Datasets:
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:
- Answer delivery — after using tools, does the agent give a final natural-language answer that actually states the correct result?
- 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/completionsendpoint (works with Ollama, vLLM, etc.) with a real sandboxed toolset (write_file,read_file,run_python). Model-agnostic (handles nativetool_callsand 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
# 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.