Gemini 3.8 Flash (effort medium · thinking on) on Check and pay staff mileage claims across the April tax-year change
GGemini 3.8 Flashmedium · thinking on · Antigravity CLI 1.4.6 (agy -p) · cloud · 2026-10-23 · by @bobsiopener
- Estimated cost
- $0.23
- Wall time
- 2m
- Human step-ins
- 0
- Outcome
- Pass
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Summary
Exact prompt
# Check and pay staff mileage claims across the tax-year change Inputs: `claims.csv` (claim_id, employee_id, trip_date, from, to, miles, passengers, submitted_on), `employees.json`, `rates.json` and `finance-note.txt`. Amounts are in pounds. `rates.json` and these rules are the record; where the finance note disagrees, the rules win. Rules: - A claim submitted more than late_after_days days after its trip_date is rejected as late; it is listed only in rejected_late, even if it is also a duplicate. - Two remaining claims are duplicates if they have the same employee_id, trip_date, from and to (from and to compared ignoring letter case and extra spaces); only the one submitted first is paid (on a tie, the lower claim number), and the others are duplicates. - Business miles are paid at rate_first per mile until the employee's miles in that tax year reach threshold_miles, then at rate_after; a tax year runs from 6 April to 5 April. Count paid claims in trip_date order (ties by claim number), starting from miles_before_2025_26 for the 2025-26 tax year and from 0 for 2026-27; a claim that crosses the threshold is split. - Each passenger adds passenger_per_mile for every mile of the claim; passenger miles don't count toward the threshold. - Write `out/mileage.json`: `{"paid": {"<claim_id>": <pay>, ...}, "rejected_late": [<claim_ids>], "duplicates": [<claim_ids>], "employee_totals": {"<employee_id>": <sum of pay>, ...}}`. `paid` has every paid claim; lists are sorted by claim number; `employee_totals` lists employees with at least one paid claim. ## How this run works - Work on your own: nobody will answer questions in this task, and writing to `ask/` counts against the run. - Write only under `out/`. Create `out/` if it is missing. Inputs are synthetic; don't fetch anything from the network. - Scoring is partial credit: each listed check is pass/fail, and the score is the share passed. Numbers are compared to within 0.005 unless the task says otherwise.
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).
Success criteria: late duplicates pay totals
Setup notes: batch29 case h02-mileage-expense-claims (offline fixture). Prepared with batch29/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-3.8-flash-medium --sandbox --dangerously-skip-permissions --disable-slash-commands --output-format stream-json, cwd=workdir. Scored on the box with `python3 score.py WORKDIR KEYDIR`. Single run, no retries, no human input. Autonomous case (seed 1774917927): no stand-in user; any ask/ file counts as an intervention and blocks a pass. Requested model gemini-3.8-flash-medium (effort medium); priced at the base model row gemini-3.8-flash (pricing.json, https://ai.google.dev/gemini-api/docs/pricing). Score: 4/4. Cost is an ESTIMATE: measured tokens x pricing.json list price (Google account, nothing billed per token); output_tokens include thinking tokens. Tool calls 22 (0 failed). Single run.
Estimated cost List-price estimate; not actually billed.
Metrics
- Estimated cost
- $0.23
- Wall time
- 2m
- Human step-ins
- 0
- Turns
- 20
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| GGemini 3.8 Flashgoogle/gemini-3.8-flasheffort medium · thinking on | 437,171 | 25,036 | $0.23 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: Gemini 3.8 Flash (google/gemini-3.8-flash, effort medium · thinking on) via
Antigravity CLI 1.4.6 (agy -p), cloud. - It counts as a success only if it meets every success criterion: late duplicates pay totals
- 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": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "init": {"model": "gemini-3.8-flash-medium", "cwd": "/home/bob/usecase-runs/b2930/out/agy/gemini-3.8-flash-medium/b29h02/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": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 0, "state": "DONE", "step_type": "user_input"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 1, "state": "DONE", "step_type": "agent_response", "duration_seconds": 2.380147403, "usage": {"input_tokens": 8441, "output_tokens": 615, "thinking_tokens": 578, "cache_read_tokens": 0, "total_tokens": 9056}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 2, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "ls -la"}}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 2, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.254919468, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "ls -la"}, "output": "Command exited with code 0.\n\nCommand output:\ntotal 36\r\ndrwxr-xr-x 4 bob bob 4096 Oct 23 05:55 .\r\ndrwxrwxr-x 3 bob bob 4096 Oct 23 05:57 ..\r\n-rw-r--r-- 1 bob bob 1984 Oct 23 05:55 TASK.md\r\ndrwxr-xr-x 2 bob bob 4096 Oct 23 05:55 ask\r\n-rw-r--r-- 1 bob bob 1780 Oct 23 05:55 claims.csv\r\n-rw-r--r-- 1 bob bob 276 Oct 23 05:55 employees.json\r\n-rw-r--r-- 1 bob bob 202 Oct 23 05:55 finance-note.txt\r\ndrwxr-xr-x 2 bob bob 4096 Oct 23 05:55 out\r\n-rw-r--r-- 1 bob bob 130 Oct 23 05:55 rates.json\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 3, "state": "DONE", "step_type": "agent_response", "duration_seconds": 1.518741415, "usage": {"input_tokens": 9402, "output_tokens": 260, "thinking_tokens": 26, "cache_read_tokens": 0, "total_tokens": 9662}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 4, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/b2930/out/agy/gemini-3.8-flash-medium/b29h02/work/TASK.md"}}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 4, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.22325661, "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/b2930/out/agy/gemini-3.8-flash-medium/b29h02/work/TASK.md"}, "output": "15 lines, 1984 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 5, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/b2930/out/agy/gemini-3.8-flash-medium/b29h02/work/employees.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 5, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.108020016, "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/b2930/out/agy/gemini-3.8-flash-medium/b29h02/work/employees.json"}, "output": "18 lines, 276 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 6, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/b2930/out/agy/gemini-3.8-flash-medium/b29h02/work/rates.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 6, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.039185167, "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/b2930/out/agy/gemini-3.8-flash-medium/b29h02/work/rates.json"}, "output": "7 lines, 130 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 7, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/b2930/out/agy/gemini-3.8-flash-medium/b29h02/work/finance-note.txt"}}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 7, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.031094263, "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/b2930/out/agy/gemini-3.8-flash-medium/b29h02/work/finance-note.txt"}, "output": "2 lines, 202 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 8, "state": "DONE", "step_type": "agent_response", "duration_seconds": 1.5521577789999998, "usage": {"input_tokens": 10934, "output_tokens": 297, "thinking_tokens": 239, "cache_read_tokens": 0, "total_tokens": 11231}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 9, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/b2930/out/agy/gemini-3.8-flash-medium/b29h02/work/claims.csv"}}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 9, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.041884076, "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/b2930/out/agy/gemini-3.8-flash-medium/b29h02/work/claims.csv"}, "output": "25 lines, 1780 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 10, "state": "DONE", "step_type": "agent_response", "duration_seconds": 5.730347489, "usage": {"input_tokens": 12508, "output_tokens": 1522, "thinking_tokens": 1477, "cache_read_tokens": 0, "total_tokens": 14030}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 11, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "git status; git log -n 5"}}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 11, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.257137143, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "git status; git log -n 5"}, "output": "Command exited with code 128.\n\nCommand output:\nfatal: not a git repository (or any parent up to mount point /)\r\nStopping at filesystem boundary (GIT_DISCOVERY_ACROSS_FILESYSTEM not set).\r\nfatal: not a git repository (or any parent up to mount point /)\r\nStopping at filesystem boundary (GIT_DISCOVERY_ACROSS_FILESYSTEM not set).\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 12, "state": "DONE", "step_type": "agent_response", "duration_seconds": 1.348531679, "usage": {"input_tokens": 14191, "output_tokens": 95, "thinking_tokens": 57, "cache_read_tokens": 0, "total_tokens": 14286}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 13, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "ls -la .."}}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 13, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.254373443, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "ls -la .."}, "output": "Command exited with code 0.\n\nCommand output:\ntotal 28\r\ndrwxrwxr-x 3 bob bob 4096 Oct 23 05:57 .\r\ndrwxrwxr-x 4 bob bob 4096 Oct 23 05:57 ..\r\n-rw-rw-r-- 1 bob bob 8713 Oct 23 05:57 events.jsonl\r\n-rw-rw-r-- 1 bob bob 103 Oct 23 05:57 meta.txt\r\n-rw-rw-r-- 1 bob bob 0 Oct 23 05:57 stderr.txt\r\ndrwxr-xr-x 4 bob bob 4096 Oct 23 05:55 work\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 14, "state": "DONE", "step_type": "agent_response", "duration_seconds": 1.7655182520000001, "usage": {"input_tokens": 14542, "output_tokens": 66, "thinking_tokens": 10, "cache_read_tokens": 0, "total_tokens": 14608}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 15, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/b2930/out/agy/gemini-3.8-flash-medium/b29h02/meta.txt"}}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 15, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.047124958, "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/b2930/out/agy/gemini-3.8-flash-medium/b29h02/meta.txt"}, "output": "3 lines, 103 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 16, "state": "DONE", "step_type": "agent_response", "duration_seconds": 1.735829926, "usage": {"input_tokens": 14769, "output_tokens": 50, "thinking_tokens": 9, "cache_read_tokens": 0, "total_tokens": 14819}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 17, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "ls -la ask out"}}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 17, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.29393888, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "ls -la ask out"}, "output": "Command exited with code 0.\n\nCommand output:\nask:\r\ntotal 8\r\ndrwxr-xr-x 2 bob bob 4096 Oct 23 05:55 .\r\ndrwxr-xr-x 4 bob bob 4096 Oct 23 05:55 ..\r\n\r\nout:\r\ntotal 8\r\ndrwxr-xr-x 2 bob bob 4096 Oct 23 05:55 .\r\ndrwxr-xr-x 4 bob bob 4096 Oct 23 05:55 ..\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 18, "state": "DONE", "step_type": "agent_response", "duration_seconds": 7.468914277, "usage": {"input_tokens": 15026, "output_tokens": 1025, "thinking_tokens": 928, "cache_read_tokens": 0, "total_tokens": 16051}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 19, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json\n\nwith open('claims.csv') as f:\n claims = list(csv.DictReader(f))\n\nprint(f'Total claims: {len(claims)}')\nfor c in claims:\n print(c)\n\""}}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 19, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.279097557, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json\n\nwith open('claims.csv') as f:\n claims = list(csv.DictReader(f))\n\nprint(f'Total claims: {len(claims)}')\nfor c in claims:\n print(c)\n\""}, "output": "Command exited with code 0.\n\nCommand output:\nTotal claims: 24\r\n{'claim_id': 'MC-103', 'employee_id': 'EM-36', 'trip_date': '2026-04-07', 'from': 'Leeds Office', 'to': 'Harrogate Clinic', 'miles': '146', 'passengers': '1', 'submitted_on': '2026-04-09'}\r\n{'claim_id': 'MC-113', 'employee_id': 'EM-41', 'trip_date': '2026-03-28', 'from': 'York Depot', 'to': 'Hull Warehouse', 'miles': '139', 'passengers': '2', 'submitted_on': '2026-06-06'}\r\n{'claim_id': 'MC-97', 'employee_id': 'EM-32', 'trip_date': '2026-04-02', 'from': 'Wakefield Site', 'to': 'York Depot', 'miles': '47', 'passengers': '2', 'submitted_on': '2026-06-01'}\r\n{'claim_id': 'MC-118', 'employee_id': 'EM-41', 'trip_date': '2026-04-11', 'from': 'Bradford Store', 'to': 'Wakefield Site', 'miles': '166', 'passengers': '2', 'submitted_on': '2026-05-01'}\r\n{'claim_id': 'MC-101', 'employee_id': 'EM-32', 'trip_date': '2026-04-20', 'from': 'Harrogate Clinic', 'to': 'Wakefield Site', 'miles': '105', 'passengers': '0', 'submitted_on': '2026-04-29'}\r\n{'claim_id': 'MC-112', 'employee_id': 'EM-36', 'trip_date': '2026-03-24', 'from': 'Bradford Store', 'to': 'Leeds Office', 'miles': '315', 'passengers': '0', 'submitted_on': '2026-05-23'}\r\n{'claim_id': 'MC-131', 'employee_id': 'EM-36', 'trip_date': '2026-04-05', 'from': 'Harrogate Clinic', 'to': 'Hull Warehouse', 'miles': '100', 'passengers': '0', 'submitted_on': '2026-04-08'}\r\n{'claim_id': 'MC-100', 'employee_id': 'EM-32', 'trip_date': '2026-04-05', 'from': 'Harrogate Clinic', 'to': 'Leeds Office', 'miles': '148', 'passengers': '2', 'submitted_on': '2026-04-10'}\r\n{'claim_id': 'MC-105', 'employee_id': 'EM-36', 'trip_date': '2026-04-07', 'from': 'Harrogate Clinic', 'to': 'York Depot', 'miles': '143', 'passengers': '2', 'submitted_on': '2026-06-06'}\r\n{'claim_id': 'MC-121', 'employee_id': 'EM-42', 'trip_date': '2026-04-04', 'from': 'Bradford Store', 'to': 'York Depot', 'miles': '148', 'passengers': '2', 'submitted_on': '2026-04-24'}\r\n{'claim_id': 'MC-132', 'employee_id': 'EM-32', 'trip_date': '2026-04-06', 'from': 'Bradford Store', 'to': 'Leeds Office', 'miles': '198', 'passengers': '0', 'submitted_on': '2026-04-09'}\r\n{'claim_id': 'MC-114', 'employee_id': 'EM-41', 'trip_date': '2026-03-26', 'from': 'Wakefield Site', 'to': 'York Depot', 'miles': '171', 'passengers': '1', 'submitted_on': '2026-05-25'}\r\n{'claim_id': 'MC-120', 'employee_id': 'EM-42', 'trip_date': '2026-04-22', 'from': 'Bradford Store', 'to': 'Wakefield Site', 'miles': '169', 'passengers': '2', 'submitted_on': '2026-04-27'}\r\n{'claim_id': 'MC-94', 'employee_id': 'EM-32', 'trip_date': '2026-04-01', 'from': 'Bradford Store', 'to': 'Hull Warehouse', 'miles': '195', 'passengers': '0', 'submitted_on': '2026-05-29'}\r\n{'claim_id': 'MC-117', 'employee_id': 'EM-41', 'trip_date': '2026-03-23', 'from': 'Bradford Store', 'to': 'Leeds Office', 'miles': '135', 'passengers': '0', 'submitted_on': '2026-04-01'}\r\n{'claim_id': 'MC-110', 'employee_id': 'EM-36', 'trip_date': '2026-04-10', 'from': 'Harrogate Clinic', 'to': 'Bradford Store', 'miles': '154', 'passengers': '0', 'submitted_on': '2026-06-07'}\r\n{'claim_id': 'MC-96', 'employee_id': 'EM-32', 'trip_date': '2026-04-06', 'from': 'Harrogate Clinic', 'to': 'Bradford Store', 'miles': '284', 'passengers': '1', 'submitted_on': '2026-05-09'}\r\n{'claim_id': 'MC-107', 'employee_id': 'EM-36', 'trip_date': '2026-04-04', 'from': 'Hull Warehouse', 'to': 'Harrogate Clinic', 'miles': '108', 'passengers': '0', 'submitted_on': '2026-06-01'}\r\n{'claim_id': 'MC-128', 'employee_id': 'EM-42', 'trip_date': '2026-03-30', 'from': 'Wakefield Site', 'to': 'Hull Warehouse', 'miles': '96', 'passengers': '2', 'submitted_on': '2026-06-08'}\r\n{'claim_id': 'MC-106', 'employee_id': 'EM-36', 'trip_date': '2026-04-03', 'from': 'Wakefield Site', 'to': 'Harrogate Clinic', 'miles': '239', 'passengers': '2', 'submitted_on': '2026-04-08'}\r\n{'claim_id': 'MC-135', 'employee_id': 'EM-32', 'trip_date': '2026-04-05', 'from': 'harrogate clinic', 'to': 'Leeds Office', 'miles': '148', 'passengers': '2', 'submitted_on': '2026-04-11'}\r\n{'claim_id': 'MC-127', 'employee_id': 'EM-42', 'trip_date': '2026-04-08', 'from': 'Harrogate Clinic', 'to': 'Hull Warehouse', 'miles': '92', 'passengers': '0', 'submitted_on': '2026-04-10'}\r\n{'claim_id': 'MC-124', 'employee_id': 'EM-42', 'trip_date': '2026-03-31', 'from': 'Leeds Office', 'to': 'Hull Warehouse', 'miles': '42', 'passengers': '2', 'submitted_on': '2026-05-28'}\r\n{'claim_id': 'MC-134', 'employee_id': 'EM-32', 'trip_date': '2026-04-01', 'from': 'Bradford Store', 'to': 'Hull Warehouse', 'miles': '195', 'passengers': '0', 'submitted_on': '2026-05-29'}\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 20, "state": "DONE", "step_type": "agent_response", "duration_seconds": 1.96410953, "usage": {"input_tokens": 6091, "output_tokens": 240, "thinking_tokens": 30, "cache_read_tokens": 12162, "total_tokens": 6331}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 21, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json, datetime\n\nwith open('claims.csv') as f:\n claims = list(csv.DictReader(f))\n\nwith open('rates.json') as f:\n rates = json.load(f)\n\nlate_after_days = rates['late_after_days']\n\nfor c in claims:\n trip = datetime.date.fromisoformat(c['trip_date'])\n sub = datetime.date.fromisoformat(c['submitted_on'])\n days = (sub - trip).days\n is_late = days > late_after_days\n print(f\\\"{c['claim_id']}: trip {trip}, sub {sub}, diff {days} days, late={is_late}\\\")\n\""}}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 21, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.273783587, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json, datetime\n\nwith open('claims.csv') as f:\n claims = list(csv.DictReader(f))\n\nwith open('rates.json') as f:\n rates = json.load(f)\n\nlate_after_days = rates['late_after_days']\n\nfor c in claims:\n trip = datetime.date.fromisoformat(c['trip_date'])\n sub = datetime.date.fromisoformat(c['submitted_on'])\n days = (sub - trip).days\n is_late = days > late_after_days\n print(f\\\"{c['claim_id']}: trip {trip}, sub {sub}, diff {days} days, late={is_late}\\\")\n\""}, "output": "Command exited with code 0.\n\nCommand output:\nMC-103: trip 2026-04-07, sub 2026-04-09, diff 2 days, late=False\r\nMC-113: trip 2026-03-28, sub 2026-06-06, diff 70 days, late=True\r\nMC-97: trip 2026-04-02, sub 2026-06-01, diff 60 days, late=False\r\nMC-118: trip 2026-04-11, sub 2026-05-01, diff 20 days, late=False\r\nMC-101: trip 2026-04-20, sub 2026-04-29, diff 9 days, late=False\r\nMC-112: trip 2026-03-24, sub 2026-05-23, diff 60 days, late=False\r\nMC-131: trip 2026-04-05, sub 2026-04-08, diff 3 days, late=False\r\nMC-100: trip 2026-04-05, sub 2026-04-10, diff 5 days, late=False\r\nMC-105: trip 2026-04-07, sub 2026-06-06, diff 60 days, late=False\r\nMC-121: trip 2026-04-04, sub 2026-04-24, diff 20 days, late=False\r\nMC-132: trip 2026-04-06, sub 2026-04-09, diff 3 days, late=False\r\nMC-114: trip 2026-03-26, sub 2026-05-25, diff 60 days, late=False\r\nMC-120: trip 2026-04-22, sub 2026-04-27, diff 5 days, late=False\r\nMC-94: trip 2026-04-01, sub 2026-05-29, diff 58 days, late=False\r\nMC-117: trip 2026-03-23, sub 2026-04-01, diff 9 days, late=False\r\nMC-110: trip 2026-04-10, sub 2026-06-07, diff 58 days, late=False\r\nMC-96: trip 2026-04-06, sub 2026-05-09, diff 33 days, late=False\r\nMC-107: trip 2026-04-04, sub 2026-06-01, diff 58 days, late=False\r\nMC-128: trip 2026-03-30, sub 2026-06-08, diff 70 days, late=True\r\nMC-106: trip 2026-04-03, sub 2026-04-08, diff 5 days, late=False\r\nMC-135: trip 2026-04-05, sub 2026-04-11, diff 6 days, late=False\r\nMC-127: trip 2026-04-08, sub 2026-04-10, diff 2 days, late=False\r\nMC-124: trip 2026-03-31, sub 2026-05-28, diff 58 days, late=False\r\nMC-134: trip 2026-04-01, sub 2026-05-29, diff 58 days, late=False\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 22, "state": "DONE", "step_type": "agent_response", "duration_seconds": 5.488028249, "usage": {"input_tokens": 3354, "output_tokens": 840, "thinking_tokens": 320, "cache_read_tokens": 16219, "total_tokens": 4194}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 23, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json, datetime, re\nfrom collections import defaultdict\n\nwith open('claims.csv') as f:\n claims = list(csv.DictReader(f))\n\nwith open('rates.json') as f:\n rates = json.load(f)\n\nlate_after_days = rates['late_after_days']\nrejected_late = []\nremaining = []\n\ndef parse_num(cid):\n m = re.search(r'\\d+', cid)\n return int(m.group()) if m else 0\n\nfor c in claims:\n trip = datetime.date.fromisoformat(c['trip_date'])\n sub = datetime.date.fromisoformat(c['submitted_on'])\n if (su\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 23, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.284612702, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json, datetime, re\nfrom collections import defaultdict\n\nwith open('claims.csv') as f:\n claims = list(csv.DictReader(f))\n\nwith open('rates.json') as f:\n rates = json.load(f)\n\nlate_after_days = rates['late_after_days']\nrejected_late = []\nremaining = []\n\ndef parse_num(cid):\n m = re.search(r'\\d+', cid)\n return int(m.group()) if m else 0\n\nfor c in claims:\n trip = datetime.date.fromisoformat(c['trip_date'])\n sub = datetime.date.fromisoformat(c['submitted_on'])\n if (su\u2026"}, "output": "Command exited with code 0.\n\nCommand output:\nDuplicate group for ('EM-32', '2026-04-05', 'harrogate clinic', 'leeds office'):\r\n MC-100: submitted 2026-04-10\r\n MC-135: submitted 2026-04-11\r\n Selected to pay: MC-100\r\nDuplicate group for ('EM-32', '2026-04-01', 'bradford store', 'hull warehouse'):\r\n MC-94: submitted 2026-05-29\r\n MC-134: submitted 2026-05-29\r\n Selected to pay: MC-94\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 24, "state": "DONE", "step_type": "agent_response", "duration_seconds": 5.34428735, "usage": {"input_tokens": 4441, "output_tokens": 689, "thinking_tokens": 516, "cache_read_tokens": 16220, "total_tokens": 5130}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 25, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv\nwith open('claims.csv') as f:\n claims = list(csv.DictReader(f))\n\nfor c in claims:\n if c['claim_id'] in ('MC-113', 'MC-128'):\n print('Late claim:', c)\n for other in claims:\n if other['claim_id'] != c['claim_id'] and other['employee_id'] == c['employee_id'] and other['trip_date'] == c['trip_date']:\n print(' Potential match:', other)\n\""}}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 25, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.301424706, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv\nwith open('claims.csv') as f:\n claims = list(csv.DictReader(f))\n\nfor c in claims:\n if c['claim_id'] in ('MC-113', 'MC-128'):\n print('Late claim:', c)\n for other in claims:\n if other['claim_id'] != c['claim_id'] and other['employee_id'] == c['employee_id'] and other['trip_date'] == c['trip_date']:\n print(' Potential match:', other)\n\""}, "output": "Command exited with code 0.\n\nCommand output:\nLate claim: {'claim_id': 'MC-113', 'employee_id': 'EM-41', 'trip_date': '2026-03-28', 'from': 'York Depot', 'to': 'Hull Warehouse', 'miles': '139', 'passengers': '2', 'submitted_on': '2026-06-06'}\r\nLate claim: {'claim_id': 'MC-128', 'employee_id': 'EM-42', 'trip_date': '2026-03-30', 'from': 'Wakefield Site', 'to': 'Hull Warehouse', 'miles': '96', 'passengers': '2', 'submitted_on': '2026-06-08'}\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 26, "state": "DONE", "step_type": "agent_response", "duration_seconds": 8.500019293, "usage": {"input_tokens": 5395, "output_tokens": 3448, "thinking_tokens": 2876, "cache_read_tokens": 16220, "total_tokens": 8843}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 27, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json, datetime, re\nfrom collections import defaultdict\nfrom decimal import Decimal\n\nwith open('claims.csv') as f:\n claims = list(csv.DictReader(f))\n\nwith open('employees.json') as f:\n employees = json.load(f)\n\nwith open('rates.json') as f:\n rates = json.load(f)\n\nrate_first = Decimal(rates['rate_first'])\nrate_after = Decimal(rates['rate_after'])\npassenger_per_mile = Decimal(rates['passenger_per_mile'])\nthreshold_miles = int(rates['threshold_miles'])\nlate_after_days = int(rat\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 27, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.290443323, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json, datetime, re\nfrom collections import defaultdict\nfrom decimal import Decimal\n\nwith open('claims.csv') as f:\n claims = list(csv.DictReader(f))\n\nwith open('employees.json') as f:\n employees = json.load(f)\n\nwith open('rates.json') as f:\n rates = json.load(f)\n\nrate_first = Decimal(rates['rate_first'])\nrate_after = Decimal(rates['rate_after'])\npassenger_per_mile = Decimal(rates['passenger_per_mile'])\nthreshold_miles = int(rates['threshold_miles'])\nlate_after_days = int(rat\u2026"}, "output": "Command exited with code 0.\n\nCommand output:\nRejected late: ['MC-113', 'MC-128']\r\nDuplicates: ['MC-135', 'MC-134']\r\nCount paid candidates: 20\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 28, "state": "DONE", "step_type": "agent_response", "duration_seconds": 3.196758641, "usage": {"input_tokens": 4895, "output_tokens": 853, "thinking_tokens": 578, "cache_read_tokens": 20296, "total_tokens": 5748}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 29, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json, datetime, re\nfrom collections import defaultdict\n\nwith open('claims.csv') as f:\n claims = list(csv.DictReader(f))\n\n# Let's filter out MC-113, MC-128, MC-135, MC-134\npaid_candidates = [c for c in claims if c['claim_id'] not in ('MC-113', 'MC-128', 'MC-135', 'MC-134')]\n\nemp_claims = defaultdict(list)\nfor c in paid_candidates:\n emp_claims[c['employee_id']].append(c)\n\nfor emp, clist in emp_claims.items():\n dates = [c['trip_date'] for c in clist]\n if len(dates) != len(s\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 29, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.306641741, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json, datetime, re\nfrom collections import defaultdict\n\nwith open('claims.csv') as f:\n claims = list(csv.DictReader(f))\n\n# Let's filter out MC-113, MC-128, MC-135, MC-134\npaid_candidates = [c for c in claims if c['claim_id'] not in ('MC-113', 'MC-128', 'MC-135', 'MC-134')]\n\nemp_claims = defaultdict(list)\nfor c in paid_candidates:\n emp_claims[c['employee_id']].append(c)\n\nfor emp, clist in emp_claims.items():\n dates = [c['trip_date'] for c in clist]\n if len(dates) != len(s\u2026"}, "output": "Command exited with code 0.\n\nCommand output:\nEmployee EM-36 has duplicate dates: ['2026-04-07', '2026-03-24', '2026-04-05', '2026-04-07', '2026-04-10', '2026-04-04', '2026-04-03']\r\nEmployee EM-32 has duplicate dates: ['2026-04-02', '2026-04-20', '2026-04-05', '2026-04-06', '2026-04-01', '2026-04-06']\r\nEmployee EM-41 has all unique trip dates.\r\nEmployee EM-42 has all unique trip dates.\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 30, "state": "DONE", "step_type": "agent_response", "duration_seconds": 2.333866068, "usage": {"input_tokens": 6033, "output_tokens": 623, "thinking_tokens": 443, "cache_read_tokens": 20296, "total_tokens": 6656}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 31, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv\n\nwith open('claims.csv') as f:\n claims = list(csv.DictReader(f))\n\nfor c in claims:\n if c['employee_id'] == 'EM-32' and c['trip_date'] >= '2026-04-06' and c['claim_id'] not in ('MC-113', 'MC-128', 'MC-135', 'MC-134'):\n print(c['claim_id'], c['trip_date'], c['miles'])\n\""}}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 31, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.284212407, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv\n\nwith open('claims.csv') as f:\n claims = list(csv.DictReader(f))\n\nfor c in claims:\n if c['employee_id'] == 'EM-32' and c['trip_date'] >= '2026-04-06' and c['claim_id'] not in ('MC-113', 'MC-128', 'MC-135', 'MC-134'):\n print(c['claim_id'], c['trip_date'], c['miles'])\n\""}, "output": "Command exited with code 0.\n\nCommand output:\nMC-101 2026-04-20 105\r\nMC-132 2026-04-06 198\r\nMC-96 2026-04-06 284\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 32, "state": "DONE", "step_type": "agent_response", "duration_seconds": 3.07483164, "usage": {"input_tokens": 2746, "output_tokens": 870, "thinking_tokens": 217, "cache_read_tokens": 24353, "total_tokens": 3616}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 33, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json, datetime, re\nfrom collections import defaultdict\nfrom decimal import Decimal\n\nwith open('claims.csv') as f:\n claims = list(csv.DictReader(f))\n\nwith open('employees.json') as f:\n employees = json.load(f)\n\nwith open('rates.json') as f:\n rates = json.load(f)\n\n# Exclude late and duplicates\nvalid_claims = [c for c in claims if c['claim_id'] not in ('MC-113', 'MC-128', 'MC-135', 'MC-134')]\n\nfor emp_id, emp_info in employees.items():\n print(f'=== Employee {emp_id} ({emp_i\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 33, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.294916809, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json, datetime, re\nfrom collections import defaultdict\nfrom decimal import Decimal\n\nwith open('claims.csv') as f:\n claims = list(csv.DictReader(f))\n\nwith open('employees.json') as f:\n employees = json.load(f)\n\nwith open('rates.json') as f:\n rates = json.load(f)\n\n# Exclude late and duplicates\nvalid_claims = [c for c in claims if c['claim_id'] not in ('MC-113', 'MC-128', 'MC-135', 'MC-134')]\n\nfor emp_id, emp_info in employees.items():\n print(f'=== Employee {emp_id} ({emp_i\u2026"}, "output": "Command exited with code 0.\n\nCommand output:\n=== Employee EM-32 (B. Ruiz) ===\r\n Tax Year 2025-26 (starting 9658):\r\n MC-94: date 2026-04-01, miles 195, cum 9658 -> 9853\r\n MC-97: date 2026-04-02, miles 47, cum 9853 -> 9900\r\n MC-100: date 2026-04-05, miles 148, cum 9900 -> 10048\r\n Tax Year 2026-27 (starting 0):\r\n MC-96: date 2026-04-06, miles 284, cum 0 -> 284\r\n MC-132: date 2026-04-06, miles 198, cum 284 -> 482\r\n MC-101: date 2026-04-20, miles 105, cum 482 -> 587\r\n=== Employee EM-36 (C. Okoro) ===\r\n Tax Year 2025-26 (starting 4362):\r\n MC-112: date 2026-03-24, miles 315, cum 4362 -> 4677\r\n MC-106: date 2026-04-03, miles 239, cum 4677 -> 4916\r\n MC-107: date 2026-04-04, miles 108, cum 4916 -> 5024\r\n MC-131: date 2026-04-05, miles 100, cum 5024 -> 5124\r\n Tax Year 2026-27 (starting 0):\r\n MC-103: date 2026-04-07, miles 146, cum 0 -> 146\r\n MC-105: date 2026-04-07, miles 143, cum 146 -> 289\r\n MC-110: date 2026-04-10, miles 154, cum 289 -> 443\r\n=== Employee EM-41 (A. Hale) ===\r\n Tax Year 2025-26 (starting 9733):\r\n MC-117: date 2026-03-23, miles 135, cum 9733 -> 9868\r\n MC-114: date 2026-03-26, miles 171, cum 9868 -> 10039\r\n Tax Year 2026-27 (starting 0):\r\n MC-118: date 2026-04-11, miles 166, cum 0 -> 166\r\n=== Employee EM-42 (G. Byrne) ===\r\n Tax Year 2025-26 (starting 9653):\r\n MC-124: date 2026-03-31, miles 42, cum 9653 -> 9695\r\n MC-121: date 2026-04-04, miles 148, cum 9695 -> 9843\r\n Tax Year 2026-27 (starting 0):\r\n MC-127: date 2026-04-08, miles 92, cum 0 -> 92\r\n MC-120: date 2026-04-22, miles 169, cum 92 -> 261\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 34, "state": "DONE", "step_type": "agent_response", "duration_seconds": 8.074843748, "usage": {"input_tokens": 4636, "output_tokens": 2772, "thinking_tokens": 1337, "cache_read_tokens": 24359, "total_tokens": 7408}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 35, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json, datetime, re\nfrom collections import defaultdict\nfrom decimal import Decimal\n\nwith open('claims.csv') as f:\n claims = list(csv.DictReader(f))\n\nwith open('employees.json') as f:\n employees = json.load(f)\n\nwith open('rates.json') as f:\n rates = json.load(f)\n\nrate_first = Decimal(rates['rate_first'])\nrate_after = Decimal(rates['rate_after'])\npassenger_per_mile = Decimal(rates['passenger_per_mile'])\nthreshold_miles = Decimal(rates['threshold_miles'])\nlate_after_days = int\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 35, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.292857654, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json, datetime, re\nfrom collections import defaultdict\nfrom decimal import Decimal\n\nwith open('claims.csv') as f:\n claims = list(csv.DictReader(f))\n\nwith open('employees.json') as f:\n employees = json.load(f)\n\nwith open('rates.json') as f:\n rates = json.load(f)\n\nrate_first = Decimal(rates['rate_first'])\nrate_after = Decimal(rates['rate_after'])\npassenger_per_mile = Decimal(rates['passenger_per_mile'])\nthreshold_miles = Decimal(rates['threshold_miles'])\nlate_after_days = int\u2026"}, "output": "Command exited with code 0.\n\nCommand output:\nMC-94 (Emp EM-32, 2025-26, 195 miles, 0 pass): 195*0.45 + 0*0.25 + 0*0.05*195 = 87.75\r\nMC-97 (Emp EM-32, 2025-26, 47 miles, 2 pass): 47*0.45 + 0*0.25 + 2*0.05*47 = 25.85\r\nMC-100 (Emp EM-32, 2025-26, 148 miles, 2 pass): 100*0.45 + 48*0.25 + 2*0.05*148 = 71.80\r\nMC-96 (Emp EM-32, 2026-27, 284 miles, 1 pass): 284*0.45 + 0*0.25 + 1*0.05*284 = 142.00\r\nMC-132 (Emp EM-32, 2026-27, 198 miles, 0 pass): 198*0.45 + 0*0.25 + 0*0.05*198 = 89.10\r\nMC-101 (Emp EM-32, 2026-27, 105 miles, 0 pass): 105*0.45 + 0*0.25 + 0*0.05*105 = 47.25\r\nMC-112 (Emp EM-36, 2025-26, 315 miles, 0 pass): 315*0.45 + 0*0.25 + 0*0.05*315 = 141.75\r\nMC-106 (Emp EM-36, 2025-26, 239 miles, 2 pass): 239*0.45 + 0*0.25 + 2*0.05*239 = 131.45\r\nMC-107 (Emp EM-36, 2025-26, 108 miles, 0 pass): 108*0.45 + 0*0.25 + 0*0.05*108 = 48.60\r\nMC-131 (Emp EM-36, 2025-26, 100 miles, 0 pass): 100*0.45 + 0*0.25 + 0*0.05*100 = 45.00\r\nMC-103 (Emp EM-36, 2026-27, 146 miles, 1 pass): 146*0.45 + 0*0.25 + 1*0.05*146 = 73.00\r\nMC-105 (Emp EM-36, 2026-27, 143 miles, 2 pass): 143*0.45 + 0*0.25 + 2*0.05*143 = 78.65\r\nMC-110 (Emp EM-36, 2026-27, 154 miles, 0 pass): 154*0.45 + 0*0.25 + 0*0.05*154 = 69.30\r\nMC-117 (Emp EM-41, 2025-26, 135 miles, 0 pass): 135*0.45 + 0*0.25 + 0*0.05*135 = 60.75\r\nMC-114 (Emp EM-41, 2025-26, 171 miles, 1 pass): 132*0.45 + 39*0.25 + 1*0.05*171 = 77.70\r\nMC-118 (Emp EM-41, 2026-27, 166 miles, 2 pass): 166*0.45 + 0*0.25 + 2*0.05*166 = 91.30\r\nMC-124 (Emp EM-42, 2025-26, 42 miles, 2 pass): 42*0.45 + 0*0.25 + 2*0.05*42 = 23.10\r\nMC-121 (Emp EM-42, 2025-26, 148 miles, 2 pass): 148*0.45 + 0*0.25 + 2*0.05*148 = 81.40\r\nMC-127 (Emp EM-42, 2026-27, 92 miles, 0 pass): 92*0.45 + 0*0.25 + 0*0.05*92 = 41.40\r\nMC-120 (Emp EM-42, 2026-27, 169 miles, 2 pass): 169*0.45 + 0*0.25 + 2*0.05*169 = 92.95\r\n\r\nEmployee totals:\r\nEM-32: 463.75\r\nEM-36: 587.75\r\nEM-41: 229.75\r\nEM-42: 238.85\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 36, "state": "DONE", "step_type": "agent_response", "duration_seconds": 10.534221514, "usage": {"input_tokens": 4819, "output_tokens": 4536, "thinking_tokens": 2972, "cache_read_tokens": 28442, "total_tokens": 9355}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 37, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json, datetime, re\nfrom collections import defaultdict\nfrom decimal import Decimal\n\n# Read inputs\nwith open('claims.csv') as f:\n claims = list(csv.DictReader(f))\n\nwith open('employees.json') as f:\n employees = json.load(f)\n\nwith open('rates.json') as f:\n rates = json.load(f)\n\nrate_first = Decimal(str(rates['rate_first']))\nrate_after = Decimal(str(rates['rate_after']))\npassenger_per_mile = Decimal(str(rates['passenger_per_mile']))\nthreshold_miles = Decimal(str(rates['thresho\u2026"}}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 37, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.394503538, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c \"\nimport csv, json, datetime, re\nfrom collections import defaultdict\nfrom decimal import Decimal\n\n# Read inputs\nwith open('claims.csv') as f:\n claims = list(csv.DictReader(f))\n\nwith open('employees.json') as f:\n employees = json.load(f)\n\nwith open('rates.json') as f:\n rates = json.load(f)\n\nrate_first = Decimal(str(rates['rate_first']))\nrate_after = Decimal(str(rates['rate_after']))\npassenger_per_mile = Decimal(str(rates['passenger_per_mile']))\nthreshold_miles = Decimal(str(rates['thresho\u2026"}, "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 \"<string>\", line 140, in <module>\r\n with open('out/mileage.json', 'w') as f:\r\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^\r\nOSError: [Errno 30] Read-only file system: 'out/mileage.json'\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 38, "state": "DONE", "step_type": "agent_response", "duration_seconds": 2.537352641, "usage": {"input_tokens": 5480, "output_tokens": 736, "thinking_tokens": 220, "cache_read_tokens": 32530, "total_tokens": 6216}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 39, "state": "ACTIVE", "step_type": "tool", "tool_name": "write_file", "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/b2930/out/agy/gemini-3.8-flash-medium/b29h02/work/out/mileage.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 39, "state": "DONE", "step_type": "tool", "tool_name": "write_file", "duration_seconds": 0.044412552, "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/b2930/out/agy/gemini-3.8-flash-medium/b29h02/work/out/mileage.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 40, "state": "DONE", "step_type": "agent_response", "duration_seconds": 1.939760519, "usage": {"input_tokens": 2230, "output_tokens": 88, "thinking_tokens": 27, "cache_read_tokens": 36592, "total_tokens": 2318}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 41, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/b2930/out/agy/gemini-3.8-flash-medium/b29h02/work/out/mileage.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 41, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.042354353, "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/b2930/out/agy/gemini-3.8-flash-medium/b29h02/work/out/mileage.json"}, "output": "38 lines, 641 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "I have processed"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " all mileage claims according to the"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " rules and tax-year requ"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "irements, and written the result to "}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "[`out/mileage.json`](fil"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "e:///home/bo"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "b/usecase-runs/b2930/out"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "/agy/gemini-3.8-flash-medium/b29h02/work/out/mil"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "eage.json).\n\n### Summary of Results\n"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "\n1. **Rejected Late Clai"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ms (`> 60` d"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ays after `trip_date`)**:\n - `MC-113` (EM-41, trip 2026-03-28, submitt"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ed 2026-06-06 \u2014 70 day"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "s)\n - `MC-"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "128` (EM-42, trip 2026-03-30, submitted 2026-06-"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "0"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "8 \u2014 70 days)\n\n2. **Duplicate Claim"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "s**:\n - `M"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "C-134` (duplicate of `MC-94` for EM-32 on 2026-0"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "4-01;"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " submitted same day, tie broken by l"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ower claim number)\n - "}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "`MC-135` (duplicate of `MC-100` for "}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "EM-32 on 2026-04-05; sub"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "mitted"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " later on 2026-04-11 vs 2026-04-10)\n"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "\n3. **"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "Tax-Year Mileage & Thresholds**:\n "}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "- **2025-26 Tax Year** (trips on/before 2026-04-"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "05) initiali"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "zed with each employee's `miles_before_2025_26`."}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " Both `MC-100` (EM-32) a"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "nd `MC-114` (EM-41) crossed the 10,0"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "00-mile threshold"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " and were split between "}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "`rate_first` (\u00a30.45) an"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "d `rate_after` (\u00a30.25"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": ").\n - **2026-27 Tax Ye"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ar** (trips on/after 20"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "26-04-06) reset mileage to 0. All bu"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "siness miles"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " were paid at `rate_first`.\n - Flat passenger "}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "rate of \u00a30.05/passenger-mile was added to claim"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "s with passe"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ngers without contributing toward the threshold."}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "\n\n4. **Emplo"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "yee Totals**:\n - **EM-32**: \u00a3463."}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "75\n - **EM-36**: \u00a3587.75\n - **EM-41**: \u00a3229.75\n - **"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "EM-42**: \u00a32"}}
{"event": "step_update", "step_update": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "step_index": 42, "state": "DONE", "step_type": "agent_response", "text_delta": "38.85\n", "duration_seconds": 11.570315662, "usage": {"input_tokens": 2961, "output_tokens": 5411, "thinking_tokens": 4815, "cache_read_tokens": 36588, "total_tokens": 8372}}}
{"event": "result", "result": {"conversation_id": "bd51d4a9-e0cb-459b-be13-2c8d84e1eedb", "status": "SUCCESS", "response": "I have processed all mileage claims according to the rules and tax-year requirements, and written the result to [`out/mileage.json`](file:///home/bob/usecase-runs/b2930/out/agy/gemini-3.8-flash-medium/b29h02/work/out/mileage.json).\n\n### Summary of Results\n\n1. **Rejected Late Claims (`> 60` days after `trip_date`)**:\n - `MC-113` (EM-41, trip 2026-03-28, submitted 2026-06-06 \u2014 70 days)\n - `MC-128` (EM-42, trip 2026-03-30, submitted 2026-06-08 \u2014 70 days)\n\n2. **Duplicate Claims**:\n - `MC-134` (duplicate of `MC-94` for EM-32 on 2026-04-01; submitted same day, tie broken by lower claim number)\n - `MC-135` (duplicate of `MC-100` for EM-32 on 2026-04-05; submitted later on 2026-04-11 vs 2026-04-10)\n\n3. **Tax-Year Mileage & Thresholds**:\n - **2025-26 Tax Year** (trips on/before 2026-04-05) initialized with each employee's `miles_before_2025_26`. Both `MC-100` (EM-32) and `MC-114` (EM-41) crossed the 10,000-mile threshold and were split between `rate_first` (\u00a30.45) and `rate_after` (\u00a30.25).\n - **2026-27 Tax Year** (trips on/after 2026-04-06) reset mileage to 0. All business miles were paid at `rate_first`.\n - Flat passenger rate of \u00a30.05/passenger-mile was added to claims with passengers without contributing toward the threshold.\n\n4. **Employee Totals**:\n - **EM-32**: \u00a3463.75\n - **EM-36**: \u00a3587.75\n - **EM-41**: \u00a3229.75\n - **EM-42**: \u00a3238.85\n", "duration_seconds": 91.772467509, "num_turns": 1, "usage": {"input_tokens": 152894, "output_tokens": 25036, "thinking_tokens": 17675, "cache_read_tokens": 284277, "total_tokens": 177930}}}