What is an AI Agent? A Guide for Customer Success Teams

The AI landscape is changing so fast that we fear this article may be outdated within minutes of its publication.

It’s hard to believe that the world was astonished by a chatbot that answered questions wrong 80% of the time a few years ago, and now entire teams are handing the keys over to AI. Even the keys to something as valuable and human-centric as Customer Success.

This guide breaks down exactly what AI agents are, how they differ from the automation you already use, where they deliver real value, and what to look for when evaluating one for your team.

What is an AI Agent?

B2B SaaS folks just love their jargon, don’t they? Of course, that’s probably rich coming from an industry that lives in terms like GRR, CSAT, and NPS. 

An AI agent is software that can take in information from multiple sources, decide what to do with it, and act without a human walking it through every step. 

That's the short version. But the long version matters because “agent” is becoming a bit of a buzzword these days and is often applied to use cases that aren’t agentic.

The real difference is that agents are not constrained to a predetermined set of rules or to a single user interface. 

AI agents are capable of acting as you would: they can make decisions, carry out actions, and learn as they go. But even more than that, an AI agent can operate with autonomy that chatbots cannot. You need to be careful when prompting an LLM to make sure that you generate the right output, but with an agent, you don’t have to give explicit guidance for each individual step.

AI Agent vs. Chatbot: Which Should CSMs Use?

Ideally both!

As mentioned above, there’s a world of difference between a chatbot and an agent. They still have their uses for Customer Success, though.

Chatbots are great for tasks that require clearly defined outputs. Generating an email, asking questions about a dataset, and editing content are all great tasks for ChatGPT and Claude. That’s because they are tasks that rely on clearly defined instructions and provide a clearly defined output. 

On the flip side, AI agents are great for open-ended tasks, those that require less oversight at the individual step level, or those that require access to multiple tools. Cross-referencing your CRM, support data, and product usage data to identify churn risks and expansion opportunities every week would be a good task for an agent. 

You could compile the data yourself and copy it into Claude, but an autonomous agent could perform this task regularly and update you with the output each time it runs.

Here’s a quick breakdown of when you might want to consider a chatbot versus an AI agent.

Top Use Cases for AI Agents in Customer Success

The use case for a chatbot is easy to grasp, but the idea of letting an AI agent run wild with your accounts is enough to give most CSMs pause. Not to mention the blank page syndrome that comes with being able to automate almost anything. 

Where do you start?

We’d recommend the following:

• Churn prediction and management
• Expansion
• Onboarding
• QBR prep

Use AI Agents for Churn Prediction

Almost every big churn can be accompanied by “we should have seen it coming.”

Well, with AI, you can predict churn before it even happens and take steps to mitigate churn before it becomes a problem.

You should have a sense of why accounts churn and what the early signs are. Whether it’s terse replies to your outreach, drops in usage, or requests to export data, take time to tabulate all of the early warning signs that churn is on the horizon. Then, leverage an AI agent to continuously monitor those signals and flag accounts that are quietly slipping. 

Example Use Case: An AI agent could watch login patterns at the user level, not just the account level. When the three power users on an account drop from daily to weekly logins, while overall account usage looks fine because new users came online, the agent can flag it as a leading indicator of champion disengagement before the aggregate metric moves.

Check out our guide to using AI for churn management for more ideas.

Use AI for Expansion

The flipside of churn is expansion. Which of your accounts are prime candidates for an upsell?

Just like churn, expansion opportunities usually hide in plain sight: 

• A team hits its seat limit. 
• A customer starts using a feature that serves as a leading indicator for the next tier.
• A new department logs in for the first time. 

These are buying signals, but they only become opportunities if you notice. It’s the same as spotting churn risks: rather than switching five different tabs to find the needle in the haystack, use AI to do the boring work for you.

Example Use Case: Usage-based expansion triggers can run automatically. When a customer hits 85% of their seat limit, 90% of their API quota, or starts inviting users from a department not in the original deal, the agent can draft an expansion conversation starter and notify you for approval before sending it.

Use AI to Automate Onboarding

Onboarding has the potential to be a revenue multiplier for your company, yet it often feels like the part where CS wastes the most time.

With enough cycles, you should have a sense of what it takes for an account to be successful: The steps new customers need to take, the features they need to activate, and how long it takes to see value in your product.

If you’ve mapped that out, then why not automate it?

An AI agent can own the operational layer of onboarding: tracking which milestones are complete, nudging the customer (or the CSM) when something is overdue, drafting the next status update, and surfacing risks like "the technical contact hasn't logged in since the kickoff." 

Example Use Case: Let the AI agent own the kickoff prep. After the deal closes, an AI agent can auto-generate a kickoff deck pulling the customer's stated goals from the sales handoff notes, their tech stack from the discovery call, and a tailored implementation timeline. This frees up critical hours of your time and lets you hit the ground running to deliver value for a new customer.

Use AI For QBR Prep

Most CSMs walk into QBRs and check-ins under-prepared, not because they don't care, but because preparing properly for five meetings a day means an hour of digging through their CSP, Salesforce, Gong, and Slack for each one. The math doesn't work.

An AI agent can produce a pre-meeting brief in seconds: recent product usage trends, open support tickets, last quarter's stated goals and progress against them, sentiment from the most recent calls, and three suggested talking points. It's the prep a CSM would do if they had unlimited time, which none do.

Example Use Case: Before a QBR, an agent can generate a first draft of the deck. If the agent has access to your calendar, this can even run on a set schedule before every QBR. With access to key data, the agent can pull in usage data, calculate ROI, surface trends, and identify underutilized features. All without you having to lift a finger. Check out our guide to leveraging AI for QBR prep for more ideas.

What to Look for in an AI Agent for Your CS Team

Again, at the risk of this article being outdated, we’ll tread lightly on specific features and instead focus on higher-level qualities to look for in a tool. 

There are different philosophies on AI. Some platforms jam AI into every nook and cranny of the software, whether it adds value or not. Some platforms are little more than wrappers for OpenAI/Anthropic.

To sift through them all, we recommend working through the following criteria:

• Is it an AI Agent or an AI Wrapper?
• How deep are the integrations?
• Is it customizable to your processes?
• Can a human intervene?

AI Agent or AI Wrapper?

There’s a big difference between AI that just surfaces insights and AI that can actually do something with those insights.

ChatGPT can read a call transcript and draft a follow-up email, but an AI agent can listen to a call, create tasks, and draft a follow-up email for approval. It can even update the account’s health score, all without you needing to lift a finger.

Pay attention to the actual capabilities – and not just marketing promises –  of a platform when evaluating it. 

Data Integration Depth

An AI agent is only as smart as the data it can see. 

Today’s CS teams need sales, product, and support data to make accurate decisions on behalf of their customers. As a result, you need a platform that brings all of your company’s data under one roof and makes it accessible to an AI agent to make decisions with the full context in view. 

When you evaluate an agent, ask what it actually connects to — product analytics, support platform, CRM, billing, email, calls, and any tools your CSMs live in — and how it reconciles conflicting data across those sources.

Customizability to Your CS Processes

Your definition of a healthy account, your onboarding milestones, your escalation criteria, and your expansion playbooks are not industry standard, and an agent that can't be configured to match them will either get ignored or force your team to bend their process to the tool.

Evaluate how much you can configure the following: 

• Health score logic
• The events the agent monitors
• The templates it uses for outreach
• The rules for when it acts versus when it asks

One-size-fits-all agents demo well and deploy poorly.

Human-in-the-Loop Controls

More autonomy is not always better. 

An agent that sends emails to your largest account without anyone reviewing them is a major churn waiting to happen. While AI can be a great timesaver, it’s important to first establish which actions an agent can take autonomously versus which actions require human oversight and approval.

Look for granular controls over what the agent can do unsupervised, what requires CSM approval, and what gets escalated to a manager. More autonomy means more leverage and more risk; the right answer is rarely "full autopilot" or "approve every action." Instead, it's a thoughtful split that you can tune as trust builds.

The Best Agentic Tools for Customer Success

You know how to evaluate platforms, so let’s review the top ones we recommend so you can start leveraging AI in your CS process.

Your two primary options are:

• All-purpose AI tools
• Specialized software for Customer Success

We’ll share examples of each.

All-Purpose AI Tools (LLMs)

The first bucket, and arguably the easiest to start with, is going to be all-purpose AI tools. You’re probably familiar with ChatGPT and Claude, but remember that those are chatbot tools. Not agents.

The agentic tools to try will be:

• Claude Cowork: A desktop version of Claude that can access files on your computer and your browser windows. It’s also capable of calling APIs and accessing apps through integrated connections.
• Claude Code: A plugin for your terminal or coding platform of choice, Claude Code can generate code and run processes from your computer.
• Codex (OpenAI): Like Claude Code, an agentic coding tool that can generate code and run processes.
• Workspace Agents (OpenAI, available for enterprise): Powered by Codex, these are agents that can run complex processes from ChatGPT.

We use the term “all-purpose” because these tools serve a broad set of use cases and are not built specifically for Customer Success. That means you’ll need to take care when providing them access to data and instructing them on tasks.

Still, they’re fairly cost-effective to start with and could be a good option for testing.

Specialized Software for Customer Success

All-purpose LLMs are a great way to dip your toes in, but they come up short for CS teams that want to deploy AI at scale. You'll spend half your time copy-pasting context into prompts, and you still won't have an agent that knows the difference between a power user going quiet and a new department onboarding.

For that, you need software built with CS in mind.

For example, Vitally’s AI-powered Customer Success Platform is a purpose-built platform for scaled CS. Vitally AI takes all of your data – notes, transcripts, product usage, support tickets, NPS, CRM data, and more –  and turns it into structured insights to power your team. Here’s what sets Vitally apart from other AI-powered Customer Success software:

• Native AI, not a wrapper: Vitally AI doesn't just summarize a call transcript and call it a day. It can listen to the call, draft follow-up tasks, update the account's health score based on sentiment, and surface the conversation in your next QBR prep without you stitching those steps together yourself.
• Deep integrations: An agent is only as smart as the data it sees. Vitally pulls in product usage, support tickets, CRM data, NPS, call transcripts, notes, and email, and reconciles them into a single view of the account. 
• Configurable to your processes: Health score logic, the events the agent monitors, the playbooks it runs against, the templates it drafts from – all of it bends to your CS process rather than forcing you to bend to the tool. 

Vitally also gives you granular control over what runs autonomously and what requires approval. The result is an agent that does the boring work – pulling data, spotting patterns, drafting first passes – so you can focus on the part of CS that actually requires a human.

Power Your CS Workflows With the Customer Success AI Prompt Book

AI agents are the latest way that CS teams can automate processes and deliver a stellar customer experience at scale. 

But we understand if handing the keys to your accounts over to automated software gives you pause. You can take a meaningful step forward by incorporating AI into your workflow gradually to do things like:

• Summarize customer calls
• Prep for a QBR meeting
• Update health scores
• Generate follow-up emails based on a transcript

We compiled a list of our favorite prompts to automate your CS workflows, from spotting churn risks to meeting follow-up. Save your favorite prompts, create a custom GPT, and see how much time you can save across all of your accounts.

Click the button below to get your free copy.