Weather decision support · Agentic product case

Do I still go-and what needs to change?

A product-first case for an agent that turns changing weather into a justified personal outdoor-activity decision, monitors what matters and never hides uncertainty behind a confident summary.

PersonSolo recreational cyclist
PlanSunday · 09:00–13:00
Route68 km · exposed sections
DecisionGo, adapt, delay or cancel

Independent product case. Weather, usage and financial figures are illustrative; production decisions require validated forecast sources, user research and operational baselines.

The moment

01 / 13

The forecast is information. The user still owns the difficult translation.

Maya plans a four-hour cycle outside Berlin. One app shows rain probability, another shows wind gusts, and an alert mentions thunderstorms for part of the region. None knows her route, exposed sections, latest return time, rain tolerance or whether a one-hour delay would solve the problem.

Scattered evidence

Three sources, no shared decision

Hourly forecast, radar movement and official warning describe different parts of the same risk.

Personal conditions

The same weather means different things

A short park ride, exposed ridge and child-carrying trip cannot share one recommendation.

Changing state

A good answer expires

A decision at 07:00 may be unsafe at 08:30 if the storm timing or route changes.

Job to be done

02 / 13

Help me protect the activity, not merely understand the weather.

“When weather could affect my outdoor plan, help me decide whether to go, adapt, delay or cancel-and tell me what evidence would make that decision change.”

Success is not opening the forecast or reading a summary. Success is making a timely, confident and appropriately cautious decision, then avoiding a preventable surprise during the activity.

Initial focus

03 / 13

Start with the decision conditions most likely to change the plan.

Each candidate is scored on frequency, consequence if missed and whether current evidence can support a useful recommendation.

ConditionFrequencyConsequenceEvidencePriorityThunderstorm timingMediumHighStrong1Wind on exposed routeHighHighStrong2Rain comfortHighLowStrong3Heat / UV loadSeasonalMediumStrong4
Chosen first: go/adapt/delay/cancel for cycling and hiking plans affected by storms, wind and rain. Air quality, snow sports and medical advice stay outside the initial scope.

Solution choice

04 / 13

Earn the agent. Compare simpler ways to solve the job first.

Forecast summary

Explains conditions but cannot connect them to the route, constraints or changing plan.

Fixed rules

Good for hard warning thresholds, but brittle across activity, exposure, timing and user tolerance.

Guided workflow

Works when the decision path is known, but forces users through every question and does not re-plan.

Bounded agent

Useful because it gathers missing context, calls several tools, compares plan variants and monitors change.

Why an agent: the path is variable, evidence changes, multiple tools are required and the system must decide which single missing fact would change the recommendation. The agent does not own official warnings or hard safety thresholds.

Experience

05 / 13

One decision, its reasons and the next moment it will be checked.

The experience opens with a decision rather than a weather paragraph. Users can inspect evidence, change a constraint and see the plan recompute.

OUTDOOR PLAN AGENTILLUSTRATIVE · BERLIN
ActivityCycling · 68 km
Window09:00–13:00
ConstraintHome by 14:00
Risk stateStorm after 12:00

What matters most for this ride?

ADAPT

Start at 08:00 and shorten the exposed final section.

This returns you before the storm window while preserving most of the ride. Recheck at 07:30; cancel if the official warning expands west or lightning probability enters the route window.

Official warning · 06:42Radar track · 06:45Route exposure · 18 km

Scope line

06 / 13

Be explicit about what is real, inferred and still simulated.

Real and designed

Decision model and boundaries

JTBD, state model, decision ownership, evidence contract, safety rules, evaluation design and recheck logic are product decisions.

Simulated here

Feeds, route and user history

Weather values, radar movement, route exposure and preference memory are scripted. No live safety claim is made.

The next proof step is one consented route connected to timestamped forecast, radar and official-warning feeds, run in shadow mode against decisions made by experienced outdoor users.

System design

07 / 13

State and safety rules constrain the agent; tools supply current evidence.

Plan state

Activity, route, time window, transport, companions, equipment, tolerance and latest acceptable return.

Product state
Weather adapter

Normalises forecast, radar, official alerts and timestamps; exposes disagreement and freshness.

Tool
Hard gates

Official severe warning, lightning proximity and stale evidence can block or withhold a recommendation.

Rule
Plan comparison

Compares start times, shorter routes and cancellation against the user's stated job and constraints.

Model
Monitoring

Rechecks only the evidence that could change the accepted plan, until the user ends monitoring.

Orchestrator

Decision ownership

08 / 13

Every decision has an owner-and a reason the model does not own it.

DecisionOwnerJustification
Is an official severe warning active on the route?TOOLA current external fact; it must be fetched, not generated.
Is evidence too stale to recommend?RULEA testable freshness threshold with a safe default.
Does lightning breach the safety boundary?RULEA deterministic risk gate, not a language judgement.
Which route sections are exposed during the risk window?TOOLGeospatial intersection over route and weather cells.
Which plan variant best preserves the user's job?MODELRequires comparing incomplete trade-offs across timing, comfort and route.
Which missing question would change the recommendation?MODELContextual judgement over current uncertainty and available options.
Should monitoring begin?PERSONRequires explicit consent for notifications and location-linked processing.
Should the activity proceed despite a hard warning?PERSONThe system can advise or refuse support; the real-world decision remains the user's.
The model owns two of eight decisions. That is intentional: agentic value comes from coordinating state, tools and plan variants-not from giving the model authority over every choice.

Evidence and refusal

09 / 13

A recommendation expires with its evidence.

Source

Every weather fact names the provider or official authority.

Observed at

Forecast, radar and warnings carry independent timestamps.

Valid for

The answer states the route area and time window it covers.

Recheck trigger

The system names the specific change that would alter the decision.

Missing

Ask one question

If latest return time changes the plan, ask for that-not a full questionnaire.

Stale

Refresh or withhold

If radar or warnings exceed their freshness limit, do not present a current recommendation.

Contradictory

Show the disagreement

When sources diverge materially, choose the safer plan and expose why confidence is lower.

Evaluation

10 / 13

Test the decision, the explanation and whether monitoring catches change.

The evaluation set crosses activity type, route exposure, user tolerance, forecast disagreement, missing constraints and sudden warning changes. Safety and usefulness have separate release gates.

Correct plan class
≥ 88%
Hard-gate recall
100%
Evidence binding
≥ 98%
Useful single question
≥ 85%
Change detected in time
≥ 95%
User outcomeConfident decision made in time
QualityPlan-class and evidence accuracy
SafetyZero missed hard warnings
OperationsCost per monitored plan

Value and learning

11 / 13

The value is a better decision; the advantage is learning which evidence changed it.

A general forecast can describe weather. This product can observe which recommendation was accepted, whether the plan changed again and which condition caused that change-with consent and aggregated measurement.

Monthly eligible plans50k

Illustrative outdoor plans where weather affects a stated decision.

×
Decision value€0.80

Retention, subscription or avoided support value per useful decision.

Run cost€12k

Leaves €28k monthly contribution before one-off build cost.

Validate before investment: eligible-plan volume, willingness to pay, retention lift and inference/tool cost. The model is useful only if completed decisions-not summaries viewed-improve.

Rollout and stop conditions

12 / 13

Increase influence before authority-and make rollback specific.

Phase 1Historical replay

Evaluate past forecast changes and expert-labelled plan decisions.

Phase 2Shadow recommendations

Generate silently; compare with user choices and actual weather.

Phase 3Decision support

Show recommendation, evidence and recheck trigger. No monitoring by default.

Phase 4Consented monitoring

Notify only when the accepted plan's decision state changes.

Stop customer recommendations

Safety or evidence fails

A hard warning is missed, a weather claim lacks current evidence, or plan-class performance falls below its gate.

Stop the investment

The decision does not improve

Users still cross-check manually, timely decisions do not increase, or monitored plans cost more than their validated value.

Decision record · 13 / 13

Ship a bounded decision companion, not an autonomous safety authority.

The product earns its place by assembling context, comparing plan variants and monitoring a decision. It does not invent weather, override official warnings, begin monitoring without consent or claim that an outdoor activity is safe.

Ship firstCycle and hike plan decisions for rain, wind and storms
Keep constrainedOfficial warnings, hard gates and consent stay outside the model
Earn nextRoute alternatives and calendar changes after evaluation proves value