Why Integrations Are Critical for AI Customer Service Agents

Of all the gen-AI applications, support automation is where AI companies have seen the most product-market fit. Klarna reported that their AI assistant handled 2/3 of all requests in the first month they deployed it, and Dukaan replaced 90% of their support staff with an AI support chatbot.

We won’t get into the philosophical debate of job displacement here, but whether you’re building an AI product boost your customers’ support staff productivity or replace most of them, there are now a lot of companies all trying to bite off a share of this massive market.

However, many businesses find themselves still underwhelmed by the depth support agents can go and in many cases still need a human to validate and pull information in 3rd party apps.

In this article, we'll explore why integrations and 3rd party agentic actions are the key to unlocking the true potential of AI support agents/chatbot products.

Note: For the purposes of this article, we’ll refer to your customers’ customers (the ones asking questions to the AI agent) as ‘end-customers’.

TL;DR

To win in the AI support space, integrations will be critical for a few reasons:

1. Contextual Intelligence: By ingesting data from your customers’ CRMs, knowledge bases, inboxes, ticketing systems, product usage platforms, and even their Slack accounts, your AI support agents will have full context on your customers’ product(s) and the end-customers they’re resolving queries for.

2. Operational Efficiency: Integrations enable AI agents to automate a wide range of administrative tasks, from logging conversations in your customers’ CRMs, creating and/or closing tickets in 3rd party ticketing platforms, or notifying human agents with a summary in Slack/Teams. This removes the need for a human-in-the-loop to perform all these ancillary tasks.

3. Omnichannel Support: With proper integrations, AI agents can provide consistent support across multiple channels (chat, email, social media, voice) while maintaining context and continuity.

Context on the product and the customer

AI support agents are only as effective as the information they can retrieve. Without proper integrations, these agents operate in a silo, lacking the context needed to provide truly helpful support. Today, many AI support agent products simply provide a few text boxes where users can add basic information about their products/services, and at best, a file upload feature.

This creates a huge gap not only in the overall knowledge that your customers have accumulated on their products, common questions, and other ‘institutional knowledge’, but also completely misses any information on the actual end-user asking questions.

Our customers that are in the AI support space are ingesting data from many sources for the RAG processes behind their product, and here are a few of the most common use cases:

• CRMs: The AI agents need to have all the context and history on the customer who is asking a question, in order to handle the conversation and ticket effectively. With CRM integrations, the AI agents can gain access to the entire history of the customer’s journey with their product/company, including detailed account information that may be used for personalization. In practice, this will result in the support agent leveraging different talk tracks and answers to prospects vs. existing customers.

• Knowledge bases: Just like how human support agents often reference their internal knowledge base to find the best answer to a customer’s question, your AI agent product needs to be able to access that same repository of information. Integrations with knowledge bases that live in SharePoint, Notion, Intercom, etc. enables your AI support agent to tap into every piece of knowledge that is available, enabling it to provide answers with an even better level of accuracy than a human manually parsing through all of that information.

• Ticketing systems: Ticketing system integrations give AI agents insight into ongoing issues and their current status, allowing for seamless continuity in problem-solving across multiple interactions or agents. For example, if an end-customer is following up on a previous request, your AI support agent can check your customers’ ticketing systems to get the context from the previous tickets.

• Platform data: Whether your customers are SaaS companies that have product analytics set up, or even if they’re e-commerce companies with web analytics data, an end-customer’s activity in your customer’s platform can give your AI agent insight into where they may have gotten stuck.

By ingesting all of this data, your AI support agents will be able to match the personalization and context that can not only rival, but surpass the abilities of your customers’ real life agents - with response times that are 10x-100x faster.

Automating Tasks

Just answering questions is not enough however. There exists a significant volume of administrative work that accompanies each end-customer interaction. This is where 3rd party agentic actions come into play. By equipping your AI agents with access to update 3rd party apps, they can fully automate the end-to-end workflow of your customers’ real-life support agents. In a copilot model, this means your AI support agent can effortlessly handle various administrative tasks, freeing the actual support staff to focus on helping more customers.

Here are a few examples:

• Updating CRMs: Once a conversation ends, your AI support agent can automatically push detailed, AI-generated summaries of each conversation directly into the CRM, ensuring that customer records are always up-to-date without manual input.

• Ticket creations: Ticket creation and management is critical for monitoring and analytics, so it’s important that as conversations take place, your AI support agent can create, update, and categorize support tickets in your customers’ 3rd party ticketing platforms based on the content of interactions, all without human intervention. This is also important if you want to be able to generate ticket IDs to share with the end-customer within the conversation.

• Notifications in Slack: By integrating with team communication platforms like Slack or Microsoft Teams, AI agents can instantly alert relevant team members about critical issues or necessary escalations, ensuring that urgent matters receive immediate attention.

These integrations not only save countless hours of administrative work but also ensure that no valuable information falls through the cracks, leading to a more efficient, responsive, and data-driven support operation.

We actually built a lightweight demo of an AI Customer Service Agent that can handle all three of these AI actions using Llamindex + Paragon, you can check out the video and details here.

The Future of AI Support: Integrations as a Competitive Advantage

The AI support agent market is growing quickly with global spending on AI-powered customer service solutions projected to increase significantly in the coming years. However, as organizations move past initial pilots and evaluate long-term ROI, the market is likely to consolidate around solutions that deliver measurable business impact, and integrations will become a critical requirement.

To build these 3rd party context-ingestion pipelines and agentic automations will require a lot of engineering work. That’s why AI companies are using Paragon’s embedded integration infrastructure to offload the plumbing of all these integrations, enabling them to go-to-market with new integrations 10x faster. Learn more about how you can use Paragon to ingest context and build agentic actions, and book a demo with our team to see a more tailored example for your use case.