7 Search API Tricks That Make AI Agents More Capable Than Basic LLMs

 

A language model is capable of explaining something, writing code, or answering questions. However, in case you request the latest rankings of your competitors, a comparison of search results between various countries, or verification of a newly discovered fact, a model needs to have access to extra information.

This is when search APIs come into play.

A search API allows developers to establish connections between applications and search results of any external resources programmatically. Rather than using only the knowledge that an AI model possesses, an agent could use search results and perform a task.

The key value of search APIs lies in how developers use the search data to their benefit. More precise queries, targeted search results, iterative searches, and well-structured answers can enhance even a simple question-answering application by turning it into a better research and automation solution.

These are seven tips developers can use to integrate search APIs in their AI applications.

1. Turn Broad Questions Into Focused Search Queries

One of the easiest methods to enhance the quality of search is to not make fuzzy requests to the search API.

Imagine that a customer is asking "What is the best project management tool for small business?" In this case, the easiest solution is just to make a request with this question and take some initial answers.

The smarter way will be to break this question on smaller subquestions:

• Which project management tools are tailored to small businesses?

• What capabilities are described on their official websites?

• Which ones offer free plan?

• What drawbacks should customers consider?

And after that, an agent can find the answers to these questions and merge all results.

Why is it useful? Different searches can provide different data, product pages describe capabilities, documentation describes drawbacks, and independent reviews give another perspective on the problem.

Also, developers can add some restrictions, such as audience, preferred source, etc., if the search API provides these features.

When to use it: Product research, technical comparison, buying guides and complex questions.

2. Use Location Targeting Instead of Assuming Everyone Sees the Same Results

Search results differ from country to country, language to language, and according to geographic environment. A search engine that fails to account for these variations will provide incorrect recommendations to users depending on their geographic location.

For instance, when researching "best accounting software", a company could obtain different competing sites in India and the United States. A result that holds relevance in one country could be non-existent in another.

In that regard, a geographical search API will allow developers to ask for search results within the location of interest. The search engine will then make a comparison of the provided pages and discover differences among the regions.

Such an approach will be beneficial in performing SEO research, business analysis, and applications that need to recommend some product or service within the specific market.

SERPHouse allows accessing SERP data to perform search analysis, including geographically based research where available with targeting options.

When to use it: Local SEO, international keywords research, region competitor research, and market entry.

3. Retrieve Structured Results That Your Application Can Process

Search result data becomes easily manageable with API calls returning it in a standard format.

Instead of copying information from search pages manually, one can receive a structured piece of data with fields like title, URL, snippet, rank, and others (depending on a provider).

An agent can then apply this data to organise results, deduplicate, filter out irrelevant web pages, and select sources for further investigation.

Imagine developing a competitor analysis app that would fetch search results for a set of commercial keywords, categorise the results according to the domain, and find out the most frequent competitors. In turn, one could create a summary using a language model.

A SERP API comes in handy for applications requiring structured data from search engines for keyword research, ranking analysis, and search visibility monitoring.

The critical thing here is not to take into account the fields in the response based on assumptions but to investigate the actual schema provided by the API.

When to use it: SEO dashboard, research pipeline, report automation, and other search-related applications.

4. Search Again When the First Results Leave Important Questions Unanswered

One search request alone may not contain sufficient information in order to resolve an elaborate question. Rather than taking the first response to be definitive, a follow-up search procedure can be programmed.

For instance, an agent researching the software product may discover its website yet still require information on the subject of API authentication, available endpoints, etc.

It could then use the information from the first result set to determine what is still missing, formulate a more narrow question, and perform another set of searches.

A sample algorithm of this kind would include the following steps:

• Perform the primary search for the subject in question.

• Analyse the results  titles, URLs, and snippets included.

• Determine any missing information or unsettled claims.

• Formulate a more narrow search question.

• Collect additional results and analyse the evidence.

Such an approach allows an agent to explore the subject rather than merely summarise the information found in the first answer.

The follow-up searches must always serve a particular purpose. Replicating a search request is unnecessary waste of request allowance.

Usage: technical research, product review, troubleshooting, report writing.

5. Combine Search Results With Source Verification

It doesn't necessarily mean that a page mentioning the claim is indeed the case.

An agent may find a blog post with an old statistic, a product page with incomplete data, and even two pages contradicting each other. Generating an answer right away will only transmit these errors to the user.

A better approach would be verifying significant claims against available sources.

For instance, while researching a software product, an agent may first check the documentation for supported features and then other sources in case of necessity for independent verification.

Developers can incorporate the following checks:

• Does the source support the claim?

• Is the data fresh enough to solve the problem?

• Are there any other reputable sources confirming this information?

• Is the answer obtained from the source itself, or is it a summary of another page?

Source URLs need to be preserved for the user to verify the information independently. Search APIs allow for obtaining potential evidence; however, it doesn't prove its factual correctness. When to use it: Research assistants, technical documentation systems, factual answers, business intelligence.

6. Filter Results Before Sending Them to a Language Model

More results are not always better for the quality of the answer. Feeding hundreds of irrelevant pages to a model increases computation costs and diverts attention away from the most useful content.

To make it more efficient, developers can filter out search results before feeding them to the next step.

Some helpful strategies include elimination of duplicate URLs, rejection of unnecessary domains if needed, preference for official documentation, and sorting by relevance to the initial question.

For example, an agent responding to a question about the parameters of an API endpoint will gain more from official documentation and some sample code than from many opinion pieces.

Some search APIs allow you to use query operators, restrict search to specific domains or adjust the number of results. Other filtering steps can be done at the application level.

Different approaches work for different tasks. A product comparison needs several independent sources of information, while a question about the API parameters might be best answered through official documentation.

When to use it: Retrieval-augmented generation (RAG), code assistants, enterprise research, and cost-sensitive applications.

7. Automate Search Monitoring Instead of Checking Results Manually

Search APIs can additionally facilitate workflows that repeat rather than answer a one-time user query.

For example, an SEO tool can be required to track target keywords, analyze competing websites, and spot any significant changes to the search results. Manual performance of such tasks is becoming increasingly hard to do as the number of keywords increases.

The repeating workflow will allow fetching results on the schedule, comparing them with cached ones, and highlighting significant changes to analyze further.

An agent will then be able to generate the report describing what keywords were changed, what pages were among the competitors, and what is worth investigating.

It can be possible to explore the SERPHouse platform for such search-data workflows via the SERP API and Web Search API.

To ensure reliable tracking, developers need to take into account possible variations caused by the location, device, search settings, and measurement time. The ranking change alone is unable to tell why the results improved or got worse.

When to use it: Keyword tracking, competitor analysis, research, and SEO reporting.

How to Build a Better Search-Based Agent

These strategies are most effective when incorporated into a clear workflow. Here is an example workflow to consider:

Make sure to use particular queries, filter out unrelated links, and check claims before giving an answer. In case you are not able to find enough evidence with the first attempt, try using a more specific query. In order to perform SEO-related tasks, structured SERP data will allow agents to compare rankings, analyse competitors, and create content. 

Each stage has its unique role. The first stage helps to improve relevance, the second to get external information, the third to remove noise, and the last one to avoid incorrect claims.

In addition, developers need to handle failed requests, empty result sets, rate limits, and unavailable sources. In case the API does not return any useful evidence, make sure to notify about it, not give any invented answer.

It makes sense to start with one search-based task and calculate the number of sources that provide relevant information to the answer. When the result justifies it, add more complex steps.

Choosing a Search API for Your Project

In choosing your preferred search API, consider the following criteria:

• Coverage: What search engines, types of results, and geographic location does the API support?

• Data structure: Is the data returned in structured form to work with your application?

• Freshness: Can it deliver fresh data that meets your requirements?

• Scalability: Are the request limits and fees reasonable for your projected load?

• Documentation: Is there proper documentation on how to authenticate and handle error responses?

• Targeting: Is it possible to specify the geographic location or any other criteria you may want to filter by?

If you need to do some research on the web, focus on relevant websites and interesting page content. In case of SEO tracking, make sure to consider the necessary SERP features and targeting capabilities.

It is possible to check out SERPHouse to examine its search-data services and documentation, as well as to register an account.

Final Thoughts

APIs for searching can be useful for developers who want to build an effective bridge between the language models and information outside the models' current body of knowledge. Focused queries, geolocation-based searches, response structures, follow-up queries, source validation, filtering, and automatic monitoring can all help improve the utility of search-based applications.

The most important thing is not to gather the biggest possible number of results but to get the right evidence, analyze it, and then use it to accomplish a particular task.

These seven approaches will serve as a good start for developers looking to create research tools, SEO processes, and applications based on the information gathered from search engines.