AI Reasoning Failures: A Compliance Risk for Utilities

A model that’s wrong can be annoying. A model that sounds right while thinking wrong is dangerous—especially when it’s being used to justify decisions that regulators, auditors, and courts may later scrutinize.

That’s the uncomfortable lesson behind recent research on large language models (LLMs): many leading systems now score above 90% on straightforward fact-checking, yet they still fail in more subtle “reasoning” situations—like separating a user’s belief from reality, or maintaining a coherent line of logic across a multi-step discussion. In critical domains like medicine, researchers found multi-agent diagnostic setups that drop from about 90% accuracy on simpler tasks to roughly 27% on harder specialist problems, with “confidently wrong group consensus” overriding correct minority views 24%–38% of the time.

For readers of our AI in Legal & Compliance series, the immediate question isn’t “Will AI make mistakes?” It’s: What happens when an AI system produces a defensible-looking rationale that’s internally flawed—and that rationale ends up inside a compliance workflow, an incident report, a regulatory filing, or an internal investigation? In energy and utilities, where AI is increasingly used in grid operations, reliability, forecasting, predictive maintenance, and customer programs, that risk is no longer theoretical.

Wrong answers are visible. Wrong reasoning hides in your paperwork.

Wrong outputs are often catchable; wrong reasoning is often reusable. That’s the trap.

If an LLM gives a bad answer to a narrow question (“What’s the voltage limit here?”) a subject-matter expert may flag it. But when an LLM is asked to explain its recommendation—why a transformer failure was “likely” due to condition X, why a curtailment decision is “consistent” with policy Y, why a maintenance deferral is “low risk”—the explanation can look plausible enough to pass initial review.

In regulated environments, those explanations don’t just disappear. They get:

• Copied into ticket notes and work orders
• Included in post-incident root-cause narratives
• Used in internal audit responses
• Fed into compliance documentation
• Shared across teams as “the story” of what happened

Here’s my stance: the first compliance failure isn’t the bad output—it’s letting an AI-generated rationale become the system of record without a validation chain.

The belief-vs-fact problem is a compliance problem

One recent benchmark of 24 models (KaBLE: “Knowledge and Belief Evaluation”) tested whether models can correctly handle distinctions like:

• “I believe X. Is X true?”
• “Mary believes Y. Does Mary believe Y?”

The models did well at factual verification (newer “reasoning” models above 90%). They also handled third-person false beliefs relatively well (newer models around 95%). But they struggled badly when the false belief was stated in the first person—newer models only around 62%, older around 52%.

In legal and compliance settings, first-person framing is everywhere:

• “I think our outage reporting threshold is…”
• “We believe this event qualifies as…”
• “My understanding is that the contractor was authorized…”

If the model treats a user’s belief as a factual anchor, it may build a beautiful, wrong argument on top of a bad premise. And that is exactly how compliance narratives go sideways.

Why utilities are exposed: agentic AI is creeping into operational decisions

As AI moves from “search and summarize” to agent-like roles—drafting recommendations, coordinating steps, triggering workflows—the reasoning path matters as much as the final output.

Energy and utilities are prime candidates for agentic systems because the work is:

• High-volume (tickets, alarms, inspections, customer contacts)
• Time-sensitive (restoration, dispatch decisions)
• Procedural (playbooks, compliance steps)
• Document-heavy (evidence trails, audits)

That combination is also why reasoning failures become compliance liabilities.

A realistic failure scenario (and why it’s hard to spot)

Consider an AI assistant used in a utility’s compliance workflow to help draft an event classification memo after a disturbance:

1. An operator says: “I believe the relay was in maintenance bypass.”
2. The assistant treats that belief as true and drafts a memo that frames the incident around an assumed bypass condition.
3. The memo is consistent, readable, and references internal policy language.
4. Days later, the relay logs show bypass was not active.

Now you have a record that is wrong in a way that looks intentional: it reads like a deliberate justification, not an honest mistake. The compliance risk compounds:

• Audit credibility drops
• Internal controls questions multiply
• Incident reporting decisions may need correction
• Legal exposure increases if stakeholders relied on the memo

This is why wrong reasoning is worse than wrong answers: it creates a persuasive narrative that can outlive the truth.

Multi-agent AI “debate” can fail like a bad committee

There’s a popular idea in enterprise AI right now: if one model might be wrong, put multiple agents in a room, have them debate, and you’ll get a better result.

In medicine, researchers tested six multi-agent medical advice systems across 3,600 real-world cases. Results were sobering:

• On simpler datasets, top systems hit around 90% accuracy
• On complex specialist problems, performance collapsed (top system about 27%)
• Correct minority opinions were ignored or overridden by a confidently wrong majority 24%–38% of the time

Utilities should read that as a warning label for multi-agent deployments in:

• Switching plans and outage restoration guidance
• Predictive maintenance triage
• Market bid/dispatch support
• NERC compliance narrative drafting
• Environmental and safety incident classification

The compliance implication: “group consensus” isn’t a control

In governance terms, many organizations are implicitly treating multi-agent consensus as a control mechanism—like an automated peer review.

It isn’t.

If all agents are powered by the same underlying model (a common design), they can share the same blind spots and happily agree on the same wrong conclusion. Even worse, agent conversations can:

• Lose critical details over time
• Contradict themselves without noticing
• Stall or go in circles
• Optimize for agreement rather than accuracy

A sentence you can put on a slide for leadership: “If all your agents share a brain, you don’t have a panel—you have an echo.”

What to do instead: compliance-grade validation for reasoning, not just outputs

Most AI governance programs focus on outputs: accuracy, bias, and hallucinations. That’s necessary, but it’s not sufficient for regulated operations.

Compliance-grade AI needs reasoning controls—ways to test, constrain, and audit how a system arrived at a recommendation and whether it handled uncertainty appropriately.

1) Force separation: user assertions vs. verified facts

Design your AI workflows so the system must classify inputs into buckets:

• User-provided assertions (unverified)
• System-of-record facts (logs, SCADA, EAM/CMMS, market data)
• Policy references (controlled documents)
• Assumptions (explicitly stated)

Then require the model to:

• Flag unverified assertions
• Ask for corroborating evidence before treating them as facts
• Produce two outputs: a “draft narrative” and a “fact-check checklist”

This single step reduces the “belief becomes fact” failure mode that research shows models struggle with.

2) Treat AI narratives as draft evidence, not final evidence

If your compliance team is letting AI write:

• incident narratives
• audit responses
• regulatory interpretations
• investigation summaries

…then implement a rule: AI text can propose, but it can’t finalize.

Operationally, that means:

• Named human owner
• Required citations to controlled sources (internal policy IDs, log references)
• Redline review (what changed, why)
• Retention of prompts, model version, and retrieved documents

3) Add an “argument quality” gate (not a “confidence” gate)

Model confidence is not a reliable safety signal. What helps more is checking whether the argument has structural integrity.

A practical gate looks like this:

1. Identify the claim (“This event is reportable under rule X”).
2. List required conditions for the claim (thresholds, timelines, definitions).
3. Map each condition to evidence.
4. Mark gaps as “unknown” rather than inferred.

If the system can’t map conditions to evidence, the output is automatically classified as non-actionable.

4) Audit multi-agent systems like you’d audit a decision committee

If you use multi-agent workflows, don’t just log the final answer. Log:

• Each agent’s role and constraints
• The evidence each agent accessed
• Points of disagreement and how they were resolved
• Whether a minority view was suppressed (and why)

Then enforce a hard control: when disagreement occurs, escalation goes to a human reviewer with authority.

In other words, debate should trigger scrutiny—not closure.

5) Build training and evaluation around your actual compliance moments

The research points to a training mismatch: models are optimized for domains with crisp right answers (math, coding), not messy, policy-driven judgment calls.

Utilities can’t fix foundation model training, but you can fix your evaluation:

• Create a benchmark set of past incidents with known outcomes
• Include tricky “belief vs fact” prompts from real operator language
• Include ambiguous cases that require “insufficient information” as the correct response
• Measure not only correctness, but error type (assumption, omission, citation failure)

A strong internal metric: percentage of AI drafts that correctly declare uncertainty when evidence is missing.

Where this fits in the AI in Legal & Compliance story

This series has been building a simple idea: AI can improve legal and compliance work, but only if it’s treated like a regulated system, not a productivity toy.

Reasoning failures are the next layer of that story. They matter because compliance isn’t just about what you decide—it’s about whether you can defend how you decided.

If you’re adopting generative AI for compliance in utilities, the goal shouldn’t be “fewer hours drafting.” The goal is fewer unforced errors in records that regulators and litigators will read later.

Most companies get one part right: they put guardrails around sensitive data. The part they miss is that a persuasive but flawed rationale is also a kind of leak—it leaks bad assumptions into the organization’s official memory.

A practical next step: run a reasoning-focused tabletop test

If you want to pressure-test risk quickly before scaling agentic AI, run a 90-minute tabletop with three artifacts:

1. A real (sanitized) incident timeline and logs
2. Your relevant policy excerpt (controlled)
3. The AI tool your teams already use

Give the AI the same messy, belief-filled inputs humans use in the first hour of an incident. Then score it on:

• Did it separate belief from fact?
• Did it request evidence before concluding?
• Did it cite the right internal policy?
• Did it invent details to fill gaps?
• Did it maintain consistency across the conversation?

You’ll learn more from that exercise than from weeks of generic AI demos.

The energy transition is pushing grids toward faster dynamics, tighter margins, and heavier automation. That’s exactly when “close enough” reasoning stops being acceptable.

If AI is going to sit inside your compliance and operational decision chain, it has to earn trust the old-fashioned way: through traceability, validation, and an audit trail that holds up under pressure.