200 assistant.response — refund approvedWAIT order_api.permission — write scope absentMANUAL finance_console — operator re-enters requestOPEN customer_outcome — refund not confirmed
The answer was correct. The action never happened.
Forward Deployed Engineering
Engineers inside the work. AI inside production.
Polaris FDE joins your business and engineering teams. We write in your repositories, build against your data, bridge the APIs and test under real constraints—until the agreed business measure moves.
SCROLL TO ENTER
02 / FIELD AUDIT
Where does the work actually stop?
The model answered. The workflow still failed. FDE follows the request through logs, permissions, APIs and human handoffs until the break is observable.
SYNC device.activity — daily target completeMISS preference_store — quiet hours not loadedPUSH reminder_service — 07:00 notification sentRISK support_handoff — context unavailable
The sensor knew. The agent ignored it.
91% dashboard.delivery_rate — reported improvement270 cancelled_orders — removed from denominator0 additional_on_time_orders — underlying result unchangedREJECT acceptance_gate — metric definition drift
The report improved. The business did not.
THE FDE BOUNDARYParse the logs. Reconstruct the state. Bridge the systems. Test every failure path. Define acceptance using the customer outcome—not the activity count.
03 / ONE TEAM, END TO END
No handoff between diagnosis and deployment.
Senior forward deployed engineers work in the same repositories, channels and operating reviews as your team. The people who find the break build the fix and stay for production acceptance.
Bind the work to a business measure.
Inspect production logs, operating records and the database. Locate the exact stall, quantify the exposure and agree on the acceptance test.
- Failure map
- Current-state baseline
- Acceptance contract
Integrate the full path. Test the edge cases.
Connect data, permissions and business APIs. Test multi-day state, concurrency, delayed events, partial failure and human takeover.
- Production integration code
- Full test evidence
- Human fallback path
Ship gradually. Observe the real result.
Release against live traffic with monitoring, alerting and rollback. Repair the edge cases found in operation and measure the agreed KPI.
- Running system
- Operations dashboard
- SLA and support plan
04 / ENGINEERING FIELD NOTES
Change only what survives the test.
Offline studies make the method auditable. Inputs, calculations, failed approaches and limits remain visible.
CUSTOMER_OPS
4,000 resolved requests were stranded before closure.
records.scanned 20,000
requests.confirmed_resolved 4,000
test.assign_owner NO CHANGE
test.execute_close_api PASSED
Close the action,not the ticket.
PROCUREMENT
The dashboard rose from 76% to 91%. On-time deliveries stayed at 1,260.
reported.delivery_rate 76% → 91%
supplier_cancellations REMOVED FROM DENOMINATOR
on_time_deliveries 1,260 → 1,260
normalized.delivery_rate 72.2%
Reject the metric.Keep the evidence.
05 / PRODUCTION DELIVERY EXAMPLE
A wearable agent that reads before it speaks.
The user’s preference, the device event and the support handoff become one executable path. Run the trace below.
POLARIS AGENT07:00
event-router / policy-gate / support-contextIDLE
$ load user.preference.quiet_hours → 19:00–09:00
$ read device.activity.daily_goal → COMPLETE
$ evaluate reminder.policy → BLOCK
$ suppress notification.reminder → 200 OK
$ persist decision.trace → trace_07A91
$ attach support.context → preference + event + action
$ result duplicate_ping → PREVENTED
USER PREF + DEVICE TELEMETRY + POLICY + HANDOFF
AGENT WORKFLOWDEVICE + BUSINESS APIsTEST SUITE + MONITORINGHUMAN TAKEOVER
ENGINEERS ONLY
Which production workflow should we trace first?
Bring the architecture, the failure and the business measure. A forward deployed engineer will review the system with your team.
Book an engineering review ↗