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

# 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.