AI AGENT TEMPLATE
An AI agent that watches rental count per bike and alerts the tune room when service is due — before problems happen on the road.
Bikes at scale fail on a schedule if you track them properly and fail randomly if you don't. A modest 30-bike fleet renting 5 days a week generates roughly a brake pad, cassette, chain, or cable issue every operating day — minor individually, cumulatively a mountain of untracked maintenance that either gets caught at check-in (customer waits while a bike is swapped, poor experience) or misses entirely and produces an on-ride failure (bad experience, sometimes a safety issue).
The Bike Maintenance Due Alert Agent lives on the mileage and time intervals for each component category across your fleet. As bikes cross thresholds — 1,000km on a chain, 500km on brake pads, 2 years on a cable housing — the agent flags them for the shop mechanic with category-specific detail. Service gets scheduled during low-demand windows instead of as an emergency when a bike breaks.
The real value is in the quiet bikes. It's easy to service the squeaky one; the bike that hasn't complained but is one hard stop from a failed brake pad is where the hidden risk lives. Operators running the agent report breakdown rates falling 60-80% after one season, customer satisfaction scores climbing, and — the economically underrated part — mechanic time shifting from reactive triage to scheduled flow-efficient work.
Below is the full agent spec: trigger, tools, mileage tracking integration, and how to tune thresholds for your specific bike categories and rental intensity.
"Every morning at 7am, pull all bikes with rental count > 10 since last service or more than 30 days since last service. For each, identify what service is due (chain, brakes, tubeless refresh). Send the tune room lead a prioritised list."
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 customised by re-prompting.
Get every bike with rental count since last service and days since last service.
For each bike, check against standard intervals: drivetrain every 10–15 rentals, brakes every 30, tubeless refresh every 60 days, full tune every 100.
Brakes over drivetrain over sealant. Bikes due for multiple services ranked highest.
One email with prioritised work list by tune tech, plus estimated time.
Email tune room lead; log notification against each bike asset.
Customisations 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.
Standard intervals in rental use: drivetrain service every 10–15 rentals, brake inspection every 30, tubeless sealant refresh every 60 days, full tune every 100 rentals. Track per-bike rental count and last-service date in your rental system. Schedule daily based on which bikes have crossed thresholds. Running this manually on 50+ bikes is error-prone; an automated alert agent removes the tracking burden.
Dash Agents handle the repetitive work so your team can focus on customers. Start your free trial and build your first agent in minutes.
