| --- |
| license: apache-2.0 |
| task_categories: |
| - text-generation |
| tags: |
| - agent |
| - tool-use |
| - function-calling |
| - benchmark |
| - evaluation |
| - agentic |
| language: |
| - en |
| pretty_name: AgentDeliveryBench |
| --- |
| |
| # AgentDeliveryBench — does your tool-using agent actually *deliver the answer*? |
|
|
| A small, fast, deterministic benchmark for **multi-turn tool-using agents** that measures the two |
| things state-based benchmarks miss: |
|
|
| 1. **Answer delivery** — after using tools, does the agent give a final natural-language answer that |
| actually *states the correct result*? |
| 2. **Termination** — does it finish within a turn budget *without looping* (no repeated/identical |
| tool calls, no running to max-turns)? |
|
|
| ## Why this exists |
|
|
| Popular function-calling benchmarks (e.g. BFCL `multi_turn`) score the **final backend STATE** — did |
| the right files/DB rows end up correct. We found this is *misleading for real agents*: a model can |
| **pass the benchmark while being useless** — it makes the right tool calls, leaves the correct state, |
| but then **loops** or ends with *"Done — I've completed that"* and **never tells the user the |
| answer**. We watched fine-tunes that beat the state benchmark score **0/15** on real agent tasks |
| (loop, never answer), while smaller untuned models that *delivered answers* scored far higher. |
|
|
| **AgentDeliveryBench scores what a user actually experiences:** a correct, stated answer, delivered |
| without flailing. |
|
|
| ## What's in it |
|
|
| - **`agent_tasks.json`** — 30 verifiable tasks across 5 difficulty tiers (easy → expert), each with a |
| deterministic answer. Tiers: easy(3), medium(4), hard(4), really_hard(4), **expert(15)**. The |
| expert tier targets the agentic failure modes: error-recovery, multi-constraint search, |
| long-horizon dependency chains, read-modify-write loops, fidelity (big-number / exact value), |
| and verification. |
| - **`agent_eval.py`** — a faithful, lightweight agent loop over any **OpenAI-compatible** |
| `/v1/chat/completions` endpoint (works with Ollama, vLLM, etc.) with a real **sandboxed toolset** |
| (`write_file`, `read_file`, `run_python`). Model-agnostic (handles native `tool_calls` *and* raw |
| Hermes `<tool_call>`). Scores **success = answer_ok AND terminated**, with a per-difficulty |
| breakdown. |
| - **`generate_harvest_tasks.py`** — generates a larger TRAIN split of task variations (the 30 stay |
| held-out as the gate). |
| |
| ## Run it |
| |
| ```bash |
| # point at any OpenAI-compatible server (default: local Ollama) |
| AGENTEVAL_URL=http://127.0.0.1:11434/v1/chat/completions \ |
| python agent_eval.py <model> --tasks agent_tasks.json --max-turns 10 |
| ``` |
| |
| ## Leaderboard (30 tasks, success = correct stated answer + clean termination) |
| |
| | model | overall | easy | medium | hard | really_hard | expert | |
| |---|---|---|---|---|---|---| |
| | **Qwen3.5-4B** | **0.90** | 3/3 | 4/4 | 4/4 | 4/4 | 12/15 | |
| | Qwen3.5-2B | 0.77 | 3/3 | 4/4 | 3/4 | 4/4 | 9/15 | |
| | Qwen3.5-0.8B | 0.53 | 3/3 | 2/4 | 3/4 | 3/4 | 5/15 | |
| | *(BFCL-tuned 3B fine-tunes)* | ~0.00 | — | — | — | — | — | |
| |
| Note the punchline: models that **win state-based benchmarks (and have stronger raw reasoning)** can |
| score **~0 here** — they make correct tool calls but **loop and never state the answer**. Even a |
| **0.8B** model that *delivers* answers beats them. The discriminating axis is **termination / |
| answer-delivery**, not tool-call correctness. |
| |
| ## License |
| Apache-2.0. |
| |
