POC planning
The POC playbook for location-based AI agents
A good pilot is narrow enough to finish and realistic enough to prove whether the agent can work in the field.
POC planning
A good pilot is narrow enough to finish and realistic enough to prove whether the agent can work in the field.
Scope
Location-based agents can easily become too broad. A retail assistant, field service copilot, campus guide, or warehouse QA agent may touch many systems and many user journeys. The first POC should avoid that trap.
The target is one place, one workflow, and one measurable outcome. That gives the team enough context to test real behavior while keeping the work bounded enough to ship, evaluate, and decide what comes next.
WorldAgents is designed around this path: model the place, configure the agent, run simulations, and move into a controlled pilot when the behavior is credible.
Playbook
Start with a store, venue, warehouse zone, campus area, or field location where context changes the work.
Pick a task with clear steps, visible inputs, rules, exceptions, and a real operator or customer outcome.
Use a concrete metric: time saved, fewer missed checks, faster onboarding, better conversion, or fewer escalations.
Run the agent through realistic scenarios so the team can adjust behavior before it meets real users.
Deliverables
The output should make it clear what worked, what failed, what must be integrated, and whether the workflow is worth expanding.
WorldAgents pilots