Beyond basic bots: Use cases for agentic AI in customer support

As a customer support manager, you wear a lot of hats — you need to improve systems and boost satisfaction, all while keeping your team from burning out. You can hire more people to lighten the workload, but that hurts your budget and creates a larger, harder-to-manage team. 

With agentic AI, parts of your support system can take action and solve problems on their own. These platforms are more powerful than old-school support bots that just point customers to knowledge base articles. An AI agent can read internal support resources, understand the customer’s problem, and find a solution or escalate the ticket.

In this guide, you’ll learn about the different types of agentic AI, from simple assistants to fully independent systems. Then we’ll look at common agentic AI use cases to show how you can use this tech to provide better B2B support.

What’s agentic artificial intelligence?

Agentic AI (often called an agentic system) is a program that can understand its environment well enough to make decisions and take actions based on set goals. Unlike basic AI that organizes data or generates text, agentic AI can work independently to complete complex, multi-step tasks.

Here are the core features of agentic AI:

• Autonomy: The ability to handle tasks without your team’s input.
• Perception: How the AI collects and understands data from its environment, like the text in support tickets or customer activities in an app.
• Reasoning: The skill needed to create plans that line up with set goals, like immediately assigning tickets to the right people.
• Action: How the AI uses tools to complete its tasks, like by calling an API or updating a database.

Agentic AI isn’t new, but the rise of large language models makes it a more practical tool for modern companies. According to MIT Sloan Management, agentic AI has already reached 35% adoption, and another 44% of organizations plan to use it soon. 

Gartner’s research shows similar results, predicting that 40% of enterprise applications will involve task-specific AI agentic models by the end of 2026. Gartner also outlines a five-stage maturity model that shows how agentic AI has changed and where it’s going:

1. AI assistants: Basic helpers embedded in apps that need human input.
2. Task-specific agents: Agents that can perform end-to-end tasks independently.
3. Collaborative agents: Multiple agents that work together within a single system.
4. Agent ecosystems: Agents that can collaborate across different tools.
5. Democratized apps: A future stage where knowledge workers can create agents on demand.

Maybe your support team already uses basic chatbots or AI assistants. But with task-specific and collaborative agents, you can dramatically improve response times and customer experiences without expanding your team.

How does agentic AI work?

Here are the core components that make an AI agent work: 

• Reasoning and planning. Devs configure an agent with a goal like solving support tickets. If the AI sees a ticket that reports “my API calls are failing,” it plans a logical sequence of steps. The agent reasons: “First, I’ll get the customer’s account details. Second, I’ll pull the API error logs. Third, I’ll compare the error pattern to known issues in documentation.”
• Tool use. Agents get access to specific sets of tools — typically APIs — that let them interact with other systems. So the agent can connect to customer relationship management software, ticketing systems, internal databases, and any other platforms where it can pull data needed to complete tasks. 
• Memory and learning. Agentic AI systems have short and long-term memory. Short-term memory lets them keep track of the current conversation and the steps they take. Long-term memory helps them learn from past interactions, so if an agent fixes an issue, it stores that solution for future use.

As these mechanics evolve, the agent moves up the Gartner maturity ladder we saw earlier. For instance, an agent that uses a single tool to perform one task is at Stage 2, while AI that can connect multiple systems to manage complex workflows is at Stage 3 or beyond. 

How can you use agentic AI to improve customer support? 

To get the most from agentic AI, start with use cases that offer immediate value to your team and customers. These examples show how you can use agentic AI to work smarter and promote customer success.

Fast troubleshooting

When a customer reports a technical error, the agent can access account data, server logs, databases, and internal APIs to find the root cause. Agentic AI identifies specific error codes, cross-references those codes with documentation, and gives the customer a clear solution. Because this process lets the AI fix a lot of issues independently, it cuts response times down and improves resolution rates.

Proactive support

By monitoring customer activity, an agent can see when a customer is stuck. For example, maybe the customer repeatedly tries and fails to configure a feature — some AI systems will spot this activity and reach out proactively. This can prevent frustration, reduce ticket volumes, improve customer satisfaction scores, and build the kind of trust that keeps accounts happy.

Cross-platform workflows

An agentic system can work across your entire omnichannel support platform. A customer could report an issue in a shared Slack channel, and that problem becomes a Jira ticket for your dev team. The AI agent manages the conversation in Slack, sends a summary email to the account manager, and lets the customer know when the issue is fixed.

Agentic AI in customer support: Case studies

For an even clearer picture of what agentic AI can do, here’s how some real companies use it.

SaaS integration

AssemblyAI, a developer that offers speech-to-text APIs, faced a common challenge in their industry: supporting devs who need fast, accurate answers. Their team was bogged down by repetitive and simple questions, which limited their ability to focus on complex issues. 

When they deployed a Pylon AI Agent named Sonny, AssemblyAI automated resolution for many of those common asks. They saw a 97% reduction in first response time and a 50% AI resolution rate.

Slack-first support

For B2B company Hightouch, Slack is a primary support channel. But with over 300 shared customer channels, manual triage became impossible. Hightouch’s support team spent hours context-switching between Slack and their ticketing system.

Hightouch adopted Pylon as their new omnichannel support platform, and added an AI agent in every channel to prompt customers in the support queue to self-serve. This change led to a 75% increase in customer self-service via Slack, and it saved the support team 100 hours per month.

Industry-specific benefits of agentic AI

Your industry and products will shape the way you use AI agents. For example, let’s see what B2B teams could focus on in two common niches:

• SaaS support. Time to resolution is a key metric in SaaS, since you’ll likely get a lot of troubleshooting tickets that need quick attention. If you automate the diagnostic and triage phases with agentic AI, you lower resolution times and free up your team to focus on other work.
• Fintech support. In regulated industries like fintech, good customer support depends on accuracy and auditability. You can reach those goals faster by giving AI agents easy access to up-to-date information, plus the controls and clearance needed to use that context in line with laws and policies.

Enhance your B2B support with Pylon’s agentic AI

Agentic AI doesn’t replace your support team, but it helps them work smarter and faster as you scale. With the right tools, you can leave routine tasks and simple tickets in the hands of AI agents, and free up your support team for more valuable work without adding headcount.

Pylon is the modern B2B support platform that offers true omnichannel support across Slack, Teams, email, chat, ticket forms, and more. Our AI Agents and Assistants automate busywork and reduce response times. Plus, with Account Intelligence that unifies scattered customer signals to calculate health scores and identify churn risk, we're built for customer success at scale.

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FAQ

How do enterprises measure ROI from agentic AI initiatives?

Enterprises measure success through automation rates, time saved, and labor cost reductions. Indirect value, such as improved customer satisfaction and operational agility, also influences ROI.

What governance structures are needed to deploy agentic AI safely?

Safe deployment requires identity-centric access controls, human-in-the-loop gates for high-stakes tasks, and real-time activity logging to ensure full traceability and accountability.

What technical prerequisites should organizations meet before adopting agentic AI?

To adopt agentic AI, organizations need unified data foundations, robust API connectivity, and rigorous security guardrails, including "kill-switch" protocols and agent-specific identity management systems.

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