AI Customer Support Agents: What They Do and How to Pick One

An AI customer support agent reads incoming questions, looks for the answer in your help centre and connected systems, and either replies or passes the case to a person. It works best on repetitive, well-documented questions, and it needs current content, a clear handover rule and regular review of what it actually told customers.

What the directory lists for support

The support category on BusinessMCP’s AI agents directory lists 13 agents. Decagon, Intercom Fin and Sierra are the editorially reviewed entries and appear first; the rest are alphabetical. The page gives tier labels, not dollar prices.

Agent	Maker	Tier	Described as
Decagon	Decagon	Enterprise	Integrates with internal tools to resolve complex issues end to end
Intercom Fin	Intercom	Paid	Resolves up to 80% of conversations using help-centre content
Sierra	Sierra	Enterprise	Conversational AI for complex, multi-turn conversations
Ada	Ada	Enterprise	AI-first service platform across channels
Botpress	Botpress	Freemium	Open-source chatbot builder with visual flow editor and LLM integration
Chatbase	Chatbase	Freemium	Chatbots trained on your data with website embedding
Forethought	Forethought	Enterprise	Generative AI to resolve, triage and assist on tickets
Freshdesk Freddy AI	Freshworks	Freemium	Triage, suggested replies and routine resolutions inside Freshdesk
Microsoft Bot Framework	Microsoft	Free	Open-source SDK for bots across Teams, Slack and web chat
Rasa	Rasa	Free	Open-source framework, deployable on-premise
Tidio AI (Lyro)	Tidio	Freemium	Small-business chatbot answering from website and FAQ content
Typebot	Typebot	Freemium	Open-source chatbot and conversational form builder
Zendesk AI	Zendesk	Paid	Ticket routing, resolution and agent assistance

These are the directory’s descriptions of vendor products, not our test results.

Three kinds of support agent

The table hides a useful split.

1. Builders. Botpress, Rasa, Typebot and Microsoft Bot Framework give developers parts to assemble. Cheap or free, but you do the engineering.
2. Helpdesk add-ons. Freddy AI and Zendesk AI sit inside tools you may already use, so setup is shorter and data is already connected.
3. Autonomous resolvers. Intercom Fin, Decagon and Sierra aim to resolve whole conversations. They cost more and need good content and integrations.

Key takeaway: the agent can only be as good as the help content behind it. If your articles are outdated, the agent will confidently repeat outdated answers.

Preparing your content

Before you switch anything on, audit the sources an agent will draw from.

• Top fifty questions. Pull them from recent tickets and check each has a current, clear answer.
• Policies. Write refund, shipping and warranty rules in plain language with exact limits.
• Exceptions. Document who handles what the agent must not.
• Tone. Give examples of replies you like and ones you do not.

Content work is usually the largest part of setup. It also helps human agents, so the effort is not wasted if you later switch tools.

Handover design

The most important rule in a support agent is when it stops. Define triggers such as:

• The customer asks for a person.
• The topic is billing disputes, safety, legal threats or account security.
• The agent’s confidence is low or the customer repeats the question.
• Sentiment is angry or distressed.

Pass the whole conversation to the human so the customer does not repeat themselves. A bad handover turns a time-saver into a complaint.

Measuring success without hurting satisfaction

Track the numbers that tell the real story.

Metric	What it tells you
Resolution rate	Share of conversations closed without a person
Reopen rate	Whether “resolved” answers actually helped
Handover rate	How often the agent gives up, and on what topics
CSAT on agent-handled chats	Customer experience, compared with human-handled
Time to first reply	The obvious speed gain

A high resolution rate with a high reopen rate is a warning, not a win. Compare CSAT between agent-handled and human-handled conversations during the pilot, and be ready to narrow the agent’s scope.

Pilot plan

1. Pick two or three ticket types that are common and well documented.
2. Run the agent in draft mode, so a person approves each reply.
3. After a week of acceptable drafts, let it answer those types directly with sampling.
4. Review conversations weekly and update content where it went wrong.
5. Expand ticket types gradually.

For smaller teams, free and freemium options such as Chatbase or Tidio can be a low-cost test; check usage caps first, as discussed in free versus paid AI agents. Teams with developers may prefer open-source builders. If your customers also reach you by phone, see AI voice agents for business.

Privacy and data handling

Support conversations contain personal data. Check where the vendor stores transcripts, how long it keeps them, whether they are used to improve models and how deletion requests are handled. For EU customers, GDPR obligations apply. Tell customers when they are talking to an AI, and make it easy to reach a person. For access and permission design, read security and data access for AI agents. To compare categories side by side, the full BusinessMCP AI agents directory is the quickest overview, and its Enterprise tier entries are collected in their own category.

Channels and languages

Support rarely arrives in one place. Email, chat, social messages and phone each behave differently, and an agent that works well in chat may not suit email, where replies are longer and customers expect more detail. Start with the channel that carries the most routine volume and add others once the first is stable.

Language is another boundary. Many agents answer in several languages, but quality varies and mistakes in a second language are harder for your team to spot. If you serve customers in languages your staff cannot read, arrange periodic review by a native speaker, and keep the handover route to a person who speaks it.

Seasonal peaks and training the team

Support load swings with launches, holidays and incidents. An agent can absorb a peak, but only for question types it knows. Before a known peak, update your help content for the expected questions and test the agent on them. During an incident, have a way to push a short notice into the agent’s instructions so it does not keep telling customers that all is well.

Human agents also need preparation. Explain what the agent will handle, show how handover appears in their tools, and ask them to flag every wrong answer. They see the failures first, and their feedback improves the content faster than any dashboard. Over time their work shifts toward harder cases, so check that their targets and training reflect that, otherwise the team will feel the change as pressure rather than relief.