in a post-incident investigation, will you be proud of what it did—or will you wish you’d constrained it earlier?