Product teams replacing an agent design tool have three routes: visual flow builders for designing conversations, developer-led or enterprise stacks for control at scale, and chat tools that put a ready agent on a website or messaging app. Choose the route that fits who builds and maintains the agent, then test it with hostile messages before launch.
Teams usually come to Voiceflow to design how an AI agent behaves: the flows, the hand-offs and what happens when a customer goes off script. Leaving it tends to mean one of three things. You want more developer control, you need something that fits a big company's stack, or you'd rather drop the design work and put a ready-made agent on your site.
Here are nine options, in no particular order. The table comes first so you can scan them, then the three routes, then one line on each tool.
Nine agent tools in one table
| Tool | Approach | Works well for |
|---|---|---|
| Botpress | Support agent platform | Agents that take actions, not only answer |
| Botsonic | No-code website chatbot | A site chatbot trained on your own content |
| Chatfuel | Messaging-app sales agent | Replies and bookings on WhatsApp, Instagram and Facebook |
| Flowise | Open source visual agent builder | Self-built LLM orchestration on an open source canvas |
| Microsoft Copilot Studio | Microsoft-stack agents | Building agents and workflows inside a Microsoft setup |
| Rasa | Developer-led agent framework | Product teams who want control and audit trails |
| StackAI | Enterprise no-code agents | Enterprise agents connected to internal systems |
| Tidio | Chat plus AI for small sites | Website chat where an AI assistant answers first |
| Typebot | Visual flow builder | Block-by-block flows with bot analytics |
Design canvas, developer stack or ready-made chat?
Visual flow builders. Typebot builds conversations block by block. Botpress is aimed at support agents that take actions such as refunds and account changes. Flowise is open source, with a canvas for agents and language model orchestration, and StackAI targets enterprises that want drag-and-drop agents connected to their own systems. This is the closest group to designing flows on a canvas.
Developer-led and enterprise stacks. Rasa is for product teams building customer-facing AI with developers in charge, and it lists audit trails as standard. Copilot Studio suits Microsoft shops: if your company already runs on Microsoft, agents built there sit next to the tools staff use every day. These routes need more technical time, and they give you more control in return.
Chat tools with a ready agent. I'll be plain: Tidio and Chatfuel are chat and messaging tools rather than flow design studios. The first puts website chat and an AI assistant in front of customers, the second works inside WhatsApp, Instagram and Facebook, and Botsonic builds a website chatbot from your own content. If nobody on your team wants to design flows, this group gets an agent live without that work.
One line on each agent builder
In no particular order, A to Z.
Botpress
Best for: Support agents that do things, such as refunds, account updates and multi-step workflows; the vendor says it's built for the tough tickets.
Botsonic
Best for: A quick website chatbot trained on your own content with no code; the vendor says it doesn't train on your data.
Chatfuel
Best for: Selling in messaging apps: an AI agent that replies, qualifies, books and follows up, from a company that calls itself an Official Meta Business Partner.
Flowise
Best for: Developers who want an open source, drag-and-build canvas for agents and language model orchestration, starting on a free tier.
Microsoft Copilot Studio
Best for: Organisations already on Microsoft that want to build agents, workflows and apps with Copilot integrations.
Rasa
Best for: Product teams building customer-facing AI with developers in control; the free Developer Edition includes its CALM approach, with audit trails and voice listed as standard.
StackAI
Best for: Enterprise teams deploying agents with drag-and-drop tools and connections to systems staff already use; the vendor promises secure, compliant apps.
Tidio
Best for: Smaller websites wanting chat where an AI assistant, Lyro, answers questions and turns the remaining chats into leads and bookings.
Typebot
Best for: Designing conversations block by block, with analytics on how each bot performs, then publishing to a website or WhatsApp.
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My advice for product teams choosing an agent platform
Choose by who'll maintain the agent a year from launch, not who builds the demo. If designers and support leads will own it, a visual builder keeps them in charge. If engineers will own it, a developer-led stack saves arguments later. If nobody has time to own it, a ready-made chat tool is the honest choice, even if it does less.
Write down what the agent may do on its own and what needs a person before you compare tools, then check each platform against that list. My guide to handing an AI agent over to a human helps with the second half, and this round-up of AI agents for small businesses is useful if you're a smaller team.
Why a tidy flow can still be steered by what customers type
A conversation flow looks controlled on the canvas. Every branch is drawn and every answer has a place. But the language model step inside that flow doesn't enforce a boundary between your instructions and what the customer types. To the model, both are text in the same prompt, so a customer can try to talk it out of your rules.
The UK NCSC makes this point in its blog post on prompt injection. It says current large language models don't enforce a security boundary between instructions and data inside a prompt, and that prompt injection is a different problem from SQL injection, so the old fixes don't carry over. Put simply, a neat flow chart isn't a security control.
So put the limits outside the prompt, and test before launch:
- Decide which actions the agent may take alone, such as answering questions, and which need a person, such as refunds or account changes.
- Enforce those limits in the system around the agent, with permissions and approval steps, not only in the instructions.
- Before launch, send the agent hostile messages: requests to ignore its rules, reveal its instructions or do something it shouldn't.
- Log what the agent does, so you can see when a conversation went somewhere you didn't plan.
- Repeat the hostile tests after every big change to the flow or the model.
If you'd like a starting list, my prompt injection defence checklist covers the basics.
Do you make an AI agent or conversation design tool? Tool makers can ask to be considered for a featured place on this list.
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Frequently asked questions
What's the difference between a flow builder and an AI agent platform?
A flow builder lets you draw the conversation step by step. An agent platform leans more on a language model to decide what to say and do. Many tools now mix both, so check how much of the behaviour you can control directly.
Do I need developers to build an AI agent?
Not always. Visual builders and ready-made chat tools let non-developers launch an agent. Developer-led stacks give more control but need engineering time to build and maintain.
What is prompt injection?
It's when someone types text that tries to override the instructions you gave an AI model, for example asking it to ignore its rules. Because the model reads your instructions and the user's text together, it can't always tell them apart.
Can a ready-made chatbot replace an agent design tool?
For simple website or messaging conversations, often yes. For complex agents that take actions in your systems, you'll usually want more design control than a ready-made chatbot offers.
How should I test an AI agent before launch?
Use real customer questions and hostile ones. Try to make it break its rules, reveal its instructions or take actions it shouldn't, and check that the limits hold outside the prompt.
