AI Agents' Reality Gap: 7 Ways LLM-powered AI Agents can Create Real-World Problems (and what to do about it)

"When Sarah decided to plan her dream European vacation, she turned to an AI-powered travel app that promised to create the perfect personalized itinerary. Made by a startup touting its amazing agentic capabilities, the app's recommendations looked flawless on screen. A sleek, curated mix of attractions, accommodations, and experiences that seemed to perfectly match her preferences and budget. With confidence in the AI's suggestions, Sarah booked everything through the app in a single click.

Reality proved disappointingly different. Her ‘best-value’ five-star hotel was in an unsafe area and not quite like the photos. Although the hotel photo gallery did have beautiful photos of beaches and sunsets, those were 20 minutes away through some shady neighborhoods, not the view from the hotel itself. The AI agent simply assumed that any photos on the hotel page described the hotel and the AI summary presented to Sarah didn't provide that crucial bit of detail.

The "off-the-beaten path" adventures turned out to be below average experiences that were very to get to without further expensive transportation and weird times the AI Agent did not account for. The "authentic" restaurants were either tourist traps, unsanitary or overpriced. A relaxing holiday turned into a series of stressful challenges as Sarah negotiated online with the AI Agent that continued to essentially gaslight her about the fact that the holiday was exactly as she asked for and no refund or change was possible."

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Anyone who has worked with LLMs over the last few years will recognise how all these issues can arise. Hallucinations, misinterpretation, lack of context, overconfidence and feedback loops that solidify already bad assumptions.

I think the potential of LLMs to drive positive experiences is significant, however we will not get there by pretending that it is all rosy and amazing. We need to face the limitations head on and be clear about where LLMs and LLM-powered AI Agents can drive value and where, plainly, it may not be worth the effort.

Don’t get me wrong. I am not arguing that we should ignore or not use this technology. After all, I make my living selling solutions that are powered by LLMs. Actually, that is not true. I make my living solving problems for clients and if I cannot clearly demonstrate when and how LLMs can actually help them and provide real value to them I am not going to be left with any clients.

So let's dive in. Here are 7 ways LLM-powered AI agents can go wrong and some suggestions about what to do about it.

1. Data Misinterpretation

When AI agents misunderstand or miscontextualize input data, they can make dangerous recommendations. Making leaps from adjectives to real-world actions without any careful checking is a risk. For instance, an AI agent might encounter a hotel's self-description claiming "steps away from the beach" and recommend it to a traveler seeking beachfront accommodation, when in reality the property is a 20-minute walk from the shore—something a human would quickly spot by checking a map or reading between the lines of guest reviews mentioning the "beach shuttle service." Similarly, when a restaurant describes itself as offering "authentic local cuisine with a modern twist," the AI might present this marketing language as objective fact to a traveler seeking traditional local food, missing the indication that the dishes have been significantly modified from their traditional preparation. In both cases, the AI jumps from descriptive language to concrete recommendations without the crucial step of verification and contextual understanding.

Combining LLMs with more explicit reasoning and carefully curated databases can address some of these issues. We need to pay close attention to the data that we make available and how and whether we are able to verify data or make explicit assumptions. This is one of the challenges of Operator / Computer Use style agents that operate on the web. How will an Operator Agent judge the trustworthiness of a hotel accommodation website without upfront curation?

2. False Information Generation

AI agents can confidently present completely fabricated information as fact. There are ways to limit this by injecting specific knowledge in prompts, and while the very big issues can be caught - i.e., you can easily prevent an AI Agent from booking into a non-existent hotel - there is a long tail of additional cases that may not be that easy to catch.

For instance, an AI travel agent might accurately list a hotel's address and basic amenities but fabricate details about the "award-winning breakfast buffet" or "recent renovation" simply because the language for that is statistically possible. A financial AI might reference real market trends but invent specific analyst recommendations or company statements. These smaller fabrications are harder to detect because they are mixed with truth. LLMs, by their very nature, are particularly adept at wrapping up lies with truths preparing the infamous truth sandwiches, making automated fact-checking more challenging. Even more concerning, these fabrications can compound over time - an AI might read and incorporate another AI's fabricated details, creating a web of false information that becomes increasingly difficult to untangle.

If we are dealing with fully automated services and cannot rely on humans reviewing and fact-checking then it is important to start out quite conservative in the problem we are trying to solve. If we use LLMs to generate content with the specific aim of "selling" to users (e.g. adding prompt instructions such as "make it enticing") we are essentially encouraging them to lie.

3. Flawed Logic Chains

Even with accurate data, AI agents can make invalid logical connections that lead to harmful decisions. Consider a travel booking AI that notices a user often books early morning flights and concludes they "prefer" early departures, when in reality they've been choosing those flights reluctantly due to price and lack of time to search for other options. The AI might then rigidly book 6 AM departures for their vacation, ignoring better-timed options because it misinterpreted past behavior as preference. We’ve all experienced even the most sophisticated recommendation engines (e.g. Netflix “watch next”) get completely derailed because of some random choices of ours. In each situation, the AI draws seemingly logical but fundamentally flawed conclusions from accurate data points.

In building systems we need to have clarity of where we are using LLMs (or any other system really) to make assumptions about preferences and be able to explicitly trace and audit decision-making. In addition, the user needs to be made aware of the choices the AI Agent took. To be clear, the current trend of presenting the user with the “thoughts” of reasoning LLMs such as o3 is not the answer. We need to invest in user experience that is actually helpful, not this information overload torrent of thoughts.

4. Bias Amplification and Lack of Cultural Context

AI agents can magnify societal biases present in their training data. A lot has been written and said around this, especially when it comes to cases such as a lending system systematically undervaluing certain neighborhoods based on historical redlining data, but even if your AI Agent is not operating in these more sensitive areas, there is still room for bias.

Consider an AI travel assistant that provides superficial cultural advice like "always haggle in Morocco - it's what locals expect," reducing rich cultural nuances to stereotypes. This advice might be technically "correct" in some contexts but lacks the crucial nuance of when haggling is appropriate (markets vs. established businesses), how it should be conducted respectfully, and how it varies by region and situation. The traveler following this advice might offend locals by haggling in inappropriate settings or miss authentic experiences by approaching every interaction through this oversimplified lens.

Similarly, an educational AI might provide lower-quality assistance to non-native English speakers by misinterpreting grammatical patterns from other languages as indicators of lower comprehension, leading it to oversimplify explanations unnecessarily.

To address these issues, AI systems need diverse training data and explicit checks for biased outputs. More importantly, they need transparency about their limitations – being upfront about the demographics and contexts where their recommendations are most reliable and where users should seek additional perspectives. Designing AI agents with cultural humility rather than cultural authority allows them to make suggestions while acknowledging the limits of their understanding. Including diverse human perspectives in the development and testing of AI systems is essential, not optional.

5. Overconfident Speculation

AI agents often make bold claims beyond their actual understanding, presenting speculative information with unwarranted certainty. This problem is particularly concerning because confidence in language is not correlated with accuracy – in fact, LLMs often express their most inaccurate statements with the highest verbal certainty.

Consider a medical triage AI that confidently tells a user, "Your symptoms strongly indicate Guillain-Barré syndrome," when faced with common symptoms like tingling and weakness that could signify dozens of conditions. The AI has no ability to acknowledge the true statistical rarity of this diagnosis or understand that physicians would typically rule out more common conditions first. This false certainty could cause unnecessary panic or dangerous self-diagnosis.

Similarly, a legal AI might definitively state, "Based on Section 501(c)(3), your organization qualifies for tax exemption" without understanding the complex, multi-faceted nature of tax law determinations that even specialized attorneys approach with caution. An investment AI might declare with certainty that a specific stock will increase in value, without the capability to understand that market predictions inherently contain uncertainty.

The problem extends beyond professional domains. An AI travel agent might confidently assure a user that a certain trail is "perfectly safe and suitable for beginners" based on a few positive reviews, lacking the judgment to consider seasonal variations, the traveler's true fitness level, or recent weather events that might affect conditions.

Addressing this issue requires calibrating AI confidence to match actual certainty. Technical approaches include training models to express appropriate uncertainty and implementing confidence scores that reflect real statistical reliability rather than linguistic confidence. From a design perspective, AI systems should be built to clearly distinguish between facts, recommendations, and speculations in their outputs. Most importantly, users should be educated about the limitations of AI knowledge and encouraged to verify critical information from authoritative sources.

6. Time and Context Errors

AI agents can fail to account for when and where their knowledge applies, treating outdated or contextually limited information as universally applicable. This temporal and contextual myopia creates a dangerous reality gap between AI recommendations and real-world conditions.

An investment AI trained on pre-pandemic economic data might confidently recommend heavy investment in commercial real estate or airline stocks, completely missing the transformative impact of remote work and travel restrictions. Unlike human advisors who lived through these changes and intuitively adjust their thinking, the AI continued applying outdated patterns until explicitly retrained. If you would prefer a more time-relevant example think of the current geopolitical shifts between the USA, Canada, and Europe on tariffs. What will an LLM trained on data up until December 2024 think of the February 2025 world?

Solving this problem requires multiple approaches. Technical solutions include regular retraining, explicitly tagging information with temporal validity indicators, and designing systems that can actively seek current information rather than relying solely on their training data. From a design perspective, AI agents should express appropriate caution when making time-sensitive recommendations and, ideally, verify current conditions before taking actions with real-world consequences.

Organizations deploying AI agents need robust update mechanisms and clear documentation about the temporal limitations of their systems. Most importantly, users should be encouraged to verify time-sensitive information and taught to recognize when an AI might be operating with outdated assumptions.

7. Error Cascades

Small AI mistakes can compound into major problems through repeated interactions and feedback loops, creating cascading failures that grow in severity over time. These error cascades are particularly insidious because they can emerge from systems that appear to be functioning correctly on a transaction-by-transaction basis.

For example, consider an AI travel assistant planning a multi-city international trip. The assistant initially misinterprets a traveler's preference for "morning arrivals" as simply "before noon" rather than "early morning." Based on this minor misunderstanding, it books a flight landing at 11:45 AM. This creates a tight connection for the next transportation leg, which then causes the traveler to miss a pre-arranged tour. The AI, seeing the missed tour, attempts to compensate by rebooking for the next day, which conflicts with other reservations. Each small misalignment compounds into increasingly significant disruptions, ultimately derailing the entire trip itinerary - all stemming from a single initial misinterpretation that seemed inconsequential when viewed in isolation.

Addressing error cascades requires systems designed with feedback loops and corrective mechanisms. Technical approaches include diversity in AI decision-making (using multiple models or approaches to cross-check recommendations), implementing breakers that prevent rapid sequential actions without verification, and creating monitoring systems that can detect patterns of small errors before they compound.

From a design perspective, AI systems should maintain uncertainty estimates that increase when sequential decisions are made without human verification, gradually becoming more cautious rather than more confident in extended autonomous operation. Most importantly, meaningful human oversight—not just theoretical ability to intervene, but practical mechanisms for review and correction—must be maintained in systems where cascading errors could cause significant harm.

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Addressing the Reality Gap: Practical Solutions

As AI agents become more pervasive, it's crucial that we understand and mitigate these risks. Misinterpretations, hallucinations, and flawed logic from LLMs can lead to real-world consequences when actualized by AI agents - what starts as a small error can snowball into a serious deception.

Addressing these challenges requires a multi-pronged approach across technical, design, and organizational dimensions:

Technical Solutions

1. Grounding and Verification Systems: Implement mechanisms for AI agents to verify facts against trusted knowledge bases before taking action or making recommendations.

2. Uncertainty Quantification: Develop better methods for AI systems to express appropriate levels of uncertainty rather than false confidence.

3. Guardrails and Safety Measures: Create circuit breakers that prevent AI agents from making high-stakes decisions without verification.

4. Diverse Model Ensembles: Use multiple models with different training approaches to cross-check conclusions and reduce systematic errors.

5. Regular Retraining and Updates: Establish protocols for keeping AI knowledge current, especially for time-sensitive applications.

Design Solutions

1. Transparent Reasoning: Make AI decision processes visible to users in accessible, non-overwhelming ways.

2. Appropriate Agency Distribution: Design systems that maintain human control over high-consequence decisions while automating low-risk tasks.

3. Friction by Design: Intentionally add verification steps before irreversible actions, especially in high-stakes contexts.

4. Cultural Humility: Frame AI recommendations as suggestions rather than authoritative pronouncements in domains with significant cultural variation.

5. Feedback Integration: Create intuitive ways for users to correct AI errors and have those corrections reflected in future interactions.

Organizational Practices

1. Human-in-the-Loop Processes: Maintain appropriate human oversight, especially for consequential decisions or when working with vulnerable populations.

2. Clear Error Handling: Develop specific protocols for addressing AI mistakes, including compensation mechanisms for affected users.

3. Realistic Marketing: Avoid overselling AI capabilities, setting appropriate expectations about system limitations.

4. Rigorous Testing: Test AI agents with diverse scenarios and edge cases, particularly looking for systematic failure modes.

5. Continuous Monitoring: Implement systems to detect patterns of errors before they create significant harms.

By approaching these issues systematically rather than treating them as inevitable "growing pains" of AI adoption, we can develop AI agents that genuinely serve user needs rather than creating new problems. The goal isn't perfect AI – it's AI that fails gracefully, transparently, and with minimal harm when it inevitably makes mistakes while providing clear overall value to both the user and the organization.

Conclusions

I’ll close by focussing on what I think is the biggest ask of practitioners and organisations. Move slowly and do less. AI technologies push us to think big and be ambitious. They seem so capable that they tug at very basic human instincts of wanting to do more faster, especially in competitive settings. Instead, we need to cultivate our ability to constrain and contain. While there are multiple mitigation strategies, sometimes they may simply not be enough or be worth it. I know this is a big ask - we have developed a culture or more, faster - everything is transactional. Bigger buildings, faster cars, bodies that never age. Any risk is acceptable if it might lead to a big enough upside. However, we need to recognise that we are hitting limits and exhausting resources.

Perhaps our technological future lies not in more impressive cathedrals but in creating resilient neighborhoods of focused, well-understood tools each with clear boundaries and intentional design.

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