Nobody wants to babysit a chatbot. When customer questions go beyond basic FAQs, support teams need AI that actually helps, not one that needs constant hand holding.
That is why AI agents have started to replace traditional chatbots in serious support setups. Intercom’s Fin is one example, an AI agent designed to actually understand what customers are saying, pull the right info, and take action when needed.
But if you are looking for something more customizable or something that won’t break the bank and doesn’t charge per-agent pricing, solutions like eesel AI offer a flexible alternative. It connects to any platform you work with, and can be trained to handle exactly the kind of support your team needs.
How Intercom AI agents actually work
AI agents are designed to go beyond simple keyword matching. When a customer message comes in, the agent first tries to understand the intent behind the question. It then checks any available context, like past conversations or user data, to tailor its response more accurately.
Once it has the full picture, the agent searches across connected sources such as help center articles, past support tickets, and internal documentation to find the most relevant information. If the question is straightforward, the agent can resolve it right away. For more involved issues, it may trigger system actions or route the conversation to a human agent with full context included.
These systems become more effective over time because they learn from each interaction. As the agent handles more queries, it identifies which responses are successful and adjusts accordingly. This ongoing feedback loop helps improve the accuracy and usefulness of future replies. However, without control over specifically which data it should be searching, responses can easily get diluted by previous responses or outdated information.
This flow shows how an AI agent moves from understanding the message to resolving it or escalating when needed.
| Feature | What it does | Real example |
|---|---|---|
| Multi-source learning | Combines answers from different systems | Pulling info from past tickets, docs, and internal notes |
| Personalization | Uses customer history to adjust replies | Referring to a previous refund request |
| Action handling | Does more than reply — it acts | Updating billing info or canceling a subscription |
| Language support | Works in 45 languages | Consistent support for global customers |
| Learning engine | Able to access and reference replies in previous conversations | Replicates support response to a bug question |
While Intercom’s AI agent generally follows this process, tools like eesel AI use similar principles with more flexibility. You can connect multiple knowledge sources, set up different bots for different roles, and use the platform across a variety of tools beyond just a single help desk. eesel AI can also train on past tickets, but with the added control measure of choosing exactly which tickets, from which agents, and from which date range, to ensure the most accurate and up to date information.
AI agents vs chatbots
The terms “AI agent” and “chatbot” are often used interchangeably, but they work in very different ways. Understanding the gap between the two helps support teams choose the right tool for the job.
| Capability | Traditional chatbots | AI agents |
|---|---|---|
| Understanding | Relies on keywords and preset patterns | Understands natural language and context |
| Learning | Follows a fixed decision tree | Learns and improves from real interactions |
| Actions | Can only respond with preset answers | Can trigger actions and connect with systems |
| Escalation | Escalates based on simple rules | Hands off with full context and history |
These differences have a big impact on the customer experience. A basic chatbot might answer common questions about pricing or hours of operation. But it will likely fall short when someone says, “I want to cancel my subscription and get a refund for last month.” That kind of request has multiple parts and needs a system that understands both the context and the actions required.
AI agents are built to handle that complexity. They can recognize intent, pull up the right information, and complete the necessary steps—sometimes without needing a human to step in.
Some tools, such as eesel AI, are designed to maintain the full conversation history even when escalating to a human. This helps support agents pick up right where the AI left off, without making the customer repeat themselves. It also gives teams more control over how and when handoffs happen, with options to set rules based on topic, sentiment, or complexity. Actions like refunds can occur within the conversation without needing the agent to step in at all.
Setting up your Intercom AI agent
Getting started with an AI agent takes more than just flipping a switch. To make it work well, you need the right setup and a clear understanding of what you want it to do. Without configuring Intercom Fin correctly, the bot’s suggestions will become more of a nuisance than a help.
What you need before you begin
Start by gathering your core materials. This includes an active Intercom subscription with admin access, knowledge base, and defined rules for when the AI should escalate to a person
Once you have that ready, connect your sources inside Intercom. That means importing help center articles, uploading internal docs, and syncing previous customer conversations.
Then, set up your communication channels. This might include your website chat widget, email, or social media accounts. Make sure to also put the right privacy and security controls in place to protect customer data.
Training your AI agent effectively
Start by uploading all your sources of data. This includes help center content, internal guides, and a sample of real customer conversations. Intercom reports that AI agents trained on high-quality data can solve over 86% of all incoming tickets right away.
During training, make sure to cover a few key areas. Start with common customer questions and what the correct responses should look like. This helps the AI recognize patterns and respond more accurately. Next, define your brand’s tone and voice so the AI speaks in a way that feels natural for your company. You should also set clear rules for when the AI should pass the conversation to a human. And if your support covers multiple regions, configure the AI to respond in the necessary languages.
Pro tip: Test everything in a closed environment before going live. This lets you catch weak spots in the responses without affecting real customers.
Customization and optimization
Once your Intercom AI agent is trained, the next step is making sure it fits your brand and support goals.
Start by adjusting the tone. Whether your brand voice is professional, casual, or somewhere in between, your AI should reflect that in every response. This creates a consistent experience for your customers.
Then, look at your workflows. Set up how tickets are routed, how priorities are assigned, and when conversations should escalate to a person. These rules help the AI handle support in a way that matches how your team already works.
For teams that need more control or faster setup times, tools like eesel AI offer more flexibility. You can build different bots for different tasks, connect to a wider range of platforms, and start seeing results in as little as one to two weeks.
Key metrics to track
Once your AI agent is up and running, it is important to know if it is actually helping. Tracking the right metrics gives you a clear view of what is working and where to improve.
Here are a few to keep an eye on:
- Resolution rate: Look at how many tickets are solved without a human stepping in. Higher numbers mean your AI is doing its job well and saving your team time.
- Response quality: Check how accurate and helpful the answers are. You can review chats directly or use customer feedback to gauge this.
- Operational efficiency: Track how quickly tickets are being handled and whether your team is able to focus on more complex issues. If your AI is handling the routine stuff, you should see gains here.
Over time, these metrics help you fine-tune how your AI agent works. You can adjust training, update your workflows, or set new rules to improve performance and customer experience.
Challenges with Intercom
Intercom’s AI agent can be a powerful tool, but like any system, it has its limits.
One of the most common challenges is managing multi part requests. These often include follow ups or layered questions that require context or actions beyond a simple reply. While the AI handles many of these well, some still need human input.
Another area where teams hit friction is in reading tone. A message may look polite but carry frustration or urgency that a person would pick up on more easily than an AI.
Customization can also be a hurdle. Setting up workflows, adjusting escalation rules, or connecting to certain systems often takes extra time or technical know-how, especially as support needs evolve.
AI is not available on the entry-level plan, and usage-based charges apply. For teams handling a growing number of tickets, this can become a cost consideration as resolution volumes increase.
How eesel AI improves Intercom
While Intercom’s native AI offers strong automation capabilities, some teams may need more control, customization, or integration options. Tools like eesel AI are built to work alongside your help desk and offer expanded flexibility, especially when it comes to connecting across platforms, tailoring workflows, and managing cost.
| Feature | eesel AI | Intercom AI |
|---|---|---|
| Pricing model | Pay per interaction | Charges based on number of resolutions |
| Knowledge sources | Pulls from multiple platforms and docs | Primarily Intercom-hosted content |
| Bot customization | Set up multiple specialized bots | Single bot configuration |
| Resolution time | Processes in real time with learned context | Varies depending on setup and usage |
| Language support | Supports over 100 languages | Supports over 45 languages |
| Training timeline | Typically ready in 1 to 2 weeks | Often takes 2 to 4 weeks to configure and train |
eesel AI is designed to connect with the tools your team already uses such as Google Docs, Confluence, or Slack and pulls in relevant content to help resolve customer issues faster. It learns from historical tickets to improve accuracy over time, and keeps full conversation context across interactions, even when escalated to a human.
You can create multiple individual bots to work separately, or create a bot network for communication between different product expert bots or different departments. Let the bots take actions, whether that is handling technical questions, processing account changes, or managing returns. This structure gives teams more flexibility and helps maintain accuracy as your support needs grow.
Start building with the right AI tools
Bringing AI into your support workflow is not about replacing your team, it is about giving them more space to focus on what really matters. Whether you are working with Intercom’s built-in AI or exploring more flexible tools like eesel AI, what matters most is choosing a setup that fits how your team works.
Start with one process. One queue. One use case. Measure how it performs, adjust as needed, and build from there.
If your team needs more customization, broader integrations, or a more predictable pricing model, eesel AI offers a way to expand what AI can do—without locking into platform limitations.
You can book a demo, start a free trial, or reach out directly at hi@eesel.app to learn more.
