Team of AI Agents Brings Live City Data to Tourism Guidance in Lisbon

Ask a chatbot for a rainy-day plan in Lisbon and you will likely receive a beautifully written itinerary that falls apart the moment you step outside. The metro schedule is wrong, the museum closed early, and the forecast was never checked. This gap between fluent language and verified fact is one of the most persistent problems in applied artificial intelligence, and it is especially acute in cities, where the information a traveler needs — weather, transport status, opening hours, events — changes by the hour and lives in a dozen different systems. A new study published in Results in Engineering tackles the problem head-on with a system called LISBOA, a multi-agent AI assistant that grounds every answer in live, official data from Portugal’s capital.

The stakes are considerable. Tourism generated nearly €20.3 billion for Portugal in the first eight months of 2025, a 5.4 percent increase over the previous year, and the Lisbon Metropolitan Area contributes roughly 36 percent of national GDP. The city’s resident population of about 548,000 swells by more than 70 percent each day with commuters, producing a functional population of around 926,000 people who must navigate a multi-modal network of metro, buses, trams, trains, and ferries that carries about 22 percent of trips within the city. Both visitors and residents constantly make decisions that depend on shifting conditions — where to go, what is open, how to move — yet the data they need is scattered across providers with different formats, languages, update cycles, and access methods.

Large language models, for all their conversational skill, are poorly equipped to handle this volatility on their own. Generation in these models is optimized for plausibility rather than verified correctness, so answers can be confidently wrong. Benchmarks such as TravelPlanner have reported that leading LLM agents produce feasible travel plans in fewer than 1 percent of cases under realistic constraints, and prior Lisbon-focused systems like the LISA chatbot, while valuable, relied mainly on knowledge bases prepared in advance rather than coordinating dynamic data streams at query time. The authors of the new study — André Silvestre, Bruno Jardim, and Miguel de Castro Neto — set out to answer a precise question: how can agentic AI support context-aware tourism and mobility guidance by integrating live urban data sources?

Their answer is an architecture built on a supervisor-worker pattern, implemented with the LangChain and LangGraph frameworks as a stateful directed graph of processing nodes. A central supervisor receives every query and classifies it using a four-tier heuristic policy: greetings are answered directly, out-of-scope requests about places outside the metropolitan area are politely declined, single-domain queries are matched by fuzzy keyword matching with a similarity threshold of 0.86, and follow-up questions reuse the previous routing decision. Only when all four heuristics fail does the supervisor invoke an LLM classifier. This heuristic-first design reduces per-query cost, latency, and routing instability. Specialized worker agents then do the grounding: a Weather agent with four tools queries the national meteorological service IPMA, a Transport agent wields 30 tools across metro, bus, and train operators, and a Researcher agent performs semantic search over curated tourism collections and municipal open data, with a constrained web fallback for cultural queries outside the local corpus.

The grounding layer is the technical heart of the system. It integrates nine Lisbon data sources, from weekly-scraped tourist place listings and daily cultural events to live REST APIs for the metro, suburban buses, and weather, plus GTFS and GTFS-Realtime feeds for urban buses, trams, and trains. Because tourism content is largely in English while municipal and transport data is in Portuguese, the system uses BGE-M3, a multilingual embedding model producing 1024-dimensional vectors that combine dense, sparse, and multi-vector retrieval. Embeddings are stored in a vector database with separate collections for places, events, and guide documents, so that fast-changing event data cannot contaminate long-lived place information. Municipal GeoJSON datasets are not pre-indexed; instead, a metadata catalog triggers on-demand fetching, and results are filtered by great-circle distance using the Haversine formula. In total, 45 tools give the agents structured access to evidence that no single retrieval mechanism could reach.

For itinerary requests, a Planner agent operates strictly on retrieved evidence. It converts the request into a structured brief recording the start point, visit areas, time window, stop types, and transport mode, then drafts an ordered plan that favors area-based sequencing. An automatic check compares the draft against the brief and the evidence — flagging stops that match no retrieved item, stops outside the requested area, or stops scheduled outside their opening hours — and returns issues to the model once for repair. Unconfirmed details such as prices or exact transport legs are stated as limitations rather than invented. Before delivery, a Quality Assurance agent applies both LLM review and deterministic checks for valid station names, metropolitan-area bounds, forecast horizons, and plausible temperatures, and can trigger one bounded repair cycle. The design philosophy is deliberate: controllable grounding and predictable behavior over open-ended autonomy.

The evaluation is as notable as the architecture. The team built a curated corpus of 82 queries spanning weather, transport, tourism research, multi-agent itineraries, greetings, and out-of-scope cases, including 16 boundary queries whose only correct answer is an explicit limitation. Each grounded worker agent was benchmarked in isolation across two foundation-model profiles — proprietary GPT-5.4-mini and open-weight Kimi-K2.5 — with responses scored by two independent LLM judges on five dimensions from factual accuracy to response quality. Across 124 scored responses, the workers achieved a mean composite quality score of 4.575 out of 5, with a mean tool F1 of 0.906 and no recorded execution errors. Weather scored highest at 4.750, while transport, spanning the most tools and providers, scored 4.486. The two model profiles performed nearly identically, suggesting the orchestration, not the choice of model, drives performance.

The most striking evidence came from a paired ablation study comparing the full pipeline against the same models answering from parametric knowledge alone. The quality score rose from 3.580 to 4.423 for the proprietary profile and from 3.595 to 4.460 for the open-weight profile — gains of roughly 0.84 and 0.87 points, both statistically significant at p < 0.001 with large effect sizes. The full pipeline beat the zero-shot baseline on 63 of 75 queries. The largest improvement appeared in weather, up more than a full point for both profiles, which makes sense: no amount of training data can recover today’s forecast. Transport and multi-agent itinerary queries also improved significantly, and the biggest per-dimension gains were in completeness and factual accuracy — precisely the qualities generic chatbots lack. Grounding, however, carries a cost: full-pipeline responses took a median of 5.79 seconds with the proprietary model versus about 2 seconds zero-shot, and cost roughly nine to twelve times more per answer.

A formative user study with 20 participants reinforced the quantitative picture. All six evaluation dimensions — usefulness, trust, response quality, personalization, interface, and satisfaction — received median ratings of 4.0 or higher on a 5-point scale, and 15 of 20 participants said they would recommend the system, with none declining. Participants particularly valued the consolidated, source-cited transport and weather guidance. Intriguingly, some complaints reflected correct behavior: when queries exceeded the five-day forecast horizon or concerned places outside the covered area, the system refused to fabricate answers, which several users experienced as a shortcoming rather than a safeguard. The most consistent genuine failures involved resolving lesser-known places, occasional intent misreadings, and assembling itineraries with more than three stops across a full day.

The authors are candid about the boundaries of their work. The query corpus was curated rather than independently annotated, the multi-agent subset contained only 13 queries, the ablation varies grounding and workflow together so it cannot isolate the contribution of orchestration itself, and the LLM judges were not calibrated against human ratings. Itinerary feasibility — actual travel times, transfers, and opening hours — was not directly verified, and the system does not replan when conditions change after delivery. Yet the core lesson stands with unusual clarity: when the model is held constant, connecting generation to current, authoritative, city-specific evidence is what transforms a plausible-sounding assistant into a useful one. For cities weighing how to deploy AI for residents and visitors, LISBOA offers a transferable blueprint — not broader autonomy, but better grounding, clearer scope, and honest communication about what the data can and cannot yet say.

Subject of Research: A multi-agent AI architecture for context-aware tourism and urban mobility guidance in Lisbon

Article Title: A multi-agent approach for context-aware tourism and urban mobility guidance

Article References: Silvestre, A., Jardim, B., & de Castro Neto, M. (2026). A multi-agent approach for context-aware tourism and urban mobility guidance. Results in Engineering, 32, Article 113369. https://doi.org/10.1016/j.rineng.2026.113369

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113369

Keywords: multi-agent systems, large language models, retrieval-augmented generation, urban mobility, smart tourism, Lisbon, grounding, itinerary planning, open data, LLM-as-judge evaluation, real-time transport data, conversational AI