Gemini 4 Argon (effort medium · thinking on) on Anchor drag alarm from a GPS track
GGemini 4 Argonmedium · thinking on · Antigravity CLI 1.4.5 (agy -p) · cloud · 2026-10-22 · by @bobsiopener
- Estimated cost
- $0.22
- Wall time
- 3m
- Human step-ins
- 0
- Outcome
- Pass
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Summary
Exact prompt
# Anchor drag alarm from a GPS track `input.json` (synthetic, not for real anchor watch): `anchor` [lat, lon], `rode_m`, `depth_m`, `bow_height_m`, `gps_offset_m`, `margin_m`, `consecutive` N, and `track` (CSV `t,lat,lon`, t in seconds). Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `anchor.svg`. Fresh inputs are used too. ## Rules 1. Alarm radius = sqrt(rode^2 - (depth + bow_height)^2) (0 if rode <= depth + bow_height) + gps_offset + margin. 2. Track rows: skip the header. A row is **bad** (counted, skipped) if it has fewer than 3 fields, t is not an integer, lat/lon is not a finite number in range [-90, 90] / [-180, 180], or t is not greater than the last *valid* row's t. 3. Distance from the anchor (metres): R = 6371000; m = mean of the anchor and point latitudes; x = radians(dlon) * cos(m) * R (east), y = radians(dlat) * R (north); distance = hypot(x, y). A point is **outside** if distance > radius. 4. An **event** is a maximal run of consecutive valid points that are outside, with length >= N. Report `start_t` (first point), `trigger_t` (Nth point), `end_t` (last point), `max_dist_m`, and `bearing_deg` = atan2(x, y) mod 360 from the anchor to the run's last point. 5. Round the radius to 2 decimals and distances and bearings to 1. ## Visual output: anchor.svg Static, self-contained SVG (root `<svg>`, no scripts, images, links or external assets; renders headless) with `<circle id="swing-circle">`, `<polyline id="track">` and an element `id="anchor"`. The drawing itself is not graded. ## answer.json `{"radius_m", "valid_points", "bad_rows", "events": [...], "max_dist_m", "outside_points"}` ## Scoring Hidden checks (partial credit; all must pass): `swing_radius` (within 0.011), `track_cleaning` (exact), `distances`, `alarm_events` (times exact, values within 0.11), `anchor_svg_static` (anchor.svg exists, follows the static rules, has the ids/tags above), `fresh_run_1`, `fresh_run_2`. 60-second limit per run.
Catenary-free swing radius with offsets, rejecting bad and out-of-order rows against the last valid time, short excursions that must not trigger, and a real drag run with trigger time and bearing. 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. swing_radius 2. track_cleaning 3. distances 4. alarm_events 5. anchor_svg_static 6. fresh_run_1 7. fresh_run_2
Setup notes: Use Case Scout batch10 case k07-anchor-drag-alarm (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 (anchor.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 10 (0 failed). Single run.
Estimated cost List-price estimate; not actually billed.
Output
Metrics
- Estimated cost
- $0.22
- Wall time
- 3m
- Human step-ins
- 0
- Turns
- 10
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| GGemini 4 Argongoogle/gemini-4-argoneffort medium · thinking on | 227,284 | 11,477 | $0.22 |
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. swing_radius 2. track_cleaning 3. distances 4. alarm_events 5. anchor_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": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "init": {"model": "gemini-4-argon-medium", "cwd": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k07/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"}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 0, "state": "DONE", "step_type": "user_input"}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 1, "state": "DONE", "step_type": "agent_response", "duration_seconds": 4.771251885, "usage": {"input_tokens": 8420, "output_tokens": 78, "thinking_tokens": 24, "cache_read_tokens": 0, "total_tokens": 8498}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "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/k07/work/input.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 2, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.6702535, "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k07/work/input.json"}, "output": "13 lines, 183 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 3, "state": "DONE", "step_type": "agent_response", "duration_seconds": 20.06273157, "usage": {"input_tokens": 2379, "output_tokens": 72, "thinking_tokens": 18, "cache_read_tokens": 6366, "total_tokens": 2451}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 4, "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/k07/work/track.csv"}}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 4, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.021944586, "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k07/work/track.csv"}, "output": "301 lines, 8517 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 5, "state": "DONE", "step_type": "agent_response", "duration_seconds": 51.748970496, "usage": {"input_tokens": 9764, "output_tokens": 4730, "thinking_tokens": 2634, "cache_read_tokens": 8746, "total_tokens": 14494}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 6, "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/k07/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 6, "state": "DONE", "step_type": "tool", "tool_name": "write_file", "duration_seconds": 1.415778426, "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k07/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 7, "state": "DONE", "step_type": "agent_response", "duration_seconds": 4.290639328, "usage": {"input_tokens": 4816, "output_tokens": 87, "thinking_tokens": 30, "cache_read_tokens": 18511, "total_tokens": 4903}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 8, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 8, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 1.679070635, "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/k07/work/solve.py\", line 188, 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/k07/work/solve.py\", line 174, 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"}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 9, "state": "DONE", "step_type": "agent_response", "duration_seconds": 19.620817826, "usage": {"input_tokens": 2444, "output_tokens": 724, "thinking_tokens": 337, "cache_read_tokens": 21278, "total_tokens": 3168}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 10, "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/k07/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 10, "state": "DONE", "step_type": "tool", "tool_name": "edit_file", "duration_seconds": 1.8088994569999999, "tool_info": {"name": "edit_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k07/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 11, "state": "DONE", "step_type": "agent_response", "duration_seconds": 4.7510815950000005, "usage": {"input_tokens": 2903, "output_tokens": 129, "thinking_tokens": 58, "cache_read_tokens": 21673, "total_tokens": 3032}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 12, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json /tmp/answer.json && cat /tmp/answer.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 12, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 2.48795553, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json /tmp/answer.json && cat /tmp/answer.json"}, "output": "Command exited with code 0.\n\nCommand output:\n{\r\n \"radius_m\": 38.15,\r\n \"valid_points\": 290,\r\n \"bad_rows\": 10,\r\n \"events\": [\r\n {\r\n \"start_t\": 822,\r\n \"trigger_t\": 852,\r\n \"end_t\": 902,\r\n \"max_dist_m\": 42.9,\r\n \"bearing_deg\": 68.2\r\n },\r\n {\r\n \"start_t\": 2640,\r\n \"trigger_t\": 2660,\r\n \"end_t\": 2967,\r\n \"max_dist_m\": 50.5,\r\n \"bearing_deg\": 314.9\r\n },\r\n {\r\n \"start_t\": 2988,\r\n \"trigger_t\": 3009,\r\n \"end_t\": 3071,\r\n \"max_dist_m\": 43.6,\r\n \"bearing_deg\": 301.8\r\n }\r\n ],\r\n \"max_dist_m\": 50.5,\r\n \"outside_points\": 49\r\n}"}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 13, "state": "DONE", "step_type": "agent_response", "duration_seconds": 6.715313526, "usage": {"input_tokens": 2556, "output_tokens": 124, "thinking_tokens": 97, "cache_read_tokens": 22527, "total_tokens": 2680}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 14, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"Path": "/tmp/anchor.svg"}}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 14, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.031164133, "tool_info": {"name": "read_file", "parameters": {"Path": "/tmp/anchor.svg"}, "output": "6 lines, 4488 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 15, "state": "DONE", "step_type": "agent_response", "duration_seconds": 9.696661607, "usage": {"input_tokens": 4484, "output_tokens": 520, "thinking_tokens": 259, "cache_read_tokens": 25084, "total_tokens": 5004}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 16, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport json, solve, csv, math\nwith open(\"input.json\") as f: d = json.load(f)\nlines = solve.load_track_lines(\"input.json\", d[\"track\"])\nr = csv.reader(lines); next(r)\nfor row in r:\n try:\n t, lat, lon = int(row[0]), float(row[1]), float(row[2])\n m = math.radians((d[\"anchor\"][0]+lat)/2)\n x = math.radians(lon-d[\"anchor\"][1])*math.cos(m)*6371000\n y = math.radians(lat-d[\"anchor\"][0])*6371000\n dist = math.hypot(x, y)\n if 37.5 <= dist <= 38.8:\n pri\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 16, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 1.7579025499999998, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport json, solve, csv, math\nwith open(\"input.json\") as f: d = json.load(f)\nlines = solve.load_track_lines(\"input.json\", d[\"track\"])\nr = csv.reader(lines); next(r)\nfor row in r:\n try:\n t, lat, lon = int(row[0]), float(row[1]), float(row[2])\n m = math.radians((d[\"anchor\"][0]+lat)/2)\n x = math.radians(lon-d[\"anchor\"][1])*math.cos(m)*6371000\n y = math.radians(lat-d[\"anchor\"][0])*6371000\n dist = math.hypot(x, y)\n if 37.5 <= dist <= 38.8:\n pri\u2026"}, "output": "Command exited with code 0.\n\nCommand output:\n2640 38.55033488623928\r\n2977 37.84966439922902\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 17, "state": "DONE", "step_type": "agent_response", "duration_seconds": 40.639332664, "usage": {"input_tokens": 2704, "output_tokens": 4727, "thinking_tokens": 52, "cache_read_tokens": 27519, "total_tokens": 7431}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 18, "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/k07/work/answer.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 18, "state": "DONE", "step_type": "tool", "tool_name": "write_file", "duration_seconds": 1.5025974720000002, "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k07/work/answer.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 19, "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/k07/work/anchor.svg"}}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 19, "state": "DONE", "step_type": "tool", "tool_name": "write_file", "duration_seconds": 2.891619599, "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k07/work/anchor.svg"}}}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 20, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "Implemented [solve.py](f"}}
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{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 20, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "[answer.json](file:///"}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 20, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "home/bob/usecase-runs/ag"}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 20, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "y-batch/out/gemini-4-arg"}}
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{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 20, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "07/work/answer.json) and"}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 20, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " [anchor.svg](file:///ho"}}
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{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 20, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "svg) for [input.json](fi"}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 20, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "le:///home/bob/usecase-r"}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 20, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "uns/agy-batch/out/gemini"}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 20, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-4-argon-medium/k07/work"}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 20, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "/input.json):\n\n- **Alarm"}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 20, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " radius**: `"}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 20, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "38.15 m"}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 20, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "`\n- **Track cleaning**: `290` valid "}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 20, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "points, `10`"}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 20, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " bad rows skipped\n- **"}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 20, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "Outside points**: `49` (`max_dist_m`: `50.5 m`)\n"}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 20, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "- **Alarm"}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 20, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " events**: 3 events (`82"}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 20, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "2\u2013902`, `2640\u20132967`,"}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 20, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " `2988"}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 20, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "\u20133071`)"}}
{"event": "step_update", "step_update": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "step_index": 20, "state": "DONE", "step_type": "agent_response", "text_delta": "\n", "duration_seconds": 6.762590027, "usage": {"input_tokens": 4886, "output_tokens": 286, "thinking_tokens": 18, "cache_read_tokens": 30224, "total_tokens": 5172}}}
{"event": "result", "result": {"conversation_id": "46ff2841-75fa-4aa5-b1ea-607020da4d03", "status": "SUCCESS", "response": "Implemented [solve.py](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k07/work/solve.py) and generated [answer.json](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k07/work/answer.json) and [anchor.svg](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k07/work/anchor.svg) for [input.json](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k07/work/input.json):\n\n- **Alarm radius**: `38.15 m`\n- **Track cleaning**: `290` valid points, `10` bad rows skipped\n- **Outside points**: `49` (`max_dist_m`: `50.5 m`)\n- **Alarm events**: 3 events (`822\u2013902`, `2640\u20132967`, `2988\u20133071`)\n", "duration_seconds": 180.912420795, "num_turns": 1, "usage": {"input_tokens": 45356, "output_tokens": 11477, "thinking_tokens": 3527, "cache_read_tokens": 181928, "total_tokens": 56833}}}