AI has changed a lot about how we consume content and services, and this is just as true in the API space. AI-driven consumption of APIs is changing the game, and whether or not we’re ready for it, providers need to prepare their services if they are to perform in the API economy.

Below, we’ll look at how AI is changing API consumption at scale and give you some best practices to implement today to get ahead of the curve.

How AI Has Changed API Consumption

One of the interesting facets of any new technology is that its impacts often spread like spiderwebs. The initial use case is often very interesting or valuable, but how it can influence the way we work, the way other technology works, and how we actually use technology is seldom appreciated.

This has proven especially true with the rise of AIs. In its current state, AI utilizes large language models (LLMs) to generate text, images, video, and so forth. These models require a large amount of context, and this context is typically delivered in the form of data and external services.

With AI, the barrier to entry for accessing data is very different. In past years, you had two options: either there was an API, or there wasn’t. If you wanted to scrape data automatically, you had to hope for an API directly from the first-party provider or from a third party who created an API for a use case similar to yours. If there was no API, you were limited to what you could feasibly do on your own. Even if you were to scrape pages from a public service, you would still have to collate that data, arrange it, and contextualize it.

The Power of the Agent

AI agents have completely changed the game. They can act like a human with the efficiency of a machine, a potent combination that is simply incredible in practice. You can ask an agent to find all of the times for a specific movie showing near you, and almost instantly, you will be able to generate a list with suggestions, directions, and additional context. Agents bridge the gap between human flexibility and machine efficiency. But what does this mean for the API provider?

The binary that once existed — the API exists, or it doesn’t — has suddenly shifted somewhat. It has become easier than ever to get data or functions from a service through an AI agent that works outside of the API entirely. This shift has created the potential for uncontrolled access to a service from the external point of view in a way that is close to an API but without any of the trappings, controls, or influences of the API itself. The limiting factor is no longer whether you have an API or not, it comes down to the amount of processing power and time dedicated to scraping data or services by the external party. This is a huge paradigm shift and is, frankly, one that is challenging to control.

Building APIs for the Reality of AI

Simply put, your service and data will be consumed whether or not you create an API. With so many agents, LLMs, and AI implementations available for mere dollars, there is basically no barrier to use with modern AI. Your service, regardless of whether you have an API (or a well-designed API, for that matter), will face machine consumption patterns. Even if you secure the service with effective authorization and authentication, the data as it lives in publicly viewable spaces is still scrapable.

The downside of this approach for the service provider is that it shifts the consumption paradigm. The service provider used to be able to control the data flow and determine what data was made available, what endpoints were public or private, and what data flow was allowed through the API. With the advent of AI and agent-based consumption outside of the API, anything publicly available can, in theory, be scraped with minimal cost in terms of time and resources by the agent, making this data accessible in a trivial manner.

With this in mind, there is one obvious solution. API providers should improve the design of their APIs for consumption, rather than having their services mirrored or transmuted into different form factors. So, how can API providers build their APIs for this reality?

Make Your Data Accessible

Data is the real lifeblood of modern AI, and the reality is that it will be generated and consumed regardless of whether you surface it. Accordingly, surfacing the data in the form and function you desire will bypass the secondary sourcing of this data while improving the overall performance of your services. As part of a complementary approach to API design, this will allow you to make it easier to use your API than it is to scrape data via an external service, an additional friction which, hopefully, will incentivize users to actually use your API as a first option.

So, how do we make data accessible? First, start with high-quality and structured data. AI applications depend on structured data to work effectively, but in the absence of structure, they will assume this structure on their own. This can have disastrous outcomes, so setting your structure clearly and expressing that inside the data itself (and as part of the endpoints) will result in far clearer and more accurate outcomes.

Next, ensure that your data is rich, providing both real-time data where appropriate and historical data where necessary. When you change your data formats, provide an understanding of how this data has changed from version to version to allow for contextual continuity.

Lastly, anonymize your data wherever possible. Data privacy through anonymization can be tricky, but this can be made easier by only collecting the data you’ll need rather than collecting everything possible. Ensure that you adhere to all data privacy protections and regulations and that your live service surfaces the same data surfaced by your API (and vice versa).

Design for Integration

Agents are typically an alternative method to consume APIs that arise because an API does not have the functionality or data flow on offer that the user desires. Yet, most developers would rather integrate with an API with a high quality of life and functionality. Accordingly, focus on good practices for integration design.

Leverage technologies like GraphQL, pagination, and hypermedia to provide an API that is self-describing and metacontextual. By giving this functional context, your API can be consumed in a governed way. Also, utilize SDKs and wrappers to ensure developers can integrate your API into other projects. A project that tries to make its own integration will never be as closely coupled to your design approach as one you create, so providing even basic SDK functionality will dramatically improve your outcomes.

Document Everything, And Surface It

Documentation is a literal playbook for agents and AI systems at large. Instead of hoping that your structure is good enough, providing ample documentation will help bypass misunderstandings or poorly designed user flows. Here are some things to consider:

• Provide usage examples and sample datasets, especially for flows that are perhaps not as obvious as they first seem. This will help AI agents understand how your service is designed and what it’s meant to do.
• Explain your data structures and expected results to clarify potential fail states and incomplete responses. Provide clear error codes that can be passed to agents or other AI-driven integrative solutions.
• Provide an AI-specific landing page and API explorer to help provide a top-level understanding of your service and how it’s used.

Focus on Optimization

As AI evolves and becomes more ubiquitous, AI consumption of APIs will grow. Accordingly, ensuring your system is as optimized as possible will go a long way towards insulating your API from the future of high automated consumption. Here are some optimization tactics:

• Focus on low latency and highly efficient queries. Ensure that you are utilizing caching, pagination, and other solutions to reduce the overall payload and serve only that which is requested or that which is required to make additional requests for materials.
• Implement effective rate limiting to make sure that traffic is managed equitably. You don’t want to kill all automated traffic, as this will default agents to crawling the live service, but you can throttle or limit automated requests to ensure equitable access.
• Enable autoscaling to meet high-traffic demands so that your consumers are not impacted by the ebbs and flows of automated use.

Conclusion

AI consumption isn’t going to end any time soon, so your best bet is to get ahead of the problem and start designing your APIs for AI consumption. This is not a case of “build it, and they will come.” They’re already here, and they’re already using it, so what you provide today will determine the efficacy and accuracy of that use at scale.

What do you think of these guidelines? Did we miss any best practices? Let us know in the comment section below!