When an AI Agent Encounters the Unexpected: A Hypothetical Case Study on Context Drift

Imagine a travel agency that has integrated AI into its customer service operations. The agency specializes in coordinating entire vacation packages—from flights and transfers to hotel stays and excursions. Airlines frequently alter departure times without notice, ranging from a 10-minute delay to a full 24-hour shift. It’s the agency’s responsibility to spot these changes quickly, then decide if they’ll affect onward transfers or hotel reservations.

To handle this, the agency deployed AI agents to manage most flight-update scenarios automatically—emailing customers about new times, rebooking transfers if needed, and double-checking hotel arrivals. On paper, this looked like a game-changer. But the real hurdle wasn’t the flight changes themselves; it was the context drift that emerged when customers wanted more than just a schedule fix.

The Initial Focus: Simple Flight Updates

The team trained the AI agent to detect airline schedule changes and adjust bookings accordingly:

• If a flight was delayed by 30 minutes, it would alert the traveler and update their pickup service.
• If it was an earlier arrival, it would confirm whether the hotel could handle a check-in at a new time.

By all accounts, the system worked flawlessly for a while—until customers began throwing in extra requests.

First Signs of Context Drift: Branching Conversations

Some travelers started tacking on additional tasks after learning about the schedule change:

• “By the way, can I switch my city tour from the morning to the afternoon because of this delay?”
• “Now that my friend’s flight is also late, can I add an extra room for them at the hotel?”

Suddenly, the AI had to deal with brand-new information—excursions, more travelers, different accommodation requirements. The model had been trained to address flight delays only, so anything outside that context seemed irrelevant to its decision-making. The AI stuck to the script, responding with updates purely about flight times, leaving the rest unanswered.

This is context drift: the world you trained your AI for changes in ways it cannot yet handle. What began as a standard flight-delay conversation evolved into something else entirely.

Major Shift: Demands for Full Refunds

Occasionally, the inconvenience pushed customers to more extreme measures: “Our new arrival is 12 hours later—just refund the whole trip.” An AI optimized for schedule tweaks didn’t know the slightest thing about refund policies, insurance constraints, or even how to respond to an emotional reaction like, “I’m done with this!”

The result? The AI repeated its same “your flight has been rebooked” lines or generated an irrelevant apology. Customers, expecting empathy or a workable solution, saw only an impersonal mismatch.

The Human Context Drift Parallel

Interestingly, humans also drift in context—but we handle it very differently. Consider a typical face-to-face conversation: we might start talking about travel delays, then seamlessly shift to an anecdote about our last vacation. We often do this without any formal marker, expecting the other person to keep up. We change our line of reasoning on the fly, trusting that the listener adapts to these mental leaps.

• Unwritten Reasoning: In a human conversation, we regularly draw conclusions or shift topics in our heads without announcing it out loud. “By the way, if I’m arriving late, I’ll need a different excursion,” or “I might as well cancel the trip if it’s one big hassle.”
• Social Cues & Emotional Changes: Humans pick up clues—tone of voice, facial expressions, or subtle wording. We can quickly sense a change in someone’s intent or emotional state and pivot our responses.

An AI agent, on the other hand, remains bound to the original thread (in this case, flight updates) unless explicitly instructed otherwise. It doesn’t read between the lines to realize, “Wait, the user just switched topics and is now thinking about a refund or a different part of their vacation.” So while a human listener might immediately sense the shift, an AI stays locked in its last known context unless we program it to detect and adapt to the new angle.

Compounding Effects of Unshared Human Logic

Picture a traveler who reasons: “A flight delay means a later arrival—therefore my pre-booked tour is now useless—maybe I should just cancel the excursion, or the entire trip.” None of this reasoning has been explicitly communicated to the AI. Instead, the traveler might jump straight to, “I’m getting a refund, right?”

For the AI, this leap appears out of nowhere. It has no log of the traveler’s mental steps—the agency’s system only sees the final statement: “I want a refund.” Without a robust, context-aware mechanism, the AI tries to address the flight delay aspect again, completely missing the broader mindset that led the traveler to demand a cancellation.

Building AI Resilience to Context Drift

1. Expand the Training Scope

Train your AI not just on “flight schedule changes,” but also on subtopics like refund scenarios, passenger additions, and excursion modifications. The more conversation layers your AI recognizes, the better it can follow unexpected twists.

2. Iterative Updates & Human Intervention

It’s essential to let humans intervene when the AI encounters an unfamiliar request. Real agent responses then become training data for the next iteration, closing the loop on new forms of context drift.

3. Markers & Categorization

Encourage the system to detect cues or keywords in user queries—like “refund,” “additional passenger,” or “changing hotels”—to pivot its conversation logic accordingly.

4. Recognize Human-Like Jumps

Give the AI a mechanism to spot sudden topic shifts, akin to how humans track a conversation flow. If the user leaps from “flight delay” to “full cancellation,” the AI should be primed to ask clarifying questions.

Balancing AI and Human Oversight

No AI—no matter how advanced—can capture all the unspoken logic humans apply daily. Maintaining a hybrid approach, where tricky scenarios escalate to a human support member, mitigates frustration. Over time, the AI learns from these edge cases, slowly bridging the gap between static scripts and fluid, real-world dialogue.

Conclusion

Context drift isn’t just about an AI’s inability to handle new data. It’s about a user (the travel agency’s customer) bringing to the table unspoken leaps in logic, emotional nuances, and additional details that may not align with the AI’s original training. Humans handle these abrupt shifts naturally; AI agents do not—unless we anticipate them.

By acknowledging the parallel with human context drift and the fact that we often leave out crucial reasoning when we change our minds, we can design AI systems that better adapt to unexpected twists. Whether it’s a minor flight delay that morphs into a refund demand, or an innocuous booking question that escalates into a total itinerary overhaul, preparing for context drift ensures our AI-powered travel services can keep pace with travelers’ evolving needs—no matter how quickly the conversation changes.