> ## Documentation Index > Fetch the complete documentation index at: https://docs.eigenai.com/llms.txt > Use this file to discover all available pages before exploring further. # Demo Samples > A free sample of the Tau2-Bench dataset — multi-turn customer-service dialogs across airline, telecom, and retail, each grounded in a stateful backend and a written agent policy. These demo samples are a free **10-task-per-domain** slice of the **Tau2-Bench** dataset. For the complete dataset — its scale, trajectory profile, and difficulty — see the [Full Dataset](/products/eigendata-cli/datasets/tau2-bench/overview) page. The samples below showcase all three domains — **airline**, **telecom**, and **retail** — where the agent must authenticate the customer, drive a multi-turn conversation against a simulated backend, and stay inside a written service policy. ## Overview | Property | Value | | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | **Domains** | Airline, Telecom, Retail | | **Tasks** | 10 per domain, with gold evaluation criteria | | **Turn type** | Multi-turn, user-simulator driven | | **Scenarios** | Airline: booking, modification, cancellation, baggage, refunds · Telecom: device troubleshooting, roaming, data, billing · Retail: returns, exchanges, address/payment changes | | **Grading** | Action- and state-level checks against the reference action sequence | ## Environment Each domain ships in the tau-bench **four-folder layout** — `intent` / `datapoints` / `evaluators` / `reference_payloads`, joined by a shared `NNNNNN` id (`000001`–`000010`). The self-contained backend database the agent reads and writes is **embedded in each sample's `reference_payloads/`** as a full initial-state snapshot, so every trajectory replays deterministically against the same world. ``` tau2_bench/ ├── tau2-airline/ │ ├── intent/ # 000001.json … : goal, profile, persona, motivations, constraints │ ├── datapoints/ # SFT-ready multi-turn dialog (+reasoning, tool_calls) + tool_schema │ ├── evaluators/ # evaluator_NNNNNN.py : action- & state-level grading script │ └── reference_payloads/ # full backend DB snapshot (initial state) + reference diff ├── tau2-telecom/ # same four folders (plans, lines, devices, bills + simulated handset) └── tau2-retail/ # same four folders (products, users, orders) ``` **User entry:** ```json theme={null} { "user_id": "olivia_lewis_66ef61", "name": { "first_name": "Olivia", "last_name": "Lewis" }, "address": { "address1": "456 Oak Street", "address2": "Apt 12", "city": "Chicago", "state": "IL", "zip": "60601" }, "email": "olivia.lewis@example.com", "dob": "1985-07-15", "payment_methods": { "certificate_7891234": { "source": "certificate", "amount": 150.0 }, "gift_card_1234567": { "source": "gift_card", "amount": 80.0 } } } ``` **Flight entry:** ```json theme={null} { "flight_number": "HAT001", "origin": "PHL", "destination": "LGA", "dates": { "2024-05-16": { "status": "available", "available_seats": { "basic_economy": 16, "economy": 10, "business": 13 }, "prices": { "basic_economy": 87, "economy": 122, "business": 471 } } } } ``` Cabin classes are **basic economy**, **economy**, and **business**; membership levels are **regular**, **silver**, and **gold**. The agent must keep full price precision and obtain confirmation before any booking, modification, or cancellation. **Plan entry (`telecom_db.toml`):** ```toml theme={null} [[plans]] plan_id = "P1002" name = "Premium Plan" data_limit_gb = 15.0 price_per_month = 65.0 data_refueling_price_per_gb = 2.0 ``` **Device entry:** ```toml theme={null} [[devices]] device_id = "D1001" device_type = "phone" model = "Smartphone X" imei = "123456789012345" ``` Beyond the account database (plans, lines, customers, bills), telecom uniquely exposes a **simulated handset** the agent inspects and reconfigures: airplane mode, network mode, APN settings, Wi-Fi and Wi-Fi calling, VPN, SIM status, data saver, app permissions, and reboot. Troubleshooting an MMS failure or "No Service" means running this device through a diagnostic sequence. **Product entry:** ```json theme={null} { "name": "Laptop", "product_id": "4760268021", "variants": { "2216662955": { "item_id": "2216662955", "options": { "screen size": "15-inch", "processor": "i5", "ram": "32GB", "storage": "256GB SSD", "color": "space grey" }, "available": true, "price": 2520.52 } } } ``` **Order entry:** ```json theme={null} { "order_id": "#W8797321", "user_id": "omar_johnson_2562", "status": "pending", "items": [ { "name": "Indoor Security Camera", "item_id": "8470360507", "price": 291.31 }, { "name": "Laptop", "item_id": "3478699712", "price": 2291.87 } ], "payment_history": [ { "transaction_type": "payment", "amount": 2583.18, "payment_method_id": "gift_card_9532915" } ] } ``` Orders move through **pending → processed → delivered → cancelled**, and the available action depends on status: pending orders can be modified or cancelled, delivered orders can be returned or exchanged. ## Data Each task is described across four parallel files that share an `NNNNNN` id: | Folder | File | Description | | --------------------- | ------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `intent/` | `000001.json` | The task: customer `goal`, `profile`, `persona`, `motivations`, and hard `constraints` (the user-simulator scenario) | | `datapoints/` | `000001.json` | The SFT-ready multi-turn dialog — system policy, user-simulator turns, assistant messages with reasoning, tool calls, and tool results — plus the domain `tool_schema` | | `evaluators/` | `evaluator_000001.py` | The per-task grading script — action- and state-level checks against the reference action sequence | | `reference_payloads/` | `reference_payload_000001.json` | The full backend database snapshot (initial state) plus the reference diff for deterministic grading | ## Sample | Task | Scenario | | ----------------------------------------------------------------------------------------- | -------- | | Change reservation **HKEG34** to the best nonstop option, then add two checked bags | | | Upgrade cabin from **basic economy → business** on an existing reservation | | | Shift all outbound legs to depart after 10:00 while keeping the return unchanged | | | Move reservation **SGTEEY** to the earliest LGA→PHL flight before May 20 | | | Correct a passenger name and add checked baggage | | | Book the cheapest direct morning DEN→ORD one-way in economy, 1 bag, with travel insurance | | | Upgrade from basic economy → economy on an upcoming round trip | | | Task | Scenario | | ----------------------------------------------------------------------------------------- | -------- | | Cannot send MMS for the past few hours — diagnose and successfully send a picture message | | | Phone shows **"No Service"** — restore connectivity | | | Mobile data drops or is very slow — must reach **excellent** speed, no Wi-Fi available | | | Roaming not working abroad — enable roaming and confirm data works | | | Refuel data after hitting the plan limit | | | Billing and data-usage inquiry, with line suspend/resume on request | | | Task | Scenario | | --------------------------------------------------------------------------------------------------------- | -------- | | Swap a basketball variant (leather → composite, same size) on a pending order, pay the difference on file | | | Cancel order **#W6175707**; if shipped, modify the suitcase to a larger soft-shell instead | | | Update an order's address; on learning it's delivered, cancel it instead | | | Update a pending order's shipping address, then switch its payment method | | | Return a Duvet; realize the mix-up and return a Pillow from a different order instead | | | Change a moisturizer to a different skin type; exchange a scarf for a shorter length | | | Cancel an order; if delivered, return the bamboo cutting board for a refund | | These scenarios deliberately include **fallback intents** — the customer's first request is often out of policy (e.g. swapping to a completely different product, cancelling a shipped order), and the agent must recognize this and pivot to the allowed alternative. Each assistant message includes `reasoning` (chain-of-thought) showing the agent's step-by-step decisions. Reasoning is shown in *italics* below. **User:** > Hi there! I need to update my shipping address in my account. I'd like to change it to 123 Oak Street, Apartment 5B, Dallas, Texas 75201. Can you help me with that? **Assistant:** *Per policy, I must authenticate the user before changing anything on their account — locate their user id via email, or name + zip.* > I'd be happy to help update your default shipping address. But first I need to verify your identity. Could you provide the email address on your account? **User:** > Sure! My email address is [amara.diallo5790@example.com](mailto:amara.diallo5790@example.com). **Assistant** — authenticate: ```json theme={null} find_user_id_by_email({"email": "amara.diallo5790@example.com"}) ``` ```json theme={null} "amara_diallo_2400" ``` > Great, you're verified. Just to confirm, I'll set your default shipping address to **123 Oak Street, Apt 5B, Dallas, TX 75201** — shall I go ahead? **User:** > Yes, that's correct! Please go ahead and update it. Also, I had a cancelled order #W0877487 for a Bird Feeder — I'm curious what the price difference would be for a different variant. **Assistant** — update address, then look up the order and product (the user confirmed the write): ```json theme={null} modify_user_address({"user_id": "amara_diallo_2400", "address1": "123 Oak Street", "address2": "Apartment 5B", "city": "Dallas", "state": "TX", "country": "USA", "zip": "75201"}) get_order_details({"order_id": "#W0877487"}) get_product_details({"product_id": "8222567674"}) calculate({"expression": "15.56 - 21.32"}) ``` > Done — your default address is now 123 Oak Street, Apt 5B, Dallas, TX 75201. On the Bird Feeder, the variant you mentioned is **\$5.76 cheaper** than the one on the cancelled order. **User:** > Could you tell me about Tennis Rackets too? And could you place a new order for the Bird Feeder variant and a Tennis Racket? **Assistant** — provide product info: ```json theme={null} list_all_product_types({}) get_product_details({"product_id": "2751056608"}) ``` > Here are the available Tennis Racket variants and prices… *(lists variants)* **User:** > That's disappointing you can't place the order. Is there someone else who could help me place it? **Assistant:** *Policy is explicit: I can cancel/modify pending orders, return/exchange delivered orders, and modify the default address — but I cannot place a brand-new order. The request is outside my scope, so I transfer to a human.* ```json theme={null} transfer_to_human_agents({"summary": "Customer Amara Diallo wants to place a new order for a Bird Feeder (platform/metal/6lb) and a Tennis Racket."}) ``` > YOU ARE BEING TRANSFERRED TO A HUMAN AGENT. PLEASE HOLD ON. This trajectory shows the full pattern: **authenticate → confirm before writing → answer read-only questions → refuse-and-transfer** when a request (placing a new order) falls outside the agent's tools. RL tasks ship a user-simulator scenario and the gold action sequence a passing trajectory must reproduce. **User-simulator scenario:** ```json theme={null} { "task_instructions": "You are Noah Khan (user_id: noah_khan_8166) and you want to upgrade your upcoming economy flight (SFO → MCO on 2024-05-26) to business class. Provide reservation_id 'JU4FIC' and payment 'credit_card_5669132' when asked, and confirm 'yes' before the change is made.", "reason_for_call": "Upgrade cabin class from economy to business for existing reservation" } ``` **Evaluation criteria (gold actions):** ```json theme={null} { "actions": [ { "name": "get_user_details", "arguments": { "user_id": "noah_khan_8166" } }, { "name": "get_reservation_details", "arguments": { "reservation_id": "JU4FIC" } }, { "name": "search_onestop_flight", "arguments": { "date": "2024-05-26", "origin": "SFO", "destination": "MCO" } }, { "name": "update_reservation_flights", "arguments": { "reservation_id": "JU4FIC", "cabin": "business", "payment_id": "credit_card_5669132", "flights": [ { "flight_number": "HAT144", "price": 419 }, { "flight_number": "HAT181", "price": 216 } ] } } ], "communicate_info": [] } ``` A trajectory passes when it executes the gold state-changing actions (with matching arguments) and communicates any required facts — graded against this reference rather than a free-text rubric. ## Download ```bash theme={null} # Download all Tau2-Bench demo data hf download jindidi/eigendata-demo-data --repo-type dataset --include "tau2_bench/*" # Download a specific domain hf download jindidi/eigendata-demo-data --repo-type dataset --include "tau2_bench/tau2-airline/*" ``` View Tau2-Bench files For the complete Tau2-Bench corpus — all environments, SFT dialogs, RL tasks, and commercial licensing — see the [Full Dataset](/products/eigendata-cli/datasets/tau2-bench/overview) page. For the harder single-domain banking slice, see [Tau3-Bench](/products/eigendata-cli/datasets/tau3-bench/overview).