If Your Agent Could Order Pizza, Would It Know Where to Go?

You just moved into a new neighborhood, and you’re on a mission to find the best pizza spot nearby. So maybe you open Google Maps, check ratings, scroll through reviews, and ask a few friends for recommendations.

Within a few minutes, you’ve got a list that’s already too long to visit one by one. You start filtering: which places are open now, who actually delivers, and which ones aren’t just frozen dough with good marketing.

Now imagine designing an online service to do the same thing. Where do machines go for information? How do you solve the same types of questions, but at scale?

The Problem: How We (and Machines) Find Pizza

That simple question, “Where’s the best pizza near me?” captures the central challenge of the modern web. Whether it’s a person, a developer, or an AI system, everyone is dealing with the same issue: how to get accurate, current, structured information from a constantly changing internet. Each method we use to find answers has strengths, but they also have limitations that need to be understood.

The Human Way

A human can read between the lines. You’ll scan a few sites, compare prices, notice when a 5-star review sounds suspiciously written, and check the date on the last customer photo. 

You’re good at reasoning, at weighing tradeoffs, at seeing context. You can tell that “decent for a Monday” is not an endorsement.

What you can’t do is scale. You can find one good pizza place, but not a thousand. Your work is unstructured, inconsistent, and impossible to reproduce.

Pros:

• Excellent at understanding context and nuance
• Naturally adaptive to new information and changing conditions
• Able to infer quality and trustworthiness from multiple signals
• Fast to interpret online data visually or linguistically
  
  

Cons:

• Slow, manual, and limited in scope
• Dependent on subjective judgment and bias
• Not repeatable or automatable
  
  

The API Way

If you wanted to make that process repeatable, you’d reach for an API. Google Places, Yelp, or OpenTable could give you a clean list of restaurants, hours, and ratings. 

You’d get structured data in JSON format that’s easy to query and store. It’s fast and scalable. But you only get what those APIs decide to share.

If you wanted to know which restaurants added vegan options last week, or who just started offering delivery, the API might not expose that data. 

When an endpoint changes or your quota runs out, the system fails. APIs are reliable for what’s defined, but they can’t evolve in real time with the web itself.

Pros:

• Consistent, machine-readable data
• Fast, reliable, and easy to integrate
• Predictable performance and costs
• Ideal for stable, repetitive tasks
  
  

Cons:

• Limited visibility beyond defined endpoints
• Provider-controlled access and rate limits
• Fragile when schemas or parameters change
• Often outdated or missing contextual updates
  
  

The AI Way

Language models make it easy to ask open-ended questions. “What’s the best pizza near me?” sounds like something an AI should be able to answer instantly. And it can, just not accurately.

A model trained on the web can recall general facts, but it can’t see what’s happening right now. It doesn’t know that a restaurant closed last month or that a new one opened across the street. Its answers are based on what it has seen before, not what exists today. The result is confident, polished, and sometimes entirely wrong.

In many ways, AIs are actually downstream from the same problems we encountered with APIs. The training datasets and external context data the AI uses to synthesize answers are themselves often attained through APIs. Therefore, no matter how intelligent, sophisticated, or natural an AI is, it’s knowledge is only as good as the data it has on hand.

Pros:

• Accessible and flexible for open-ended queries
• Can combine information from many domains
• Reduces technical friction for non-specialists
• Fast and conversational
  

Cons:

• Based on static or outdated knowledge
• No direct connection to live data sources
• Unverifiable results with unknown provenance

No structured or reproducible output

Each method covers a piece of the puzzle. 

Humans bring understanding but no scale. 

APIs bring structure but no adaptability. 

AI brings scale and accessibility but no live connection to truth. 

None of them combine reliability, flexibility, and timeliness in one system.

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Enter Web Search Agents

Web Search Agents were built for that purpose. They are digital operators that navigate, interpret, and structure live web data with the precision of a human and the scalability of an automated system. They don’t just download pages or run static scripts. They explore, adapt, and extract information based on the logic of the task itself.

Each agent has a specific mission. One might track product availability across e-commerce sites. Another might monitor pricing changes or collect regulatory data from public filings. Each one works autonomously, coordinated by Nimble’s orchestration layer that manages scale, compliance, and delivery.

Here’s how they work.

Configuration through the Nimble interface
Users define the data objective: what to collect, from where, and how often. The setup includes filters, validation logic, and delivery destinations. Everything runs through Nimble’s dashboard, with no code or manual scheduling.

Adaptive exploration
Once deployed, the agent browses target sites like a human researcher. It can click, scroll, expand menus, and even handle login flows if needed. It recognizes site structure and layout patterns dynamically, so when a page changes, it adapts automatically without breaking.

Live data structuring
The agent parses what it sees into structured, schema-consistent outputs. Product names, prices, timestamps, or text are extracted and normalized in real time. The result is clean JSON that can flow directly into your database, analytics stack, or model input pipeline.

Validation and governance
Every data point passes through Nimble’s trust layer, where it’s verified for accuracy, completeness, and compliance. Duplicates are removed, anomalies flagged, and freshness confirmed before anything leaves the system.

Streaming delivery
The final dataset is streamed directly to where you need it - cloud storage, a data warehouse, or an API endpoint. Pipelines can run continuously or on a schedule, maintaining always-current intelligence without manual upkeep.

Web Search Agents operate at scale across thousands of domains. They handle millions of requests, resolve site variability automatically, and maintain uptime through Nimble’s orchestration network. When one source changes, others keep running. When traffic spikes, capacity scales seamlessly.

Pros:

• Live, structured, validated web data delivered continuously
• Self-adapting to layout and content changes
• No-code setup through Nimble’s UI
• Built-in governance and compliance
• Integrates directly with existing workflows
  

Cons:

• Requires careful scope definition to avoid unnecessary data volume
• Governance policies must align with use-case sensitivity

How a Web Search Agent Would Find the Best Pizza

1. Define the mission
   In Nimble’s UI, the user creates a new agent named “Local Pizza Finder.” The goal: gather and rank live data about pizza restaurants within a defined area, combining details like location, rating, delivery options, and recent reviews.
   
2. Scope the data
   The agent is directed to multiple domains, such as Google Maps, Yelp, DoorDash, and TripAdvisor, with the instructions to extract information from structured listings and visible text reviews.
   
3. Explore intelligently
   The agent begins navigating each target site. It scrolls through results, filters by “open now,” expands menus, and identifies location coordinates. It adapts to each site’s unique layout, automatically skipping ads or unrelated listings.
   
4. Extract and structure
   As it browses, the agent collects structured fields like name, rating, delivery availability, and price range. All data is normalized into a consistent schema.
   
5. Validate and deliver
   The agent cross-references duplicates across sites, flags mismatched information, and verifies freshness. The cleaned data is streamed to the team’s data warehouse, where it can be visualized or used to power an AI chatbot.
   

Within minutes, the Web Search Agent delivers a reliable, consistent, structured dataset that AIs can use to answer the question in real-time, and get you that hot slice you’ve been after all along.

Conclusion

Humans, APIs, AIs, and Agents all have their strengths and weaknesses, and provide important solutions for businesses of varying sizes. Ultimately, the key is to know for your unique use case, business, and scope, which tool is the best for the job.

We hope this blog shed a helpful light on the different technologies and solutions available, and if you’d like to explore how Nimble’s Web Search Agents can help you structure and stream internet data, drop us a line!

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