Unlike simple chatbots, AI agents tackle complex requests by first creating a detailed, transparent plan. The agent can even adapt this plan mid-process based on initial findings, demonstrating a more autonomous approach to problem-solving.

Dara Khosrowshahi argues that future travel innovation won't be in discovery, which LLMs will dominate. The real opportunity lies in creating AI agents for seamless booking and revolutionizing the "in-market" experience, such as eliminating physical hotel check-ins through mobile technology.

A user deployed AI to research obscure camper van deals in Europe, message vendors, and even complete a multi-hour travel agent licensing exam to save money. This showcases using AI not just for answers, but for executing complex, goal-oriented projects.

The true power of an AI travel agent lies not just in booking complex trips, but in handling disruptions. Glenn Fogel's goal is a system that predicts potential issues like weather or mechanical failures and re-arranges the entire itinerary—flights, hotels, cars—seamlessly before the traveler is even aware of the problem.

AI's value in logistics extends beyond raw inference. Agentic systems learn from user interactions and messy data to create and refine Standard Operating Procedures (SOPs). This allows them to handle complex, recurring tasks with increasing efficiency over time, unlike models that start from scratch.

The evolution of search won't stop with LLMs. The next stage involves autonomous AI agents that complete tasks like booking travel on a user's behalf. Marketers must shift their focus from answering human queries to ensuring their products and services are discoverable and selectable by these agents.

The next major leap for AI is its ability to connect disparate apps and data sources (email, calendar, location) to take autonomous actions. This will move AI from a Q&A tool to a proactive agent that seamlessly manages complex workflows.

Current Generative AI acts as a passive co-pilot, responding to prompts for single tasks. The emerging 'Agentic AI' is an active autopilot, capable of planning and executing multi-step workflows across different tools, fundamentally changing how complex work is accomplished.

Modern AI agents, given context from calendars and email, now anticipate user needs. For example, an agent can identify a flight booked from the wrong city and prompt the user to change it, moving beyond simple command-and-response interactions.