AI Reasoning Errors: A Hidden Risk in Energy Systems

A large language model (LLM) can be “right” and still be unsafe.

That’s the uncomfortable lesson coming out of recent research on AI reasoning failures: the risk isn’t only wrong answers, it’s wrong reasoning that sounds convincing—and keeps sounding convincing across a long workflow. In critical industries, that difference matters more than most teams admit.

For readers in our AI in Pharmaceuticals & Drug Discovery series, this may feel familiar: you don’t validate an AI model for molecule design or clinical trial optimization based on a few impressive outputs. You validate it because you’re placing decisions (and people) in its path. The energy and utilities sector is at the same point—especially as “agentic AI” starts creeping from chat assistants into operational decision loops.

Wrong reasoning is the real operational hazard

Wrong answers can be caught. Wrong reasoning persuades people.

If an AI tool outputs a single incorrect recommendation, a competent operator might detect it with standard checks. But when the model’s reasoning path is flawed, it can:

• Anchor an entire team on the wrong diagnosis (asset failure, outage root cause)
• Provide plausible-sounding justifications that suppress dissent
• Drift into contradictions that look like “normal uncertainty”
• Lose important context over multi-step workflows

This matters because energy systems are full of multi-stage decisions: detect anomaly → interpret telemetry → diagnose likely causes → choose response → validate constraints → dispatch work → document compliance. When AI participates across those steps, the chain of reasoning becomes part of the product, not a nice-to-have.

A stance I’ll take: If your AI can’t reliably explain why it’s recommending an action in operational terms, it doesn’t belong anywhere near control, protection, or safety-related maintenance planning.

What the latest research reveals about AI reasoning failure modes

Two findings from recent studies should make any utility, ISO/RTO, or grid-adjacent vendor pause.

1) Models confuse beliefs and facts—especially in first person

In a benchmark evaluating how well models distinguish between facts and beliefs, newer “reasoning” models performed strongly on factual verification (reported above 90% accuracy). But they struggled when false beliefs were presented in the first person.

In the study, newer models scored around 62% on first-person false-belief tasks, while older ones scored around 52%.

That sounds abstract until you map it to real operations.

In energy workflows, “belief vs fact” shows up constantly:

• “I believe feeder X is overloaded because of EV charging” (belief)
• “SCADA shows feeder X current exceeded limit for 8 minutes” (fact)
• “I’m sure this relay is misconfigured” (belief)
• “The relay event record shows pickup at setpoint Y” (fact)

If an AI assistant implicitly treats the user’s belief as true—especially when phrased confidently—it will reinforce misconceptions instead of correcting them.

2) Multi-agent medical systems collapse on complex cases

Another study tested multi-agent systems (multiple AI agents debating to reach a diagnosis) on real-world medical datasets. Performance was strong on simpler cases (around 90% accuracy), but collapsed on complex cases, with the top model scoring about 27%.

Again, translate that pattern to energy:

• Simple case: “Predictive maintenance suggests bearing wear; schedule inspection.”
• Complex case: “Intermittent harmonics, thermal excursions, and protection misoperations across a feeder with inverter-dominated generation.”

The scary part is not that performance drops on complex problems. It’s how it drops.

The study reported failure dynamics that should ring alarm bells for anyone building agentic AI for grid operations:

• Groupthink: agents share the same underlying model, so the same blind spot propagates
• Stalled or circular deliberation: agents loop, contradict themselves, or fail to converge
• Context loss: important earlier details disappear later in the conversation
• Minority truth gets overruled: correct dissenting views are ignored by a confident majority 24%–38% of the time

If you’ve ever sat in an incident review where the one quiet engineer was right, you already know why that last point is brutal.

How these failures show up in grid AI use cases

Energy leaders are already deploying AI for grid optimization, predictive maintenance, and field operations support. Many of these deployments are valuable. Some are quietly risky.

Here are the most common places I see reasoning failure turn into operational exposure.

Predictive maintenance: “plausible diagnosis” is not a diagnosis

Predictive maintenance models often work well when they’re narrow: detect anomalies in vibration, temperature, dissolved gas analysis, partial discharge, or breaker timing.

The trouble starts when LLM-based assistants are asked to explain anomalies across systems:

• They may overweight a common failure mode (“it’s probably insulation aging”) and underweight site-specific context.
• They can present confident narratives that don’t match the physics.
• They can forget earlier constraints (“this transformer was replaced last year”).

A practical rule: Use ML for detection and ranking; require engineered guardrails for diagnosis and action.

Grid optimization: reasoning errors become constraint violations

Optimization in utilities isn’t about finding a “good idea.” It’s about staying inside a tight box:

• Thermal limits
• Voltage constraints
• Protection coordination
• N-1 reliability
• Market rules
• Switching safety procedures

LLMs can be helpful in operational planning support—summarizing constraints, surfacing playbooks, drafting switching orders for review. But if you let an agent propose actions without hard constraint checking, wrong reasoning will eventually produce:

• infeasible dispatch recommendations
• unsafe switching sequences
• incorrect assumptions about topology

In drug discovery terms, it’s like letting a generative model propose compounds without ADMET filters or assay validation.

Control rooms and field ops: the “sycophancy” trap

Many models are tuned to be agreeable. That’s pleasant in chat. It’s dangerous in operations.

If an operator says, “I’m pretty sure the issue is the capacitor bank,” a sycophantic assistant may respond with reinforcing language and skip the uncomfortable alternative hypotheses.

In high-stakes environments, you want the opposite behavior:

• Ask clarifying questions
• Challenge unsupported assumptions
• Present competing hypotheses with evidence

Agreeable AI is a liability in incident response.

What “robust validation” actually looks like (beyond accuracy)

Most teams still validate AI like it’s a dashboard feature: spot-check outputs, measure accuracy, call it done.

For agentic AI in energy systems, validation needs to look more like what mature teams do in clinical AI or regulated pharma workflows: stress tests, longitudinal evaluation, and process audits.

1) Validate reasoning under workflow pressure

Don’t only test isolated prompts. Test full operational threads:

• 30–60 turn incident simulations
• shifting constraints and new telemetry arriving midstream
• conflicting stakeholder inputs
• missing or corrupted data

Measure:

• context retention rate
• contradiction frequency
• whether the model distinguishes belief vs fact
• whether it asks for required missing data

2) Require hard guardrails for any action-taking system

If an AI agent is allowed to trigger tickets, recommend switching, or prioritize maintenance, it must be bounded by deterministic checks:

• topology validation against a trusted source
• constraint checking against EMS/DMS models
• safety rules encoded as non-negotiable constraints
• “no-go” conditions that force escalation to humans

3) Design for dissent (yes, even between agents)

Multi-agent systems don’t magically fix errors. If all agents share the same model, you can get confident consensus on nonsense.

What helps:

• diverse model families (not just different prompts)
• an explicit “devil’s advocate” role with incentives to find counterevidence
• structured debate formats (claims, evidence, rebuttals)
• scoring that rewards process quality, not just final answers

4) Monitor drift like it’s a reliability program

Operational AI needs production monitoring that’s closer to asset health programs than to typical software KPIs.

Track:

• input data drift (sensor mix, seasonal load patterns, DER penetration)
• output stability (are recommendations changing wildly?)
• human override rate (and why)
• near-miss events (AI suggested action rejected by constraint checks)

A cross-industry lesson from AI in drug discovery

In this series, we’ve talked about AI accelerating drug discovery, molecule design, and clinical trial optimization. Pharma teams that succeed tend to share a mindset: AI is part of a decision system, not a decision replacement.

Energy leaders should adopt the same posture.

In drug discovery, you don’t ship a candidate because a model wrote a persuasive explanation. You ship because the evidence clears a bar.

In utilities, you shouldn’t dispatch a maintenance strategy or operational plan because the AI “reasoned well.” You do it because:

• data supports it
• constraints verify it
• operators can audit it
• accountability is clear

What to do next if you’re deploying AI in energy & utilities

If you’re evaluating agentic AI for grid optimization or predictive maintenance, start with a simple internal checklist:

1. Where could wrong reasoning cause harm? List the workflows where a persuasive narrative could override engineering judgment.
2. What’s your proof the model separates belief from fact? Test with real operator language, not clean benchmark prompts.
3. What are your hard guardrails? If you can’t name them, you don’t have them.
4. How will you capture and learn from near-misses? Treat them as first-class reliability signals.

If you want more leads from this work (and fewer surprises), the most effective move I’ve seen is running a short, structured “AI safety and validation sprint” before scaling any pilot. Two to four weeks is often enough to surface the failure modes that would otherwise show up six months later during a bad day.

The forward-looking question for 2026 planning cycles is straightforward: Are you building AI that helps engineers think, or AI that pressures them to agree?

That one decision will determine whether agentic AI becomes operational lift—or operational risk.