Gemini 4 Argon (effort medium · thinking on) on Great-circle vs rhumb-line passage plan with ETAs

GGemini 4 Argonmedium · thinking on · Antigravity CLI 1.4.5 (agy -p) · cloud · 2026-10-22 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first tryCompare

Estimated cost: $0.21
Wall time: 4m
Human step-ins: 0
Outcome: Pass

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Exact prompt

# Great-circle vs rhumb-line passage plan with ETAs `input.json` (synthetic; not for navigation): `waypoints` ([lat, lon] decimal degrees, legs may cross the 180° meridian), `speed_kn`, `depart_utc` ("YYYY-MM-DDTHH:MM:SSZ"). Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `route.svg`. Fresh inputs are used too. ## Rules (sphere, R = 3440.065 nm) - Longitude difference dlon is always wrapped into [-180°, 180°). - **Great circle**: haversine distance; initial course = atan2(sin dlon * cos lat2, cos lat1 * sin lat2 - sin lat1 * cos lat2 * cos dlon) mod 360. - **Rhumb line**: dpsi = ln(tan(pi/4 + lat2/2) / tan(pi/4 + lat1/2)); q = dlat / dpsi, or cos(lat1) when |dpsi| <= 1e-12; distance = sqrt(dlat^2 + q^2 * dlon^2) * R (radians); course = atan2(dlon, dpsi) mod 360. - The boat sails the **rhumb** legs at `speed_kn`; leg ETA = departure + cumulative rhumb distance / speed, rounded to the nearest minute (half a minute rounds up), formatted "YYYY-MM-DDTHH:MMZ". - `rhumb_extra_pct` = (total rhumb - total GC) / total GC * 100. Round distances to 2 decimals, courses to 1, the percentage to 3. ## Visual output: route.svg Static, self-contained SVG (root `<svg>`, no scripts, images, links or external assets; renders headless) with `<polyline id="route-gc">`, `<polyline id="route-rhumb">` and an element `id="waypoints"`. The drawing (projection etc.) is not graded. ## answer.json `{"legs": [{"gc_nm", "gc_initial_course", "rhumb_nm", "rhumb_course", "eta_utc"}, ...], "total_gc_nm", "total_rhumb_nm", "rhumb_extra_pct", "arrival_utc"}` ## Scoring Hidden checks (partial credit; all must pass): `great_circle_legs`, `rhumb_legs` (first four legs; distances within 0.011, courses within 0.11), `totals`, `etas` (exact strings), `route_svg_static` (route.svg exists, follows the static rules, has the ids/tags above), `fresh_run_1`, `fresh_run_2` (all keys, within 0.02). 60-second limit per run.

Antimeridian wrap on both methods, the due east/west rhumb leg where dpsi is zero, initial great-circle course, and minute-rounded UTC ETAs that drift if per-leg rounding is used. 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. great_circle_legs 2. rhumb_legs 3. totals 4. etas 5. route_svg_static 6. fresh_run_1 7. fresh_run_2

Setup notes: Use Case Scout batch10 case k09-passage-gc-vs-rhumb (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 (route.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 9 (0 failed). Single run.

Estimated cost List-price estimate; not actually billed.

Output

Gemini 4 Argon (effort medium · thinking on) on Great-circle vs rhumb-line passage plan with ETAsSuccess

Metrics

Estimated cost: $0.21
Wall time: 4m
Human step-ins: 0
Turns: 10

Model usage
Model	Input tok	Output tok	Cost
GGemini 4 Argongoogle/gemini-4-argoneffort medium · thinking on	170,754	12,469	$0.21

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. great_circle_legs 2. rhumb_legs 3. totals 4. etas 5. route_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

{"event": "init", "conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "init": {"model": "gemini-4-argon-medium", "cwd": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k09/work", "tools": ["ask_custom_permission", "ask_permission", "ask_question", "browser_click_element", "browser_drag_pixel_to_pixel", "browser_get_dom", "browser_get_network_request", "browser_input", "browser_list_network_requests", "browser_mouse_down", "browser_mouse_up", "browser_move_mouse", "browser_press_key", "browser_refresh_page", "browser_resize_window", "browser_scroll", "browser_scroll_dom", "browser_select_option", "browser_subagent", "call_mcp_tool", "capture_browser_console_logs", "capture_browser_screenshot", "click_browser_pixel", "command_status", "define_subagent", "delete_knowledge", "execute_browser_javascript", "find_by_name", "finish", "generate_image", "grep_search", "invoke_subagent", "list_browser_pages", "list_dir", "list_permissions", "list_plugin_accounts", "list_resources", "manage_inbox", "manage_subagents", "manage_task", "multi_replace_file_content", "notebook_edit", "notebook_execution", "open_browser_url", "read_browser_page", "read_resource", "read_url_content", "replace_file_content", "run_command", "run_workflow", "schedule", "search_marketplace", "search_web", "sed_file", "send_command_input", "send_message", "view_file", "wait", "wait_5_seconds", "write_to_file"], "permission_mode": "always-proceed"}}
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{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 1, "state": "DONE", "step_type": "agent_response", "duration_seconds": 12.289684011, "usage": {"input_tokens": 8443, "output_tokens": 78, "thinking_tokens": 24, "cache_read_tokens": 0, "total_tokens": 8521}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 2, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k09/work/input.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 2, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.060032, "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k09/work/input.json"}, "output": "30 lines, 285 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 3, "state": "DONE", "step_type": "agent_response", "duration_seconds": 60.873703279, "usage": {"input_tokens": 2529, "output_tokens": 4692, "thinking_tokens": 1454, "cache_read_tokens": 6389, "total_tokens": 7221}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 4, "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/k09/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 4, "state": "DONE", "step_type": "tool", "tool_name": "write_file", "duration_seconds": 1.829035149, "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k09/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 5, "state": "DONE", "step_type": "agent_response", "duration_seconds": 3.61383478, "usage": {"input_tokens": 4773, "output_tokens": 108, "thinking_tokens": 45, "cache_read_tokens": 8919, "total_tokens": 4881}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 6, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json && cat answer.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 6, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 1.558498309, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json && cat 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/k09/work/solve.py\", line 229, in <module>\r\n    main()\r\n    ~~~~^^\r\n  File \"/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k09/work/solve.py\", line 214, in main\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"}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 7, "state": "DONE", "step_type": "agent_response", "duration_seconds": 9.735188977, "usage": {"input_tokens": 2451, "output_tokens": 771, "thinking_tokens": 367, "cache_read_tokens": 11643, "total_tokens": 3222}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 8, "state": "ACTIVE", "step_type": "tool", "tool_name": "edit_file", "tool_info": {"name": "edit_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k09/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 8, "state": "DONE", "step_type": "tool", "tool_name": "edit_file", "duration_seconds": 1.362650803, "tool_info": {"name": "edit_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k09/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 9, "state": "DONE", "step_type": "agent_response", "duration_seconds": 12.714789051, "usage": {"input_tokens": 2949, "output_tokens": 497, "thinking_tokens": 34, "cache_read_tokens": 12045, "total_tokens": 3446}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 10, "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: data = json.load(f)\nwps = data[\"waypoints\"]\nspd = data[\"speed_kn\"]\ndep = solve.parse_utc(data[\"depart_utc\"])\ncum_raw = 0.0\ncum_rnd = 0.0\ngc_raw = 0.0\ngc_rnd = 0.0\nfor i in range(len(wps)-1):\n    gc, gcc, rh, rhc = solve.compute_leg(*wps[i], *wps[i+1])\n    cum_raw += rh\n    cum_rnd += round(rh, 2)\n    gc_raw += gc\n    gc_rnd += round(gc, 2)\n    t_raw = (cum_raw / spd) * 60.0\n    t_rnd = (cum_rnd / spd) * 60.0\n    print(f\"Leg {i+1}: gc={gc:.6\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 10, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 1.538839037, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport json, math, solve\nwith open(\"input.json\") as f: data = json.load(f)\nwps = data[\"waypoints\"]\nspd = data[\"speed_kn\"]\ndep = solve.parse_utc(data[\"depart_utc\"])\ncum_raw = 0.0\ncum_rnd = 0.0\ngc_raw = 0.0\ngc_rnd = 0.0\nfor i in range(len(wps)-1):\n    gc, gcc, rh, rhc = solve.compute_leg(*wps[i], *wps[i+1])\n    cum_raw += rh\n    cum_rnd += round(rh, 2)\n    gc_raw += gc\n    gc_rnd += round(gc, 2)\n    t_raw = (cum_raw / spd) * 60.0\n    t_rnd = (cum_rnd / spd) * 60.0\n    print(f\"Leg {i+1}: gc={gc:.6\u2026"}, "output": "Command exited with code 0.\n\nCommand output:\nLeg 1: gc=232.744371 rh=232.747805 min_raw=1662.4843 min_rnd=1662.5000 eta_raw=2026-11-21T21:12Z eta_rnd=2026-11-21T21:13Z\r\nLeg 2: gc=604.315225 rh=604.436675 min_raw=5979.8891 min_rnd=5979.9286 eta_raw=2026-11-24T21:10Z eta_rnd=2026-11-24T21:10Z\r\nLeg 3: gc=770.530510 rh=770.984734 min_raw=11486.9230 min_rnd=11486.9286 eta_raw=2026-11-28T16:57Z eta_rnd=2026-11-28T16:57Z\r\nLeg 4: gc=375.273778 rh=375.291136 min_raw=14167.5739 min_rnd=14167.5714 eta_raw=2026-11-30T13:38Z eta_rnd=2026-11-30T13:38Z\r\nLeg 5: gc=122.303870 rh=122.305796 min_raw=15041.1868 min_rnd=15041.2143 eta_raw=2026-12-01T04:11Z eta_rnd=2026-12-01T04:11Z\r\nTotals raw: 2105.17 2105.77 0.028\r\nTotals rnd: 2105.16 2105.77 0.029\r\n{\r\n  \"legs\": [\r\n    {\r\n      \"gc_nm\": 232.74,\r\n      \"gc_initial_course\": 156.1,\r\n      \"rhumb_nm\": 232.75,\r\n      \"rhumb_course\": 155.6,\r\n      \"eta_utc\": \"2026-11-21T21:12Z\"\r\n    },\r\n    {\r\n      \"gc_nm\": 604.32,\r\n      \"gc_initial_course\": 41.6,\r\n      \"rhumb_nm\": 604.44,\r\n      \"rhumb_course\": 39.5,\r\n      \"eta_utc\": \"2026-11-24T21:10Z\"\r\n    },\r\n    {\r\n      \"gc_nm\": 770.53,\r\n      \"gc_initial_course\": 93.4,\r\n      \"rhumb_nm\": 770.98,\r\n      \"rhumb_course\": 90.0,\r\n      \"eta_utc\": \"2026-11-28T16:57Z\"\r\n    },\r\n    {\r\n      \"gc_nm\": 375.27,\r\n      \"gc_initial_course\": 149.8,\r\n      \"rhumb_nm\": 375.29,\r\n      \"rhumb_course\": 148.9,\r\n      \"eta_utc\": \"2026-11-30T13:38Z\"\r\n    },\r\n    {\r\n      \"gc_nm\": 122.3,\r\n      \"gc_initial_course\": 125.9,\r\n      \"rhumb_nm\": 122.31,\r\n      \"rhumb_course\": 125.3,\r\n      \"eta_utc\": \"2026-12-01T04:11Z\"\r\n    }\r\n  ],\r\n  \"total_gc_nm\": 2105.17,\r\n  \"total_rhumb_nm\": 2105.77,\r\n  \"rhumb_extra_pct\": 0.028,\r\n  \"arrival_utc\": \"2026-12-01T04:11Z\"\r\n}\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 11, "state": "DONE", "step_type": "agent_response", "duration_seconds": 32.977591393, "usage": {"input_tokens": 2217, "output_tokens": 2175, "thinking_tokens": 2148, "cache_read_tokens": 14465, "total_tokens": 4392}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 12, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"Path": "/tmp/route.svg"}}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 12, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.105175103, "tool_info": {"name": "read_file", "parameters": {"Path": "/tmp/route.svg"}, "output": "20 lines, 3293 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 13, "state": "DONE", "step_type": "agent_response", "duration_seconds": 11.824421549, "usage": {"input_tokens": 4797, "output_tokens": 671, "thinking_tokens": 50, "cache_read_tokens": 16683, "total_tokens": 5468}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 14, "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/k09/work/answer.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 14, "state": "DONE", "step_type": "tool", "tool_name": "write_file", "duration_seconds": 2.700258793, "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k09/work/answer.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 15, "state": "DONE", "step_type": "agent_response", "duration_seconds": 25.501559378, "usage": {"input_tokens": 2800, "output_tokens": 2592, "thinking_tokens": 48, "cache_read_tokens": 19431, "total_tokens": 5392}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 16, "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/k09/work/route.svg"}}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 16, "state": "DONE", "step_type": "tool", "tool_name": "write_file", "duration_seconds": 2.228388474, "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k09/work/route.svg"}}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 17, "state": "DONE", "step_type": "agent_response", "duration_seconds": 11.530351735, "usage": {"input_tokens": 2671, "output_tokens": 292, "thinking_tokens": 45, "cache_read_tokens": 22232, "total_tokens": 2963}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 18, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport json, xml.etree.ElementTree as ET\nwith open(\"answer.json\") as f:\n    ans = json.load(f)\nassert set(ans.keys()) == {\"legs\", \"total_gc_nm\", \"total_rhumb_nm\", \"rhumb_extra_pct\", \"arrival_utc\"}\nassert len(ans[\"legs\"]) == 5\ntree = ET.parse(\"route.svg\")\nroot = tree.getroot()\nids = {el.attrib.get(\"id\"): el.tag.split(\"}\")[-1] for el in root.iter() if \"id\" in el.attrib}\nprint(\"SVG IDs:\", ids)\nassert ids.get(\"route-gc\") == \"polyline\"\nassert ids.get(\"route-rhumb\") == \"polyline\"\nassert \"waypoints\" i\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 18, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 3.21513254, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport json, xml.etree.ElementTree as ET\nwith open(\"answer.json\") as f:\n    ans = json.load(f)\nassert set(ans.keys()) == {\"legs\", \"total_gc_nm\", \"total_rhumb_nm\", \"rhumb_extra_pct\", \"arrival_utc\"}\nassert len(ans[\"legs\"]) == 5\ntree = ET.parse(\"route.svg\")\nroot = tree.getroot()\nids = {el.attrib.get(\"id\"): el.tag.split(\"}\")[-1] for el in root.iter() if \"id\" in el.attrib}\nprint(\"SVG IDs:\", ids)\nassert ids.get(\"route-gc\") == \"polyline\"\nassert ids.get(\"route-rhumb\") == \"polyline\"\nassert \"waypoints\" i\u2026"}, "output": "Command exited with code 0.\n\nCommand output:\nSVG IDs: {'route-gc': 'polyline', 'route-rhumb': 'polyline', 'waypoints': 'g'}\r\nAll checks passed!\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "Implemented [solve.py](f"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ile:///home/"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "bob/use"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "case-runs/agy-batch/out/gemini-4-argon-medium/k0"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "9/work/"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "solve.py) and generated [answer.json"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "](file:///home/bob/useca"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "se-runs/agy-"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "batch/out/gemini-4-argon-medium/k09/"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "work/answer."}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "json) and"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " [route.svg](file:///home/bob/usecas"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "e-runs/agy-b"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "atch/out/gemini-4"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-argon-medium/k09/work/route.svg) fr"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "om [input.js"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "on](file:///home"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "/bob/usecase-runs/agy-batch/out/gemi"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ni-4-argon-m"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "edium/k0"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "9/work/input.json).\n\n### Summary of "}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "Implementation\n- **Great"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-circle & Rh"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "umb-line Calculations"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "** ([`compute_leg`](file"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": ":///home/bob/usecase-run"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "s/agy-batch/"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "out/gemini"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-4-argon-medium/k09/work"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "/solve.py#L29-L57"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": ")): Wraps $\\Delta\\lambda$ into $"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "[-180^\\circ,"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " 180^\\circ)$, computes s"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "pherical hav"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ersine distance and init"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ial bearing "}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "($R = 3440.065\\text{ nm}"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "$), and computes Mercato"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "r rh"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "umb-line distance and course with th"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "e $|\\Delta\\psi| \\le 10^{"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-12}$ fallba"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ck $q = \\cos\\"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "varphi_1$.\n- **ETAs & Totals** ([`format_eta_nea"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "rest_minute`"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "](file:///home/"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "bob/usecase-runs/agy-batch/out/gemin"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "i-4-argon-me"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "dium/k09"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "/work/solve.py#L22-L26),"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " [`main`](fi"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "le:///home/bob/usecase-r"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "uns/agy-"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "batch/out/gemini-4-argon-medium/k09/"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "work/solve.p"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "y#L1"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "63-L237)): Accumulates rhumb distanc"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "es at `speed_kn` from `d"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "epart_utc`, rounding hal"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "f"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-minutes up "}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "(`math.floor(ts / 60.0 +"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " 0.5)`) to `\"YYYY-MM-DDT"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "H"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "H:MMZ\"`.\n- **Static Visualization** "}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "([`build_svg`](file:///h"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ome/bob/"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "usecase-runs/agy-batch/out/gemini-4-"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "argon-medium"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "/k09/work"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "/solve.py#L93-L160)): Produces a sel"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "f-contained `<svg>` with `<polyline id=\"route-gc\">`, `<polyl"}}
{"event": "step_update", "step_update": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "step_index": 19, "state": "DONE", "step_type": "agent_response", "text_delta": "ine id=\"route-rhumb\">`, and `<g id=\"waypoints\">`.\n", "duration_seconds": 12.626100331, "usage": {"input_tokens": 2463, "output_tokens": 593, "thinking_tokens": 14, "cache_read_tokens": 22854, "total_tokens": 3056}}}
{"event": "result", "result": {"conversation_id": "cc4598dd-834a-4d40-98e6-fc0ccf67d490", "status": "SUCCESS", "response": "Implemented [solve.py](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k09/work/solve.py) and generated [answer.json](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k09/work/answer.json) and [route.svg](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k09/work/route.svg) from [input.json](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k09/work/input.json).\n\n### Summary of Implementation\n- **Great-circle & Rhumb-line Calculations** ([`compute_leg`](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k09/work/solve.py#L29-L57)): Wraps $\\Delta\\lambda$ into $[-180^\\circ, 180^\\circ)$, computes spherical haversine distance and initial bearing ($R = 3440.065\\text{ nm}$), and computes Mercator rhumb-line distance and course with the $|\\Delta\\psi| \\le 10^{-12}$ fallback $q = \\cos\\varphi_1$.\n- **ETAs & Totals** ([`format_eta_nearest_minute`](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k09/work/solve.py#L22-L26), [`main`](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k09/work/solve.py#L163-L237)): Accumulates rhumb distances at `speed_kn` from `depart_utc`, rounding half-minutes up (`math.floor(ts / 60.0 + 0.5)`) to `\"YYYY-MM-DDTHH:MMZ\"`.\n- **Static Visualization** ([`build_svg`](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k09/work/solve.py#L93-L160)): Produces a self-contained `<svg>` with `<polyline id=\"route-gc\">`, `<polyline id=\"route-rhumb\">`, and `<g id=\"waypoints\">`.\n", "duration_seconds": 207.596018469, "num_turns": 1, "usage": {"input_tokens": 36093, "output_tokens": 12469, "thinking_tokens": 4229, "cache_read_tokens": 134661, "total_tokens": 48562}}}