Gemini 4 Argon (effort medium · thinking on) on Go/no-go departure time from a gridded wind file
GGemini 4 Argonmedium · thinking on · Antigravity CLI 1.4.5 (agy -p) · cloud · 2026-10-22 · by @bobsiopener
- Estimated cost
- $0.23
- Wall time
- 5m
- Human step-ins
- 0
- Outcome
- Pass
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Summary
Exact prompt
# Go/no-go departure time from a gridded wind file `input.json` (synthetic; not a forecast) names `grid.json` and gives `from` and `to` [lat, lon], `passage_hours` D (integer), `latest_departure_h`, and `limits`. `grid.json` has `lats`, `lons`, `times_h` (ascending) and fields `u_kt`, `v_kt` (eastward/northward wind components, knots, the direction the air moves TOWARD) and `gust_kt`, each indexed `[time][lat][lon]`. Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `window.svg`. Fresh inputs are used too. ## Rules 1. Interpolation of each field: bilinear in (lat, lon) at the two bracketing grid times, then linear in time. Outside a grid axis, clamp to its edge. 2. Course = atan2((lon_to - lon_from) * cos(mean lat), lat_to - lat_from) in degrees mod 360. 3. For every integer departure hour t0 = 0 .. latest_departure_h: sample k = 0..D at time t0 + k and position from + (to - from) * k / D (straight in lat/lon). At each sample: wind speed = hypot(u, v); wind FROM direction = atan2(-u, -v) mod 360; off = smallest angle between it and the course. The sample fails, checked in this order, with reason `wind@<t>` if speed > max_wind_kt, `gust@<t>` if gust > max_gust_kt, `headwind@<t>` if off < min_wind_angle_deg and speed > headwind_max_kt. Stop at the first failing sample; that departure is not ok and its reason is recorded (t as an integer). Otherwise ok with reason null. 4. `earliest_ok_h`: first ok departure or null. `longest_ok_run`: [first, last] departure hours of the longest run of consecutive ok departures (earliest wins ties), or null. ## Visual output: window.svg Static, self-contained SVG (root `<svg>`, no scripts, images, links or external assets; renders headless) with an element `id="timeline"` (one mark per departure hour) and an element `id="best-departure"`. The drawing itself is not graded. ## answer.json `{"course_deg", "departures": [{"depart_h", "ok", "reason"}, ...], "earliest_ok_h", "longest_ok_run"}` ## Scoring Hidden checks (partial credit; all must pass): `course` (within 0.11), `departure_flags` (ok flags), `failure_reasons` (exact), `best_departure`, `window_svg_static` (window.svg exists, follows the static rules, has the ids above), `fresh_run_1`, `fresh_run_2`. 60-second limit per run.
Space-time interpolation of vector components (not speed and direction), the toward/from convention, ordered failure reasons per sample along a moving position, and run-length selection with ties. Original benchmark task written for usecaserank.com. Headless, offline run on a reproducible fixture. Cost shown is a list-price API-equivalent estimate (run on a Google account via Antigravity CLI, not billed per token).
Benchmark code for comparing AI models, not navigation or safety software.
Success criteria: 1. course 2. departure_flags 3. failure_reasons 4. best_departure 5. window_svg_static 6. fresh_run_1 7. fresh_run_2
Setup notes: Use Case Scout batch10 case k10-wind-grid-departure (offline fixture). Prepared with batch10/prepare.sh on the shared box; only TASK.md + starting files went to the agent machine, hidden keys stayed on the box. Prompt = TASK.md verbatim. Flags: -p --model gemini-4-argon-medium --sandbox --dangerously-skip-permissions --disable-slash-commands --output-format stream-json, cwd=workdir. Scored on the box with `python score.py WORKDIR KEYDIR`. Single run, no retries, no human input. Visual output (window.svg) rendered offline to PNG with google-chrome headless, network blocked, 1280x800 at ~3 s after load. Requested model gemini-4-argon-medium (effort medium); priced at the base model row gemini-4-argon (pricing.json, https://ai.google.dev/gemini-api/docs/pricing). Score: 7/7. Cost is an ESTIMATE: measured tokens x pricing.json list price (Google account, nothing billed per token); output_tokens include thinking tokens. Tool calls 13 (0 failed). Single run.
Estimated cost List-price estimate; not actually billed.
Output
Metrics
- Estimated cost
- $0.23
- Wall time
- 5m
- Human step-ins
- 0
- Turns
- 11
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| GGemini 4 Argongoogle/gemini-4-argoneffort medium · thinking on | 231,319 | 13,236 | $0.23 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: Gemini 4 Argon (google/gemini-4-argon, effort medium · thinking on) via
Antigravity CLI 1.4.5 (agy -p), cloud. - It counts as a success only if it meets every success criterion: 1. course 2. departure_flags 3. failure_reasons 4. best_departure 5. window_svg_static 6. fresh_run_1 7. fresh_run_2
- Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.
How runs are scored: Methodology.
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Automated checks
Trust 80% comes from these checks (how Trust % works).
- Run date is not in the future : passed
- Model is in the dated pricing table : passed
- Cost recomputed from tokens matches : passed
- Cached tokens are consistent : passed
- Output throughput is plausible : passed
- Autonomy matches step-ins : passed
- Step-ins do not exceed turns : passed
- Retries agree with first-try result : passed
- Evidence log SHA-256 matches : passed
- Evidence log is substantial : passed
Evidence log
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{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 7, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 1.479928247, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json"}, "output": "Command exited with code 1.\n\nThere were sandbox errors that may or may not be related to the failure. If you think the failure is because of running in the sandbox, you can run the command again with `BypassSandbox` set to true to request explicit user permission.\n\nCommand output:\nTraceback (most recent call last):\r\n File \"/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k10/work/solve.py\", line 201, in <module>\r\n solve(sys.argv[1], sys.argv[2])\r\n ~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k10/work/solve.py\", line 185, in solve\r\n with open(answer_path, \"w\", encoding=\"utf-8\") as f:\r\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nOSError: [Errno 30] Read-only file system: 'answer.json'\r\n"}}}
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{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 15, "state": "DONE", "step_type": "agent_response", "duration_seconds": 24.647506941, "usage": {"input_tokens": 3675, "output_tokens": 781, "thinking_tokens": 349, "cache_read_tokens": 17978, "total_tokens": 4456}}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 16, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport json, math, solve\nwith open(\"input.json\") as f: inp = json.load(f)\nwith open(\"grid.json\") as f: grid = json.load(f)\nfor t0 in [0, 6, 11, 12, 13, 18, 24, 30]:\n lat_from, lon_from = inp[\"from\"]\n lat_to, lon_to = inp[\"to\"]\n D = inp[\"passage_hours\"]\n for k in range(D+1):\n t = t0 + k\n lat = lat_from + (lat_to - lat_from)*k/D\n lon = lon_from + (lon_to - lon_from)*k/D\n u = solve.interp_field(grid[\"u_kt\"], grid[\"lats\"], grid[\"lons\"], grid[\"times_h\"], t, la\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 16, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 2.851077079, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport json, math, solve\nwith open(\"input.json\") as f: inp = json.load(f)\nwith open(\"grid.json\") as f: grid = json.load(f)\nfor t0 in [0, 6, 11, 12, 13, 18, 24, 30]:\n lat_from, lon_from = inp[\"from\"]\n lat_to, lon_to = inp[\"to\"]\n D = inp[\"passage_hours\"]\n for k in range(D+1):\n t = t0 + k\n lat = lat_from + (lat_to - lat_from)*k/D\n lon = lon_from + (lon_to - lon_from)*k/D\n u = solve.interp_field(grid[\"u_kt\"], grid[\"lats\"], grid[\"lons\"], grid[\"times_h\"], t, la\u2026"}, "output": "Command exited with code 0.\n\nCommand output:\nt0=0 k=0 t=0: u=4.35 v=9.82 spd=10.74 g=14.59 wfrom=203.9\r\nt0=0 k=1 t=1: u=3.24 v=10.04 spd=10.55 g=14.13 wfrom=197.9\r\nt0=0 k=2 t=2: u=2.03 v=9.72 spd=9.93 g=13.92 wfrom=191.8\r\nt0=0 k=3 t=3: u=1.12 v=8.36 spd=8.43 g=14.27 wfrom=187.6\r\nt0=0 k=4 t=4: u=2.29 v=12.94 spd=13.14 g=18.18 wfrom=190.1\r\nt0=0 k=5 t=5: u=3.67 v=16.62 spd=17.02 g=21.67 wfrom=192.5\r\nt0=0 k=6 t=6: u=5.20 v=19.91 spd=20.58 g=24.77 wfrom=194.6\r\nt0=0 k=7 t=7: u=7.10 v=20.20 spd=21.41 g=26.27 wfrom=199.4\r\n---\r\nt0=6 k=0 t=6: u=7.63 v=18.46 spd=19.97 g=26.63 wfrom=202.5\r\nt0=6 k=1 t=7: u=7.31 v=19.08 spd=20.44 g=28.19 wfrom=201.0\r\nt0=6 k=2 t=8: u=8.96 v=19.99 spd=21.91 g=28.81 wfrom=204.2\r\nt0=6 k=3 t=9: u=11.72 v=21.54 spd=24.53 g=28.81 wfrom=208.6\r\nt0=6 k=4 t=10: u=12.80 v=11.79 spd=17.40 g=29.42 wfrom=227.4\r\nt0=6 k=5 t=11: u=15.37 v=2.29 spd=15.54 g=28.09 wfrom=261.5\r\nt0=6 k=6 t=12: u=17.90 v=-6.36 spd=18.99 g=26.22 wfrom=289.6\r\nt0=6 k=7 t=13: u=16.50 v=-5.40 spd=17.36 g=23.97 wfrom=288.1\r\n---\r\nt0=11 k=0 t=11: u=14.83 v=1.54 spd=14.91 g=28.18 wfrom=264.1\r\nt0=11 k=1 t=12: u=17.70 v=-6.82 spd=18.97 g=26.10 wfrom=291.1\r\nt0=11 k=2 t=13: u=16.16 v=-5.72 spd=17.14 g=25.08 wfrom=289.5\r\nt0=11 k=3 t=14: u=14.64 v=-4.80 spd=15.41 g=23.92 wfrom=288.2\r\nt0=11 k=4 t=15: u=13.63 v=-4.44 spd=14.33 g=20.81 wfrom=288.1\r\nt0=11 k=5 t=16: u=12.23 v=-3.35 spd=12.68 g=18.24 wfrom=285.3\r\nt0=11 k=6 t=17: u=10.70 v=-1.91 spd=10.87 g=15.96 wfrom=280.1\r\nt0=11 k=7 t=18: u=8.80 v=-0.50 spd=8.81 g=13.80 wfrom=273.3\r\n---\r\nt0=12 k=0 t=12: u=17.74 v=-7.90 spd=19.42 g=25.88 wfrom=294.0\r\nt0=12 k=1 t=13: u=16.46 v=-6.22 spd=17.60 g=24.39 wfrom=290.7\r\nt0=12 k=2 t=14: u=14.99 v=-5.05 spd=15.82 g=23.14 wfrom=288.6\r\nt0=12 k=3 t=15: u=13.56 v=-3.88 spd=14.11 g=21.65 wfrom=286.0\r\nt0=12 k=4 t=16: u=11.95 v=-3.20 spd=12.37 g=18.61 wfrom=285.0\r\nt0=12 k=5 t=17: u=10.64 v=-2.03 spd=10.83 g=16.27 wfrom=280.8\r\nt0=12 k=6 t=18: u=8.98 v=-0.58 spd=9.00 g=14.03 wfrom=273.7\r\nt0=12 k=7 t=19: u=8.97 v=-0.27 spd=8.97 g=13.77 wfrom=271.7\r\n---\r\nt0=13 k=0 t=13: u=16.46 v=-7.17 spd=17.96 g=24.28 wfrom=293.5\r\nt0=13 k=1 t=14: u=15.23 v=-5.62 spd=16.23 g=22.68 wfrom=290.3\r\nt0=13 k=2 t=15: u=13.83 v=-4.38 spd=14.50 g=21.20 wfrom=287.6\r\nt0=13 k=3 t=16: u=11.57 v=-2.78 spd=11.90 g=19.02 wfrom=283.5\r\nt0=13 k=4 t=17: u=10.26 v=-1.96 spd=10.45 g=16.42 wfrom=280.8\r\nt0=13 k=5 t=18: u=9.05 v=-0.71 spd=9.08 g=14.31 wfrom=274.5\r\nt0=13 k=6 t=19: u=9.04 v=-0.46 spd=9.06 g=13.91 wfrom=272.9\r\nt0=13 k=7 t=20: u=9.13 v=-0.03 spd=9.13 g=13.73 wfrom=270.2\r\n---\r\nt0=18 k=0 t=18: u=10.66 v=-3.27 spd=11.15 g=13.90 wfrom=287.1\r\nt0=18 k=1 t=19: u=9.84 v=-2.00 spd=10.04 g=13.73 wfrom=281.5\r\nt0=18 k=2 t=20: u=9.10 v=-1.15 spd=9.17 g=13.88 wfrom=277.2\r\nt0=18 k=3 t=21: u=9.23 v=-0.74 spd=9.26 g=14.28 wfrom=274.6\r\nt0=18 k=4 t=22: u=9.15 v=-1.80 spd=9.33 g=13.65 wfrom=281.2\r\nt0=18 k=5 t=23: u=8.75 v=-2.23 spd=9.03 g=13.42 wfrom=284.3\r\nt0=18 k=6 t=24: u=7.38 v=-2.49 spd=7.79 g=13.50 wfrom=288.6\r\nt0=18 k=7 t=25: u=6.97 v=-2.27 spd=7.33 g=13.53 wfrom=288.0\r\n---\r\nt0=24 k=0 t=24: u=8.44 v=-3.44 spd=9.11 g=13.62 wfrom=292.2\r\nt0=24 k=1 t=25: u=9.30 v=-2.50 spd=9.63 g=12.76 wfrom=285.1\r\nt0=24 k=2 t=26: u=9.96 v=-2.85 spd=10.36 g=12.19 wfrom=286.0\r\nt0=24 k=3 t=27: u=10.21 v=-3.59 spd=10.82 g=11.55 wfrom=289.4\r\nt0=24 k=4 t=28: u=8.96 v=-2.39 spd=9.28 g=11.76 wfrom=284.9\r\nt0=24 k=5 t=29: u=7.36 v=-1.96 spd=7.62 g=11.51 wfrom=285.0\r\nt0=24 k=6 t=30: u=4.98 v=-2.24 spd=5.46 g=10.60 wfrom=294.2\r\nt0=24 k=7 t=31: u=6.10 v=-3.80 spd=7.19 g=11.37 wfrom=301.9\r\n---\r\nt0=30 k=0 t=30: u=7.66 v=-1.77 spd=7.86 g=10.34 wfrom=283.0\r\nt0=30 k=1 t=31: u=8.00 v=-1.87 spd=8.22 g=11.28 wfrom=283.1\r\nt0=30 k=2 t=32: u=9.03 v=-1.45 spd=9.15 g=12.35 wfrom=279.1\r\nt0=30 k=3 t=33: u=10.78 v=-1.30 spd=10.86 g=13.64 wfrom=276.9\r\nt0=30 k=4 t=34: u=10.23 v=-2.98 spd=10.65 g=14.06 wfrom=286.3\r\nt0=30 k=5 t=35: u=10.23 v=-4.52 spd=11.19 g=15.06 wfrom=293.8\r\nt0=30 k=6 t=36: u=9.78 v=-6.51 spd=11.75 g=16.72 wfrom=303.6\r\nt0=30 k=7 t=37: u=8.43 v=-6.60 spd=10.71 g=15.00 wfrom=308.0\r\n---\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 17, "state": "DONE", "step_type": "agent_response", "duration_seconds": 35.363691728, "usage": {"input_tokens": 3944, "output_tokens": 2488, "thinking_tokens": 2177, "cache_read_tokens": 21654, "total_tokens": 6432}}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 18, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport json, math\nwith open(\"grid.json\") as f: g = json.load(f)\nfor ti, t in enumerate(g[\"times_h\"]):\n us = [x for row in g[\"u_kt\"][ti] for x in row]\n vs = [x for row in g[\"v_kt\"][ti] for x in row]\n gs = [x for row in g[\"gust_kt\"][ti] for x in row]\n u_m = sum(us)/len(us)\n v_m = sum(vs)/len(vs)\n spd = math.hypot(u_m, v_m)\n a1 = math.degrees(math.atan2(-u_m, -v_m)) % 360\n print(f\"t={t:2d}: u_mean={u_m:6.2f}, v_mean={v_m:6.2f}, spd={spd:5.2f}, gust_max={max(gs):5.2f}, atan2\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 18, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 3.6896723, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport json, math\nwith open(\"grid.json\") as f: g = json.load(f)\nfor ti, t in enumerate(g[\"times_h\"]):\n us = [x for row in g[\"u_kt\"][ti] for x in row]\n vs = [x for row in g[\"v_kt\"][ti] for x in row]\n gs = [x for row in g[\"gust_kt\"][ti] for x in row]\n u_m = sum(us)/len(us)\n v_m = sum(vs)/len(vs)\n spd = math.hypot(u_m, v_m)\n a1 = math.degrees(math.atan2(-u_m, -v_m)) % 360\n print(f\"t={t:2d}: u_mean={u_m:6.2f}, v_mean={v_m:6.2f}, spd={spd:5.2f}, gust_max={max(gs):5.2f}, atan2\u2026"}, "output": "Command exited with code 0.\n\nCommand output:\nt= 0: u_mean= 3.19, v_mean= 9.88, spd=10.38, gust_max=15.20, atan2(-u,-v)=197.9\r\nt= 3: u_mean= 0.81, v_mean= 10.79, spd=10.82, gust_max=14.50, atan2(-u,-v)=184.3\r\nt= 6: u_mean= 5.87, v_mean= 18.74, spd=19.64, gust_max=28.20, atan2(-u,-v)=197.4\r\nt= 9: u_mean= 9.38, v_mean= 21.00, spd=23.00, gust_max=34.10, atan2(-u,-v)=204.1\r\nt=12: u_mean= 18.23, v_mean= -7.59, spd=19.75, gust_max=28.60, atan2(-u,-v)=292.6\r\nt=15: u_mean= 14.00, v_mean= -4.94, spd=14.85, gust_max=21.70, atan2(-u,-v)=289.5\r\nt=18: u_mean= 10.03, v_mean= -1.53, spd=10.15, gust_max=15.20, atan2(-u,-v)=278.7\r\nt=21: u_mean= 9.58, v_mean= -1.83, spd= 9.75, gust_max=14.30, atan2(-u,-v)=280.8\r\nt=24: u_mean= 8.82, v_mean= -3.57, spd= 9.52, gust_max=14.20, atan2(-u,-v)=292.0\r\nt=27: u_mean= 9.23, v_mean= -2.08, spd= 9.46, gust_max=13.50, atan2(-u,-v)=282.7\r\nt=30: u_mean= 7.22, v_mean= -1.77, spd= 7.44, gust_max=12.10, atan2(-u,-v)=283.7\r\nt=33: u_mean= 8.79, v_mean= -2.54, spd= 9.15, gust_max=13.70, atan2(-u,-v)=286.1\r\nt=36: u_mean= 10.82, v_mean= -4.29, spd=11.64, gust_max=17.10, atan2(-u,-v)=291.6\r\nt=39: u_mean= 5.29, v_mean= -5.12, spd= 7.36, gust_max=11.40, atan2(-u,-v)=314.1\r\nt=42: u_mean= 10.21, v_mean= -6.13, spd=11.91, gust_max=17.20, atan2(-u,-v)=301.0\r\nt=45: u_mean= 10.18, v_mean= -6.08, spd=11.85, gust_max=17.20, atan2(-u,-v)=300.8\r\nt=48: u_mean= 9.89, v_mean= -0.70, spd= 9.91, gust_max=14.00, atan2(-u,-v)=274.0\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 19, "state": "DONE", "step_type": "agent_response", "duration_seconds": 55.160966695, "usage": {"input_tokens": 3544, "output_tokens": 3313, "thinking_tokens": 428, "cache_read_tokens": 25599, "total_tokens": 6857}}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 20, "state": "ACTIVE", "step_type": "tool", "tool_name": "write_file", "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k10/work/answer.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 20, "state": "DONE", "step_type": "tool", "tool_name": "write_file", "duration_seconds": 1.582934265, "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k10/work/answer.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 21, "state": "ACTIVE", "step_type": "tool", "tool_name": "write_file", "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k10/work/window.svg"}}}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 21, "state": "DONE", "step_type": "tool", "tool_name": "write_file", "duration_seconds": 3.125651554, "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k10/work/window.svg"}}}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 22, "state": "DONE", "step_type": "agent_response", "duration_seconds": 26.061357462, "usage": {"input_tokens": 3473, "output_tokens": 221, "thinking_tokens": 57, "cache_read_tokens": 29144, "total_tokens": 3694}}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 23, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport json, os, solve, xml.etree.ElementTree as ET\n\n# Test SVG XML validity and required IDs\ntree = ET.parse(\"window.svg\")\nroot = tree.getroot()\nassert root.tag.endswith(\"svg\")\ntimeline = root.find(\".//*[@id=\\\"timeline\\\"]\")\nassert timeline is not None and len(list(timeline)) == 31\nbest = root.find(\".//*[@id=\\\"best-departure\\\"]\")\nassert best is not None\nprint(\"All assertions passed!\")\n'"}}}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 23, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 1.816031191, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport json, os, solve, xml.etree.ElementTree as ET\n\n# Test SVG XML validity and required IDs\ntree = ET.parse(\"window.svg\")\nroot = tree.getroot()\nassert root.tag.endswith(\"svg\")\ntimeline = root.find(\".//*[@id=\\\"timeline\\\"]\")\nassert timeline is not None and len(list(timeline)) == 31\nbest = root.find(\".//*[@id=\\\"best-departure\\\"]\")\nassert best is not None\nprint(\"All assertions passed!\")\n'"}, "output": "Command exited with code 0.\n\nCommand output:\nAll assertions passed!\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "Implemented [solve.py](file:///home/"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "bob/usecase-runs/agy-bat"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ch/out/gemin"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "i-4-argon-medium/k10"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "/work/solve.py) and generated [answer.json](file:///home/bob"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "/usecase-run"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "s"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "/agy-batch/out/gemini-4-argon-medium"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "/k10/work/answer.json"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": ") and [window.svg](file:///home/bob/"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "usecase-runs"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "/agy-batch/out/"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "gemini-4-argon-medium/k1"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "0/work/windo"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "w.svg):\n\n- **Interpolati"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "on & Cl"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "amping**: [`interp_field"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "`](file:///home/bob/usec"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ase-runs/agy"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-batch/out/"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "gemini-4-argon-medium/k10/work/solve"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": ".py#L29-L3"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "7) clamps out-of-range coordinates to axis edges"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " via [`clamp_and_bracket"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "`](file:///h"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ome/"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "bob/usecase-runs/agy-bat"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ch/out/gemini-4-argon-me"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "dium/k10"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "/work/solve.py#L7-L17), performs bilinear interp"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "olation in `"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "(lat, lon)` via"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " [`interp_lat_lon`](file:///home/bob"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "/usecase-run"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "s/agy-batch/out/gemini-4"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-argon-"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "medium/k10/work/solve.py#L20-L26) at"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " the two bra"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "cketing grid times, and "}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "linearly"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " interpolates in time.\n- **Course & "}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "Sampling**: [`solve`](fi"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "le:///home/b"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ob/usecase-runs/agy"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-batch/out/gemini-4-argon-medium/k10"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "/work/solve.py#L"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "84-L202) computes course (`39.976777"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "\u00b0`), evalua"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "tes each"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " integer departure `t0 = 0..latest_departure_h` "}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "along `k = 0"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "..D`,"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " checks `wind@<t>`, `gus"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "t@<t>`, and "}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "`headwind@<t>` in order,"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " and identifies `earlies"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "t_ok_h` (`12`) and `long"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "est_ok_run` "}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "(`"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "[12, 30]`).\n- **Static Visualization"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "**: [`generate_svg`](fil"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "e:///home/bo"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "b/usecase"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-runs/agy-batch/out/gemi"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ni-4-argon-medium/k10/wo"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "rk/solve"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": ".py#L45-L81) writes a se"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "lf-contained static SVG containing `#timeline` (one mark per departure h"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "our) and `#best-departur"}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "e`."}}
{"event": "step_update", "step_update": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "step_index": 24, "state": "DONE", "step_type": "agent_response", "text_delta": "\n", "duration_seconds": 19.097302749, "usage": {"input_tokens": 2364, "output_tokens": 563, "thinking_tokens": 17, "cache_read_tokens": 30568, "total_tokens": 2927}}}
{"event": "result", "result": {"conversation_id": "850cbf9f-8d6e-4a78-acc3-3d9ac3f2726f", "status": "SUCCESS", "response": "Implemented [solve.py](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k10/work/solve.py) and generated [answer.json](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k10/work/answer.json) and [window.svg](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k10/work/window.svg):\n\n- **Interpolation & Clamping**: [`interp_field`](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k10/work/solve.py#L29-L37) clamps out-of-range coordinates to axis edges via [`clamp_and_bracket`](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k10/work/solve.py#L7-L17), performs bilinear interpolation in `(lat, lon)` via [`interp_lat_lon`](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k10/work/solve.py#L20-L26) at the two bracketing grid times, and linearly interpolates in time.\n- **Course & Sampling**: [`solve`](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k10/work/solve.py#L84-L202) computes course (`39.976777\u00b0`), evaluates each integer departure `t0 = 0..latest_departure_h` along `k = 0..D`, checks `wind@<t>`, `gust@<t>`, and `headwind@<t>` in order, and identifies `earliest_ok_h` (`12`) and `longest_ok_run` (`[12, 30]`).\n- **Static Visualization**: [`generate_svg`](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k10/work/solve.py#L45-L81) writes a self-contained static SVG containing `#timeline` (one mark per departure hour) and `#best-departure`.\n", "duration_seconds": 303.017754387, "num_turns": 1, "usage": {"input_tokens": 41122, "output_tokens": 13236, "thinking_tokens": 5665, "cache_read_tokens": 190197, "total_tokens": 54358}}}