API vs AI: how to understand their relationship

As AI use cases continue to spread, it can be difficult to discern when and how APIs should be used.

To help your team leverage APIs effectively for key AI initiatives, we’ll break down the differences between AI and APIs, how they support one another, and when it’s best to keep them apart.

To start, let’s briefly align on each.

What is an API?

An API is a collection of protocols and guidelines that define how applications can securely interact with each other.

More specifically, they’re made up of endpoints that expose certain types of data and functionality in an application. 

Each of these endpoints consist of predefined rules, such as how often they can be called in a given period (i.e., its rate limit), how they return response data (e.g., its approach to pagination), and how they authenticate incoming requests (e.g., using OAuth 2.0).

For example, BambooHR, a leading HRIS solution, offers an endpoint that lets you fetch data on a specific employee: <code class="blog_inline-code"> https://api.bamboohr.com/api/gateway.php/{companyDomain}/v1/employees/{id}</code>

This endpoint also authenticates calls via API keys, enforces varying rate limits based on your plan, and uses cursor-based pagination.

What is AI?

AI has a much broader definition. Put simply, it’s the use of computational systems to behave in ways that are associated with people, like learning, problem solving, making decisions, and taking actions.

For example, AI agents are a common application of AI. 

They can perform a whole host of activities on behalf of employees, whether that’s processing refund requests for customers, generating reports from different data sources for finance, and submitting PTO requests for employees.

Companies like Ema (Enterprise Machine Assistant) are cropping up to help companies build and manage these types of AI agents.

Given these unique definitions, how do AI and APIs compare? We’ll tackle that next.

https://www.merge.dev/blog/ai-agent-integrations?blog-related=image

AI vs API

AI and APIs are fundamentally different concepts and can be used independent of one another. That said, certain AI applications that rely on static, predefined workflows can and should use APIs.

Examples of when they can work together

To better understand when they should be used together, let’s review a few relevant use cases.

Enterprise AI search

Enterprise AI search products allow employees to ask questions and—based on the underlying API integrations—receive comprehensive and accurate responses through natural language processing (NLP).

Guru is a great example. 

An employee can ask all kinds of questions in Guru, like “Where is the team offsite this quarter?” 

Using the relevant information from an integrated file, Guru’s AI-powered search product can then generate an output that includes the date.

Financial reporting

Imagine that you provide a financial planning and analysis (FP&A) solution and want to help users get value from your platform quickly.

To that end, you can implement API integrations with their accounting solutions. Your large language model (LLM) can then use the integrated financial data to auto-generate predefined reports, like a runway forecast.

https://www.merge.dev/blog/rag-vs-ai-agent?blog-related=image

Headcount management

Say you offer a headcount management solution and want to help users identify unusual employee spend and the factors that drive it through an AI agent.

To support this use case, you can build API integrations with customers' HRIS solutions and create an AI assistant on top of these integrations.

The AI assistant can analyze patterns in employee spending over several months (using salary data pulled from the integrated HRIS) to establish baseline figures. It can then detect unusual patterns over time, uncover the driving forces, and then alert the relevant teams.

Examples of when they shouldn’t be used together

If you’re dealing with workflows that require an LLM to make improvised, real-time decisions, it may be more effective to use alternative integration methods, like the Model Context Protocol (MCP).

Here are just a few examples.

Personalized employee onboarding

Imagine you offer an HRIS solution and want to build an AI agent that determines highly specific onboarding steps for each new employee.

This could involve deciding which systems they should get access to, what meetings need to be scheduled, what emails they should receive in the first few days, and much more.

Given that this level of intelligence requires determining and executing workflows that are difficult to predict or standardize, relying on APIs wouldn't make sense.

Customer support agents

If you offer a customer support solution and want to power AI agents that can handle all kinds of customer issues independently, you likely wouldn’t depend on APIs.

Your AI agents would constantly receive new types of requests that are hard to forecast and would need to determine the steps for supporting each on the spot.

https://www.merge.dev/blog/api-vs-mcp?blog-related=image

Get the best of AI and APIs with Merge

Merge can add hundreds of integrations to your product through a single Unified API to help you support a wide range of AI features and products.

To power more agentic use cases, Merge also supports an MCP server (Merge MCP) that lets you access all of these integrations’ endpoints in the form of tools.

Learn more about how Merge can support your AI features and products by scheduling a demo with an integration expert.