AI AGENT TEMPLATE
An AI agent that tracks rental hours per e-bike and flags units approaching their next scheduled service window — so e-bikes get caught for service before they fail on a ride.
Sending an e-bike out with 40% battery instead of 100% is a customer-experience disaster that manifests 8km from the shop, when the customer is hot, tired, and ready to ride home to find their bike has 8% left. You either send a van to rescue them (expensive, often impossible), leave them to pedal the e-bike home as a 25kg manual bike (one-star review), or hear nothing until the bad review posts. None of these are good.
The E-Bike Low Battery Alert Agent catches the problem before the bike leaves the shop. Integrated with your bike check-out workflow and (for fleet models that support it) battery telemetry, the agent flags any e-bike due to leave the shop below a configurable charge threshold — typically 90%. Staff get a notification in the check-out app; the bike is held or swapped before the customer leaves.
The operational win is measured in avoided service calls and prevented negative reviews. Even a single avoided rescue pays for the agent for a season. More subtly, the agent drives the culture shift needed — staff get used to seeing battery status as a check-out-critical variable, not an afterthought, which prevents the incidents that don't quite trip the alert but still produce unhappy customers.
Below is the full agent spec: trigger, tools, battery telemetry integration, and how to configure the threshold and check-out gating for your specific e-bike fleet.
"Every morning at 7am, pull rental hours and last-service date for every e-bike. Flag any unit due for scheduled service (drivetrain check every 25 hours, full inspection every 100 hours, motor/controller check every 200 hours). Hold flagged units off the bookable rack until signed off."
Paste this into Dash Agents. Dash reads the prompt, picks the right tools, assembles the logic, and creates a ready-to-run agent in seconds.
The agent uses the standard Dash Agents tool library. Every tool call is logged.
On trigger, the agent runs these steps in order. Any step can be customized by re-prompting.
Get rental hours since last service and days since last service for every e-bike.
Drivetrain every 25 rental hours, full inspection every 100 hours, motor/controller check every 200 hours.
Brake / motor inspections rank highest; cosmetic service lowest.
Mark flagged units as "in workshop" so the calendar drops them out of availability.
Email workshop lead with prioritized list plus estimated workshop time.
Customizations by re-prompting in plain English: change the trigger timing or conditions, adjust the recipient list, tune thresholds, modify tone, add approval gates, connect additional channels (SMS, WhatsApp) if available. Run in approval mode for the first week to build confidence before switching to autonomous.
Structured automation beats manual follow-up in three ways:
Set this agent up on your first week. Run in approval mode for five days while you watch the outputs, then flip to autonomous. The ROI compounds across a full season — a quiet automation that simply does the right thing, every time, without anyone having to remember.
Tiered schedule: ABC check before every rental (5 min), drivetrain service every 25 rental hours (motor adds load), full inspection every 100 rental hours (covers brakes, electrical connectors, fastener torque), motor/controller check every 200 hours. Premium fleets service more often; e-bikes used in coastal/wet conditions service shorter intervals due to corrosion.
Dash Agents handle the repetitive work so your team can focus on customers. Start your free trial and build your first agent in minutes.
