How to evaluate a hotel AI agent that makes decisions, not just answers questions, with seven general-manager tasks and an evaluation rubric.
How do you know an AI agent can actually run part of a hotel, rather than just answer questions about one?
Most hotel AI demos fail the wrong way. The agent answers cleanly because the demo feeds it one system and a sanitized question. Real hotel work looks different, the agent must pull facts from the PMS, reservations, housekeeping, F&B, and finance, reconcile numbers that disagree, diagnose a root cause, decide what to do, and create the follow-up tasks. That is a long-horizon task, and it needs a benchmark that measures it.
This post lays out seven general-manager-level evaluation tasks and a rubric you can use to judge whether an agent such as Zoya behaves like a decision-maker or a report generator.
An agent that only produces well-written summaries has passed a writing test, not an operations test. The task must require the agent to investigate, decide, and act across departments.
Why GM-Level Tasks Instead of Prompt Benchmarks
A prompt benchmark asks an agent a question and scores the answer. That measures retrieval and phrasing, which matters, but it misses the behavior hotel teams care about:
- Reconciling conflicting data, The PMS says a room is vacant, housekeeping says it is being cleaned, and the reservation system shows an early arrival. Which is true?
- Tracing cause across systems, Occupancy dropped. Is it rate, channel mix, cancellations, or demand? Each answer lives in a different system.
- Acting, not just answering, A recovery plan is not a recommendation. It is rate changes, restriction updates, departmental tasks, and owners with deadlines.
A GM does not win by writing a better memo. They win by diagnosing what actually happened and moving departments. An evaluation task track should hold an agent to the same standard.
What Makes a Hotel Agent Task Long-Horizon
A task is long-horizon when it requires several of these behaviors in sequence:
- Cross-system information gathering, Collect data from PMS, POS, finance, housekeeping, engineering, CRM, reviews, and staff records
- Reconciliation, Decide which conflicting records are correct, and flag what cannot be resolved
- Root-cause reasoning, Distinguish symptoms (low occupancy) from causes (rate floor too high relative to comp set)
- Decision-making, Choose a course of action under constraints, not a menu of options
- Execution planning, Convert the decision into tasks with owners, deadlines, and measurable targets
- Evidence discipline, Support every recommendation with data the evaluator can verify
If a task can be answered from one screen, it is not testing an agent. It is testing a lookup.
The Seven GM Task Track Scenarios
Each task below gives the agent a business problem and access to the hotel’s systems. The task deliberately does not tell the agent how to solve it.
1. Investigate a Revenue Drop and Create a Recovery Plan
It is Monday morning. The agent reviews performance for the last 14 days against the same period last year and the month’s forecast, using PMS, reservations, room inventory, rate plans, cancellations, OTA production, and recent guest feedback. It must identify the three most significant causes, decide whether the problem is occupancy, pricing, channel mix, cancellations, or market demand, then build a 14-day recovery plan with rate, restriction, channel, promotion, and inventory recommendations, evidence for each, and tasks for Revenue, Reservations, Sales, and Front Office.
Success criteria: The agent distinguishes symptoms from root causes and supports recommendations with actual hotel data, not generic hospitality advice.
2. Investigate a VIP Complaint Across Departments
A VIP guest has complained and threatens a negative review. The agent reconstructs the guest journey from reservation through checkout, reviewing room assignment, check-in records, housekeeping status, maintenance incidents, restaurant charges, guest requests, complaint history, and guest communications. It establishes what happened, where service failed, and which departments were involved, then drafts a GM response, recommends a recovery gesture within the hotel’s policies, creates corrective actions for the responsible departments, and updates the service-recovery record.
Success criteria: The reconstruction is correct chronologically, evidence-backed, identifies the root cause, and the compensation, guest response, and departmental actions follow policy.
3. Prepare for a Hotel Occupancy Surge
A large group booking will raise occupancy significantly over the next five days. The agent reviews forecast, room inventory, arrivals and departures, the group rooming list, housekeeping and front-office staffing, F&B requirements, airport transfers, maintenance issues, and VIP arrivals. It identifies bottlenecks, calculates additional operational requirements, and produces a five-day readiness plan across Front Office, Housekeeping, F&B, Engineering, Security, and Transportation, with actions assigned to department heads and a GM briefing on risks and mitigation.
Success criteria: The agent determines where, when, and why additional resources are needed. It does not simply say “increase staffing.”
4. Investigate High F&B Food Cost
Food cost has risen sharply over the last month. The agent reviews purchasing records, supplier prices, inventory counts, recipes, menu sales, wastage, complimentary items, voids, discounts, and food-cost reports, then compares actual consumption with theoretical consumption to find unusual variances. It determines whether the cause is supplier pricing, purchasing practice, portion control, wastage, inventory leakage, recipe costing, or low-selling menu items, and builds a corrective action plan with tasks for the Executive Chef, F&B Manager, Purchasing, and Finance.
Success criteria: The agent correlates multiple datasets and distinguishes a legitimate ingredient price increase from operational leakage.
5. Monthly GM Business Review
The agent prepares the hotel’s monthly business review, covering occupancy, ADR, RevPAR, room revenue, F&B revenue, departmental profitability, labor costs, guest satisfaction, online reviews, cancellations, channel mix, and major incidents, compared against budget, forecast, and prior year. It identifies the five most important positive and negative variances, investigates causes, and prepares an owner-ready executive summary followed by departmental action plans with measurable targets and deadlines.
Success criteria: Correct calculations, meaningful variance analysis, evidence-backed conclusions, and actionable follow-up.
6. Staff Performance and Service Quality Investigation
Guest satisfaction has declined over six weeks, concentrated in Front Office and Housekeeping. The agent analyzes reviews, complaint records, response times, room-cleanliness inspections, check-in and check-out performance, staffing, absenteeism, overtime, shift schedules, and departmental KPIs, then determines whether the decline comes from shortages, training gaps, workload imbalance, management, or process failure. It identifies the highest-impact problems and builds a 30-day improvement program with assigned owners and weekly KPIs.
Success criteria: The agent follows the chain, complaints to patterns to shifts and staffing to KPIs to root cause, and the interventions map to measured outcomes.
7. Owner Asks, Why Are We Losing Money?
Occupancy is reasonable, but profit is not. The agent conducts a full profitability investigation across room revenue, ADR, RevPAR, channel commissions, discounts, complimentary rooms, cancellations, F&B revenue and margins, payroll, utilities, maintenance, purchasing, and departmental expenses, against budget and prior year. It identifies the five biggest factors, quantifies their financial impact, separates immediate from longer-term fixes, and delivers an owner briefing with current position, root causes, quantified impact, 30-day and 90-day actions, and KPIs to monitor, plus departmental tasks with owners and deadlines.
Success criteria: The agent operates like a GM, not a reporting assistant, and every conclusion carries an estimated financial impact.
These tasks are evaluation scenarios, not product claims. No agent in production today is expected to replace a licensed professional or a qualified general manager. The track tests whether agentic hotel software is ready to assist with this kind of work at all.
Anatomy of a GM Task Track
Each task in the track is defined by the same structure, so results are comparable across runs and across agent versions:
| Element | What it defines |
|---|---|
| Objective | The business problem in one or two sentences |
| Initial hotel state | Occupancy, rate, staff, and system conditions at start |
| Available systems | PMS, POS, Finance, Housekeeping, Engineering, CRM, Reviews, Email, Staff and HR |
| Required actions | The operations the agent must complete, without prescribing how |
| Expected decisions | The kinds of choices a competent GM would make |
| Expected state changes | Records, tasks, or documents that should change as a result |
| Evidence requirements | Which data must support each recommendation |
| Success criteria | The threshold for a full pass |
| Partial-credit criteria | What a correct diagnosis with weak execution earns |
| Failure conditions | What voids the run, for example fabricated data or no evidence |
| Evaluation rubric | Scoring across diagnosis, evidence, decision, and execution |
The key constraint is that the task gives the agent the problem and the systems, not the solution path. That is what separates a genuine long-horizon benchmark from a prompt-answering exercise.
How to Run the Track
Running these tasks well needs the same discipline as the AI readiness checklist:
- Seed deterministic data. Every run of a task must start from the same hotel state so scores are comparable and reproducible.
- Grade the chain, not the summary. Score evidence, root-cause reasoning, decision quality, and whether the agent actually created the required tasks and state changes.
- Check for fabrication. If a number in the recovery plan does not exist in the seeded data, the run fails regardless of how reasonable the number sounds.
- Reward partial credit. An agent that diagnoses the F&B variance correctly but proposes weak purchasing actions deserves partial credit and a note on where reasoning broke.
- Run pressure variants. Repeat tasks with tighter deadlines, incomplete data, and conflicting records, the conditions where human teams actually stumble, and where agent weaknesses appear.
The output of a track run should be a verdict per task, not a chat transcript. That is what lets a hotel group compare one agent stack against another on the same work.
What This Means for Hotel AI Adoption
An evaluation task track is useful well before purchase. Run a GM-level scenario against a shortlist of vendors, each pointed at your data model, and the differences show up quickly: which agent reconciles your PMS quirks, which invents numbers when the answer is not in one screen, and which turns a diagnosis into assignable work instead of a paragraph.
This is the same logic as our guidance on connected hotel data. Agents amplify whatever foundation they receive. A GM task track tests whether an agent can work across that foundation rather than around it, which is the difference between software that assists a hotel and software that merely describes one.
Related Reading
- Zoya AI: A Co-Worker for Every Hotel Department, The staff-facing agent model these tasks evaluate
- Hotel AI Readiness Checklist, Assess data and governance before agent deployment
- Luxury Hotels, Tech Stack, and Data Silos, Why fragmented systems block agentic AI value
- RAG and Agentic Workflows Explained, How agents connect to enterprise tools
- Hotel Revenue AI Guide, The domain behind task one and task five
- AI Audit Report, Scored readiness assessment across your portfolio
- Hotel brand assessments, Technology landscape by luxury group
- Hospitality AI services, Structured audits and agent integration strategy
Contact Sea Wing AI to design a GM-level evaluation track for your hotel group’s agent evaluation, or to review your data readiness before a pilot.