An open-source AI agent successfully navigated Google Flights and automated a flight booking demonstration, but the achievement highlights broader challenges preventing widespread adoption of autonomous travel shopping. Legal ambiguities and platform restrictions create barriers more substantial than the technical capabilities required to execute such tasks.

Two researchers published an analysis on January 16 examining why artificial intelligence agents struggle with consumer tasks like flight bookings despite recent advances in browser automation technology. According to Alex Imas and Andrey Fradkin, writing on the Substack publication Ghosts of Electricity, the obstacles involve both internet infrastructure designed exclusively for humans and unclear legal frameworks surrounding AI agent authorization.

A developer named paquino11 demonstrated practical implementation of these concepts by creating a working system combining Browser Use framework with Google's Gemini 2.0 Flash model. The demonstration, shared on Reddit's r/automation community approximately one year ago, showed an AI agent autonomously navigating to Google Flights and executing flight search operations for routes between Gothenburg and London with specific travel dates from March 1 to March 10, 2025.

According to the project documentation on GitHub, the system initialized a language model using Gemini 2.0, deployed browser automation capabilities, navigated to Google Flights, and attempted to complete booking workflows. The developer described it as "cool to find out an open-source alternative to OpenAI Operator, and free, since Gemini 2.0 Flash is currently free of charge."

Technical capabilities exceed practical deployment

The Browser Use framework represents emerging infrastructure for agentic AI systems operating autonomously through web interfaces. McKinsey data indicates $1.1 billion in equity investment flowed into agentic AI in 2024, with job postings related to this technology increasing 985 percent from 2023 to 2024.

The flight booking demonstration occurred as multiple platforms launched AI agents for marketing automation. Amazon introduced Ads Agent on November 11, 2025, automating campaign management tasks. Adobe deployed Experience Platform Agent Orchestrator on September 10, 2025, creating an AI platform for managing agents across Adobe and third-party ecosystems.

According to Imas and Fradkin, AI agents encounter fundamental obstacles when navigating human-optimized web interfaces. Seat selection systems present particular challenges because interactive elements change state dynamically. Prices appear only after clicking, availability updates continuously as other customers complete purchases, and visual elements respond to hover interactions that agents struggle to interpret correctly.

The researchers described watching an agent attempt flight booking through ChatGPT Atlas. According to thinking traces visible during execution, the agent experienced confusion and misdirection trying to interact with website elements optimized for human spatial reasoning and visual processing. The entire process consumed substantially more time than human completion would require and ultimately resulted in errors.

Platform control through terms of service

Legal frameworks present obstacles distinct from technical implementation challenges. According to Imas and Fradkin, fundamental questions remain unresolved about whether AI agents inherit user access rights to websites where those users hold legitimate accounts and accepted terms of service.

Platforms maintain terms prohibiting "any use of data mining, robots, or similar data gathering and extraction tools." AI agents navigating websites arguably fall under these restrictions even when acting on explicit human instructions using credentials belonging to those humans. According to the analysis, platforms can claim agents must identify themselves as automated systems rather than masquerading as standard web browsers.

The Computer Fraud and Abuse Act provides platforms additional leverage through precedent established in Facebook v. Power Ventures. The Ninth Circuit held that continued access after explicit prohibition constitutes unauthorized access under federal computer fraud statutes. According to the court's language cited in the analysis, "Once permission has been revoked, technological gamesmanship will not excuse liability."

This legal framework creates asymmetric power dynamics. Platforms can develop "bowling-shoe agents" while blocking "bring-your-own" agents. According to Imas and Fradkin, users can only deploy agents controlled by platforms themselves, which may not operate in user interests when platform business models benefit from restricting agent capabilities.

Industry disputes over web scraping demonstrate platform resistance to automated access. Google filed a lawsuit against SerpApi on December 19, 2025, alleging the Texas company violated the Digital Millennium Copyright Act by circumventing SearchGuard protections. Reddit sued SerpApi, Oxylabs, AWMProxy, and Perplexity AI on October 22, 2025, for circumventing anti-scraping measures.

The proliferation of AI agents masquerading as human browsers complicates legitimate use cases. Security researchers at DataDome and WebDecoy documented AI agents adopting adversarial tactics including user agent spoofing, distributed IP rotation, and rapid parallel requests to bypass website defenses. xAI's Grok triggered 16 requests from 12 IP addresses using spoofed user agents, according to the research.

Competition implications drive resistance

Platforms derive substantial revenue from advertising businesses dependent on human attention and browsing patterns. According to Imas and Fradkin, AI agents threaten this model because they can evaluate thousands of options across multiple platforms without susceptibility to position bias or sponsored placements optimized for human clickthrough behavior.

Eric Seufert, an analyst writing about digital advertising markets, characterized the fundamental flaw with agentic commerce as violating platform motivations to control customer relationships and monetize first-party data through advertising. Retail platforms generate revenue by presenting specific products in particular positions, leveraging decades of optimization based on human browsing behavior.

Google expanded AI travel planning capabilities on November 17, 2025, introducing Canvas-based itinerary creation and agentic booking for restaurants, events, and flights. The system combines real-time Search data, Google Maps information, and web content to generate travel suggestions. According to the announcement, the integration demonstrates Level 1 Connected Problem-Solver functionality in Google Cloud's five-level taxonomy for autonomous systems.

Traditional travel advertising through Google Ads continues operating separately from consumer-facing AI features. Travel Feeds in Search Ads expanded to all hotel advertisers globally in October 2024, enabling comprehensive hotel data display including real-time prices and availability within search advertisements.

The travel industry has criticized Google for self-preferencing practices under the Digital Markets Act. According to eu travel tech, Google's hotel usage surged from 10 percent to 80 percent between 2013 and 2023. Google Flights expanded market share from 11.8 percent to 22.2 percent in Germany, from 19 percent to 33.6 percent in the Netherlands, and from 14.4 percent to 23.4 percent in Spain between 2020 and 2023.

Infrastructure proposals meet platform incentives

Multiple companies have developed infrastructure for machine-readable web interactions. Parallel Web Systems offers services converting regular websites into AI-native interfaces. A coalition developed the Agentic Commerce Protocol as an open standard enabling AI agents to interact with retailers for shopping purposes.

Six companies launched Ad Context Protocol on October 15, 2025, betting that an open-source technical standard could enable AI agents to communicate across platforms and execute advertising tasks autonomously. The protocol emerged from Scope3, Yahoo, PubMatic, Swivel, Triton, and Optable. According to the companies involved, AdCP provides a unified interface allowing AI agents to discover inventory, compare pricing, and activate campaigns across different advertising platforms.

Industry observers remain skeptical about whether platforms will cooperate. Augustine Fou, a fraud researcher and marketing consultant, cautioned that agentic AI does not eliminate bad actors in supply chains. According to Fou, more automation means less transparency, and agents can still act on behalf of people with bad incentives.

Advertising platforms have spent decades optimizing human-facing interfaces for profitability. A machine-readable layer threatens to bypass sponsored placements, featured results, and ranking algorithms calibrated using human clickthrough data. According to Imas and Fradkin, platforms have strong incentives to delay infrastructure that lets independent agents bypass advertising-based revenue models.

Regulatory framework proposals

The analysis proposes a legal framework establishing user rights to deploy AI agents on any platform they can access as humans, provided agents operate through user credentials, act only at user direction, identify themselves as AI agents, and avoid data harvesting beyond transaction requirements.

Technology for implementing such standards already exists. Personhood credentials using cryptographic protocols can identify agents as belonging to specific users. Platforms could set reasonable security requirements for agent identification while not categorically banning agents or reserving agentic capabilities exclusively for platform-controlled tools.

The proposal draws parallels to established consumer rights. Humans can hire personal shoppers, use browser extensions applying coupons, and visit multiple websites before purchasing. According to Imas and Fradkin, the principle that consumers can seek assistance navigating markets is well-established. The question centers on why AI assistance should receive different treatment than human assistance when agents act on explicit instructions using user credentials for user benefit.

Legitimate platform concerns exist. AI agents can be tricked through prompt injection attacks, phishing schemes, and adversarial manipulation. An agent autonomously entering payment information could be exploited in ways humans would detect. According to the analysis, the appropriate response involves security standards for agents rather than outright prohibition, with platforms potentially certifying specific agents as safe for various use cases.

Enforcement challenges complicate implementation. Distinguishing legitimate user agents from data scrapers, fake order bots, or competitive surveillance tools requires sophisticated detection systems. Platforms have legitimate interests preventing abuse, and agent identification represents one mechanism for doing so.

OpenAI revised ChatGPT crawler documentation with significant policy changes in December 2025. The modifications separated training data collection from user-initiated browsing activity, but ChatGPT-User traffic cannot be controlled through robots.txt files according to the updated documentation. Website operators seeking to control access must implement more sophisticated solutions than traditional blocking mechanisms.

Cloudflare introduced pay-per-crawl services on July 1, 2025, allowing content creators to charge AI crawlers for access through HTTP 402 Payment Required responses. Over 80 media executives rallied against unauthorized AI scraping at an IAB Tech Lab summit in July 2025, confronting AI companies that scrape publisher content without consent or compensation.

Adoption patterns across industries

Marketing organizations have implemented agentic AI capabilities for specialized workflows. LiveRamp introduced agentic orchestration on October 1, 2025, enabling autonomous AI agents to access identity resolution, segmentation, and measurement tools. The system allows agents to plan workflows based on natural language descriptions of marketing objectives.

Google Cloud survey results from April 18, 2025, showed 88 percent of early adopter organizations implementing AI agents reported positive return on investment across multiple business applications. Content creation speeds increased by 46 percent and content editing efficiency improved by 32 percent through AI agent implementation.

Implementation challenges persist despite growing investment. Gartner predicted on June 25, 2025, that over 40 percent of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, and inadequate risk controls. The research firm's January 2025 poll of 3,412 webinar attendees revealed uneven investment patterns, with 19 percent reporting significant investments while 42 percent made conservative investments.

IAB Tech Lab CEO warned the industry about chasing "shiny pennies" in agentic AI without addressing fundamental challenges. According to Anthony Katsur, protocols don't solve misaligned incentives and bad actors. The advertising industry needs to tackle supply chain transparency and privacy before jumping to new phases of agentic development.

Anthropic opened Claude Code's automation power through Cowork in January 2026, enabling task automation through web browser control. OpenAI released Operator in January 2025 with similar capabilities. Amazon introduced Nova Act in March 2025, competing directly with Anthropic and OpenAI's offerings.

The flight booking demonstration by paquino11 used Python 3.9 with a Google API Key for Gemini 2.0 Flash access. The system required the langchain_google_genai library for AI model integration and browser-use framework for automation. According to the GitHub documentation, users could optionally specify custom Chrome installation paths for browser control.

Timeline

Summary

Who: Researchers Alex Imas and Andrey Fradkin analyzed obstacles facing AI agents attempting consumer tasks like flight bookings, while developer paquino11 demonstrated practical implementation using Browser Use framework with Google's Gemini 2.0 Flash model. The analysis affects digital platforms including Google Flights, Booking.com, airlines, travel agencies, and marketing technology providers implementing agentic AI systems.

What: AI agents possess technical capabilities to navigate websites and complete booking workflows but face legal ambiguities around access authorization and platform restrictions through terms of service. The Computer Fraud and Abuse Act precedent allows platforms to prohibit agent access after explicit revocation, creating legal risk for users deploying autonomous shopping tools. Browser-based automation frameworks combine language models with web interaction capabilities but encounter obstacles from interfaces optimized exclusively for human spatial reasoning and visual processing.

When: The analysis was published January 16, 2026, following a year of significant agentic AI development including Amazon's Ads Agent launch on November 11, 2025, Google's travel planning expansion on November 17, 2025, and multiple advertising automation protocols throughout fall 2025. Investment in agentic AI reached $1.1 billion in 2024 with job posting increases of 985 percent from 2023 to 2024 according to McKinsey data.

Where: Legal obstacles exist primarily in United States jurisdictions where Computer Fraud and Abuse Act precedent applies, while European markets face additional complexities from Digital Services Act requirements and GDPR compliance frameworks. Technical implementation occurs across web interfaces including travel booking platforms, e-commerce sites, and service portals where human-optimized designs create barriers for autonomous agents operating through standard browsers.

Why: Platforms derive revenue from advertising models dependent on human browsing patterns and attention allocation, creating business incentives to restrict AI agents that bypass sponsored placements and position-based optimization. Legal frameworks lack clarity on whether AI agents inherit user access rights, enabling platforms to prohibit autonomous tools through terms of service while developing proprietary "bowling-shoe agents" that serve platform interests rather than user objectives. The gap between technical capabilities and practical deployment reflects unresolved tensions between consumer automation rights and platform control over user experiences and monetization strategies.

Share this article
The link has been copied!