What it takes to run multiple APIs side-by-side with AI assistance

If you have dabbled around in application development, you may have come across the term API.

Application Programming Interface, or API, is a software intermediary responsible for facilitating communication between two applications. It is the channel that communicates a request to the provider and backtracks the response to the said request.

Even though APIs promise a bouquet of benefits by themselves, using them side-by-side can amplify its effects. Here’s a look at how you can make it happen using Artificial Intelligence (AI).

How Do APIs Work?

Before exploring the implications of running multiple APIs side-by-side, let us first understand how APIs work. To illustrate the functioning of APIs, here’s an example: booking a flight from a third-party travel aggregator.

You, the customer, are on one end of the spectrum, and the airline’s database is at the other end. The API is essentially the link between these two points.

Before you get started with the booking, you key in various flight-related details, such as your departing airport, arrival airport, date of travel, class, etc. The API collects these details and transfers them to the airline’s website. Based on the inputs, the servers identify the appropriate options and the API fetches these to the client-side, along with the flight costs. If you need to see more information about a particular flight, the API will once again take this request to the database and return with the relevant details. And finally, when you are all set to finalize and pay for your booking, the API will coordinate action at every step of the process — from flight selection to meal or seat selection to payment to ticket confirmation.

If the API is designed correctly, the whole booking process will take place seamlessly without any crashes!

Advantage of Using Multiple APIs in Parallel

What we have discussed so far is a single API operating at a single instance. But when multiple APIs operate in tandem, it can render the following benefits:

• Greater Efficiency: By connecting APIs or running them in parallel, you can build complex capabilities that need not be created from scratch. As such, you can add new functionalities without reinventing the wheel.
• Higher Productivity: APIs help with quick prototyping and product creation. These expedited development timelines offer productivity boosts to build multivariate solutions.
• Data Availability: By setting up complex communication channels, it is possible to transfer data, in its various forms and at various stages, between different endpoints.
• Improved Collaboration: APIs help in improving collaboration and internal communication. However, this effect gets multiplied by many folds when several APIs operate together.
• Automation: If API brings about the ideal conditions for automation, then multiple APIs side-by-side can pave the way for hyper-automation! APIs can set up channels of seamless communication and collaboration which can automate processes without a hitch.
• Cost Savings: By reducing the time to market and increasing the revenue potential through benefits like personalization, APIs can be the best way to keep development cost-effective and resource-independent.

How Can AI Help in Running Multiple APIs Side-by-Side?

Now that it has been established that APIs play a central role in responding to user activity, API management becomes key to seamless app operation. And the use of Artificial Intelligence (AI) in API management emerges as a heaven-sent solution due to the following:

Improving Processes

Deep learning techniques like AI use relevant information to gain a deeper understanding of the API’s relevance. Given that the technology is at par with human analytical processing, with the added advantage of handling high volumes of data, it is capable of efficient modeling and documentation to streamline API performance. This application can help sharpen API creation and provisioning processes in real-time, which can perfectly orchestrate parallel API performance.

Dynamic Scalability

The objective of running multiple APIs side-by-side is to gain additional capabilities with the least turnaround time. With AI, this process can be made scalable through the continued experimentation and integration of APIs through a self-correcting mechanism. AI tools can monitor metrics to assess future requirements, be it a peak or a dip, and manage APIs accordingly.

Asynchronous Operations

With AI in the mix, it will be possible to run APIs in the background to maintain a high degree of responsiveness. Keeping data at hand and maintaining responsiveness can help the application score big on the UI front. Such an approach can also help in running complicated computations and transmission of resultant data sets with minimal downtime. As a result, the app performance can be made uniform, schema-friendly, and protocol-independent.

Way Forward

When it comes to building applications that accept visual, language, or speech inputs, AI will prove to be an effective tool for deploying and orchestrating between multiple APIs.

For instance, an eCommerce application could translate product catalogs into multiple languages, allow customers to listen to product reviews, and share personalized recommendations.

Similarly, you can have a chatbot that comprehends the user’s negative or positive sentiment through voice to offer a satisfactory response.

In short, AI-driven multiple APIs make a solid case for the future of Artificial General Intelligence.