APIs Archives

1. API Discoverability and Graph-Based RESTful APIs

Data access is crucial for AI agents tasked with understanding and responding to complex customer inquiries. Modern LLM developer tools should facilitate understanding and access through APIs that are well defined with JSON-LD, GraphQL, or OpenAPI spec. These API protocols enable AI agents to dynamically query and interpret interconnected data structures. The more discoverable your APIs, the easier it becomes for your AI to provide personalized and accurate service.
Similar to human agents onboarding to your support team, AI agents need access and understanding of your system data to provide relevant and accurate customer service.

2. Design by Contract with AI Function Calling

Ensuring reliable AI-to-system interactions requires strict compliance with well-defined operational rules. This is where the practice of design by contract proves invaluable. The best LLM tools should establish clear contracts for AI functions, ensuring that each interaction occurs within its designated boundaries and yields the expected outcomes. This structured approach minimizes errors and enhances the reliability of AI agents by mandating validation checks when reading or writing data.
Your LLM toolkit should promote and enforce a defined data schema for your AI agents. For more insights, refer to Quiq’s exploration of this topic in their LLM Function Calling post.

3. Functional and Aspect-Oriented Programming

Functional programming emphasizes pure functions and immutability, and when combined with aspect-oriented programming, which tackles cross-cutting concerns, it establishes robust and scalable frameworks ideal for AI development.

Modern LLM toolkits that embrace these paradigms offer sophisticated tools for constructing more resilient cognitive architectures. These components can be independently developed, tested, and reused, making them ideal for assembling complex AI agents, including agent swarms. Agent swarms, consisting of multiple AI agents working in concert, benefit particularly from an atomic, yet cohesive approach to decision making. Your design choices will become crucial as the demands of customer interactions grow more complex over time.

4. Observability: Ensuring Transparency and Performance

Your LLM toolkit should offer comprehensive monitoring capabilities that allow developers and business operators to track how AI agents make decisions. These tools should enable high level and deep dive analysis that clearly shows how inputs are processed and decisions are formulated. This level of transparency is crucial for troubleshooting and optimizing performance.

By offering detailed insights into AI performance and behavior, modern LLM toolkits play a critical role in helping businesses maintain high service quality and build trust in their AI-driven solutions. The ability to trace how and why a message was delivered or an action taken has never been so important, and top LLM dev tools provide it. Traditional logging and APM software won’t cut it in the era of stochastic AI. Please see Quiq’s LLM Observability post for a deeper discussion on the topic.

5. Continuous Integration

Continuous integration (CI) systems within LLM toolkits play an important role in development, testing, integration, and deployment of AI agents. Your toolkit should ensure agents adapt correctly to changes in models, data, logic or your system at large. LLM toolkits that oversee the lifecycle of AI agents will need to be resilient to updates and iterative improvements based on real-world scenarios and emerging capabilities of the models.

Additionally, modern LLM toolkits, such as those highlighted in Quiq’s AI Studio Debug Workbench, should provide an environment for running a wide range of scenarios. This includes allowing developers to closely inspect, recreate and replay AI behavior on-demand or test-time. You will need to be well informed and react quickly and confidently across the lifecycle of your project.

Remaining Skeptical in the Era of AI

As a software developer with 20 years of experience, I’ve found that a healthy dose of skepticism and reliance on time-tested practices have helped me remain focused on building robust solutions. Not only has this experience proven effective over the years, but it has also laid a strong foundation for my journey as an Applied AI Engineer.

However, LLMs present new challenges that traditional tools and techniques alone can’t fully address. To unlock the potential of these models, we must remain adaptable and open to integrating new tools, techniques and tactics. While I still often use Emacs for editing, I’ve also come to fully embrace the LLM toolkit equipped with a visual procode interface that promotes solid engineering practices. An LLM toolkit will not erase the need for your software engineering practices, but it does provide me, my team and our customers with the tools necessary to unlock the power of AI in an enterprise environment.

Finally, tools like AI Studio offer a surface where we can collaborate with our counterparts across the business to help grow AI that is well understood, reliable, and impactful. Without collaboration, an AI initiative will likely grind to a halt. You will need some new tools to help you bridge the gap.

To learn more about how Quiq is helping software engineers, operational teams and business leaders find the intersection of AI in 2025, learn more about AI Studio.

Large language models hold a lot of power to improve your customer experience and make your agents more effective, but they won’t do you much good if you don’t have a way to actually access them.

This is where application programming interfaces (APIs) come into play. If you want to leverage LLMs, you’ll either have to build one in-house, use an AI API deployment to interact with an external model, or go with a customer-centric AI for CX platform. The latter choice is most ideal because it offers a guided building environment that removes complexity while providing the tools you need for scalability, observability, hallucination prevention, and more.

From a cost and ease-of-use perspective this third option is almost always best, but there are many misconceptions that could potentially stand in the way of AI API adoption.

In fact, a stronger claim is warranted: to maximize AI API effectiveness, you need a platform to orchestrate between AI, your business logic, and the rest of your CX stack.

Otherwise, it’s useless.

This article aims to bridge the gap between what CX leaders might think is required to integrate a platform, and what’s actually involved. By the end, you’ll understand what APIs are, their role in personalization and scalability, and why they work best in the context of a customer-centric AI for CX platform.

How APIs Facilitate Access to AI Capabilities

Let’s start by defining an API. As the name suggests, APIs are essentially structured protocols that allow two systems (“applications”) to communicate with one another (“interface”). For instance, if you’re using a third-party CRM to track your contacts, you’ll probably update it through an API.

All the well-known foundation model providers (e.g., OpenAI, Anthropic, etc.) have a real-world AI API implementation that allows you to use their service. For an AI API practical example, let’s look at OpenAI’s documentation:

(Let’s take a second to understand what we’re looking at. Don’t worry – we’ll break it down for you. Understanding the basics will give you a sense for what your engineers will be doing.)

The top line points us to a URL where we can access OpenAI’s models, and the next three lines require us to pass in an API key (which is kind of like a password giving access to the platform), our organization ID (a unique designator for our particular company, not unlike a username), and a project ID (a way to refer to this specific project, useful if you’re working on a few different projects at once).

This is only one example, but you can reasonably assume that most protocols built according to AI API best practices will have a similar structure.

This alone isn’t enough to support most AI API use cases, but it illustrates the key takeaway of this section: APIs are attractive because they make it easy to access the capabilities of LLMs without needing to manage them on your own infrastructure, though they’re still best when used as part of a move to a customer-centric AI orchestration platform.

How Do APIs Facilitate Customer Support AI Assistants?

It’s good to understand what APIs are used for in AI assistants. It’s pretty straightforward—here’s the bulk of it:

Personalizing customer communications: One of the most exciting real-world benefits of AI is that it enables personalization at scale because you can integrate an LLM with trusted systems containing customer profiles, transaction data, etc., which can be incorporated into a model’s reply. So, for example, when a customer asks for shipping information, you’re not limited to generic responses like “your item will be shipped within 3 days of your order date.” Instead, you can take a more customer-centric approach and offer specific details, such as, “The order for your new couch was placed on Monday, and will be sent out on Wednesday. According to your location, we expect that it’ll arrive by Friday. Would you like to select a delivery window or upgrade to white glove service?”
Improving response quality: Generative AI is plagued by a tendency to fabricate information. With an AI API, work can be decomposed into smaller, concrete tasks before being passed to an LLM, which improves performance. You can also do other things to get better outputs, such as create bespoke modifications of the prompt that change the model’s tone, the length of its reply, etc.
Scalability and flexibility in deployment: A good customer-centric, AI-for-CX platform will offer volume-based pricing, meaning you can scale up or down as needed. If customer issues are coming in thick and fast (such as might occur during a new product release, or over a holiday), just keep passing them to the API while paying a bit more for the increased load; if things are quiet because it’s 2 a.m., the API just sits there, waiting to spring into action when required and costing you very little.
Analyzing customer feedback and sentiment: Incredible insights are waiting within your spreadsheets and databases, if you only know how to find them. This, too, is something APIs help with. If, for example, you need to unify measurements across your organization to send them to a VOC (voice of customer) platform, you can do that with an API.

Looking Beyond an API for AI Assistants

For all this, it’s worth pointing out that there’s still many real-world AI API challenges. By far the quickest way to begin building an AI assistant for CX is to pair with a customer-centric AI platform that removes as much of the difficulty as possible.

The best such platforms not only allow you to utilize a bevy of underlying LLM models, they also facilitate gathering and analyzing data, monitoring and supporting your agents, and automating substantial parts of your workflow.

Crucially, almost all of those critical tasks are facilitated through APIs, but they can be united in a good platform.

3 Common Misconceptions about Customer-Centric AI for CX Platforms.

Now, let’s address some of the biggest myths surrounding the use of AI orchestration platforms.

Myth 1: Working with a customer-centric AI for CX Platform Will be a Hassle

Some CX leaders may worry that working with a platform will be too difficult. There are challenges, to be sure, but a well-designed platform with an intuitive user interface is easy to slip into a broader engineering project.

Such platforms are designed to support easy integration with existing systems, and they generally have ample documentation available to make this task as straightforward as possible.

Myth 2: AI Platforms Cost Too Much

Another concern CX leaders have is the cost of using an AI orchestration platform. Platform costs can add up over time, but this pales in comparison to the cost of building in-house solutions. Not to mention the potential costs associated with the risks that come with building AI in an environment that doesn’t protect you from things like hallucinations.

When you weigh all the factors impacting your decision to use AI in your contact center, the long-run return on using an AI orchestration platform is almost always better.

Myth 3: Customer-Centric AI Platforms are Just Too Insecure

The smart CX leader always has one eye on the overall security of their enterprise, so they may be worried about vulnerabilities introduced by using an AI platform.

This is a perfectly reasonable concern. If you’re trying to choose between a few different providers, it’s worth investigating the security measures they’ve implemented. Specifically, you want to figure out what data encryption and protection protocols they use, and how they think about compliance with industry standards and regulations.

At a minimum, the provider should be taking basic steps to make sure data transmitted to the platform isn’t exposed.

Is an AI Platform Right for Me?

With a platform focused on optimizing CX outcomes, you can quickly bring the awesome power and flexibility of generative AI into your contact center – without ever spinning up a server or fretting over what “backpropagation” means. To the best of our knowledge, this is the cheapest and fastest way to demo this API technology in your workflow to determine whether it warrants a deeper investment.

To parse out more generative AI facts from fiction, download our e-book on AI misconceptions and how to overcome them. If you’re concerned about hallucinations, data privacy, and similar issues, you won’t find a better one-stop read!

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