Get a Real-World Price Check

Price Check is useful when software knows what information or action it needs but the final step requires a person at a physical location. On HumanTask API, the request is framed as a bounded job: collect a current displayed price from a physical location and return it with evidence and context. The requester defines the target, timing and proof before anyone accepts the work.

When to Use Price Check

The best price check jobs are concrete enough that two independent workers would understand the same objective. Examples include verify shelf price for a product; record a menu or service-board price; check whether a local promotion is active; or compare a small set of competitor prices using the same protocol. These are not broad consulting assignments. They are observable field actions that can be accepted, completed and checked.

Define a Price Check Request

A requester should provide location, product or service identifier, and price field to capture. It should also state promotion details, visit timing, and required supporting photo. The instruction should separate facts the worker can observe from decisions the AI or business will make later. That distinction prevents a simple field task from quietly turning into specialist advice.

Required inputs

• Location
• Product or service identifier
• Price field to capture
• Promotion details
• Visit timing
• Required supporting photo

Evidence That Makes the Result Useful

For price check, a useful completion package may contain observed price, currency as displayed, supporting image, promotion notes, and timestamp and location context. The evidence schema should be selected before dispatch. A task that merely says “send proof” is weaker than one that specifies which files, fields and observations are mandatory. The receiving agent can then test completeness without interpreting a chat message.

Suggested result fields

• Observed price
• Currency as displayed
• Supporting image
• Promotion notes
• Timestamp and location context

Failure Modes to Plan For

Real locations create exceptions that software APIs rarely face. In this capability, common examples are: multiple prices apply to loyalty members or bundles, tax treatment is not obvious from signage, price differs between shelf and checkout, and the item is unavailable. Those outcomes should be returned as explicit exception states rather than hidden inside a free-text note. An AI agent can then retry with new instructions, choose another location, widen the deadline or escalate to a specialist.

Exception examples

• Multiple prices apply to loyalty members or bundles
• Tax treatment is not obvious from signage
• Price differs between shelf and checkout
• The item is unavailable

Example Workflow for an AI Agent

Consider this workflow: A pricing agent can request the same product price from three nearby stores and compare evidence-backed observations instead of scraping stale pages. The agent first decides that a physical check is necessary, then creates a price_check task with the address, deadline and evidence fields. The worker accepts the job, completes only the permitted actions and submits the requested proof. After the result arrives, the software can validate required fields, store the media references and continue its original plan.

Who Uses Price Check

Price Check can support pricing teams, researchers, brands, procurement systems and AI agents comparing local offers. These users have different business goals, but they share the same bottleneck: the missing fact or action exists offline. A reusable task definition lets them solve that bottleneck without maintaining a field team in every city.

API Shape for Price Check

The machine-facing representation should be narrow. A price_check request can carry a normalized location, human-readable instructions, a deadline, budget, required evidence and a client reference. The response should return a task identifier and lifecycle state. Follow-up operations should expose status and evidence without forcing the caller to scrape a dashboard. MCP can present the same operation as an agent tool; REST and OpenAPI can serve conventional application integrations.

Example task object

{
  "capability": "price_check",
  "location": {"address": "TARGET_ADDRESS"},
  "deadline": "ISO_8601",
  "instructions": "TASK-SPECIFIC_INSTRUCTIONS",
  "evidence_required": ["TASK_SPECIFIC_FIELDS"]
}

Launch a Price Check Task

Start with one price check request that has an unambiguous outcome. Define the location, deadline and proof first; then create the task through the web flow or the available developer interface. If the workflow repeats, promote the same evidence schema into a reusable integration.

What makes price check different from a generic gig

The value is not simply that a person is available. The value is that the request is standardized enough for software to understand the expected result. For price check, the schema should reflect the actual decision being supported: the agent needs evidence about collect a current displayed price from a physical location and return it with evidence and context. That makes the task easier to price, route, compare and audit than an open-ended message to a freelancer.