An honest Decagon AI review for 2025: Features, limitations, and pricing

Let's be real, the pressure on customer support teams is intense right now. Everyone wants you to automate more, resolve tickets faster, and somehow keep that personal touch that customers love. This pressure has paved the way for some seriously powerful (and often complicated) AI agent platforms, like Decagon AI.

So, in this Decagon AI review, we’re going to cut through the marketing noise. We'll get into its main features, talk about some of the real-world limitations you should know about, and unpack its pricing. By the end, you should have a good sense of whether it’s the right tool for your team or if another option makes more sense.

What is Decagon AI?

Decagon AI is an AI platform designed for enterprise-level customer support automation. The big idea isn't just to deflect tickets; it's to create AI agents that can think and solve problems like your best human agents. The company was co-founded by Jesse Zhang (ex-Google) and Ashwin Sreenivas (ex-Palantir), so it’s got some serious tech DNA behind it.

And they’ve definitely made a splash in the AI world, pulling in some major investment to fuel their growth. After a $10 million seed round, they snagged another $25 million in a Series A from big names like Andreessen Horowitz. That kind of backing has helped them land some huge clients, like Rippling, Bilt, Classpass, and Chime.

The results speak for themselves. Rippling, for instance, saw its ticket deflection jump by 32%, and Chime managed to get a 70% resolution rate across both chat and voice. It’s pretty clear that for the right company, Decagon can automate a ton of work.

How does Decagon AI work?

The magic of Decagon is that it builds AI agents that do more than just read from a script. They learn from your past support conversations and can actually do things for customers across chat, email, and even voice calls.

Agents that learn and take action

The whole point of Decagon is to create AI agents that really understand a customer's history and what they're trying to do. The platform learns from every conversation, so its agents are always getting smarter.

They can also plug into your backend systems to handle real tasks, like processing a refund or checking on an order. You set this up using what Decagon calls "Agent Operating Procedures" (AOPs). Think of these as the rulebooks that tell the agent how to act. This is what lets you go beyond basic Q&A and start automating entire support processes.

Multi-channel approach

One of the things that makes Decagon stand out is its voice AI. It’s designed for natural, quick-response phone conversations. You get a lot of control over the agent's voice, too, so you can tweak the tone, style, and speed to fit your brand.

The platform also remembers conversations across different channels. If a customer chats with you and then calls in later, the voice agent already has all the context. That alone can make for a much less frustrating customer experience. Decagon can also hand off conversations to a human agent (with a neat little summary) and send SMS follow-ups.

Connecting to your existing tools

To pull all this off, Decagon has to play nice with the tools you're already using. It has ready-made integrations for popular help desks like Zendesk and Salesforce, and it can connect to knowledge bases like Confluence.

For its voice features, it hooks into platforms like Amazon Connect and RingCentral. It also supports SIP trunking, which is just a technical way of saying it can connect directly to your company's phone system.

Real-world challenges and limitations

Decagon is a beast of a platform, but it's not for everyone. The demos look great, but feedback from the real world points to a few common headaches with transparency, complexity, and how the whole thing is structured.

The "black box" problem

One of the biggest gripes you’ll hear about platforms like Decagon is the "black box" issue. A user on Reddit summed it up perfectly: you can't always see why the AI did what it did. That makes it tough to review conversations, tweak the agent’s behavior, or figure out what went wrong when it makes a mistake.

It's worth noting that Decagon's website says their AI is "not a black box" and that they have audit logs. This might point to a disconnect between the marketing promise and the day-to-day reality of managing the agent. If you can't easily follow the AI's logic, you're giving up a lot of control.

The setup requires engineers

Decagon mentions that some integrations need "no custom code," but for anything more advanced, user feedback suggests you'll need developers on standby. Building custom workflows with their AOPs or connecting to new APIs isn't something a non-technical person can easily do. One founder who knows the space pointed out that this makes it hard for support teams to truly own the platform.

This clashes with the idea of a self-serve tool. Even though Decagon offers "self-serve APIs", that name itself implies you need to be a developer. If you don’t have engineers to spare, this complex setup can be a huge roadblock.

Single-agent design limitations

This one is a bit more subtle, but it's a big deal. Some experts call it the "single-agent mindset." Decagon seems to rely on one generalist agent that tries to handle every single customer question. This can lead to problems when conversations get complicated or jump between topics.

For example, an agent that's great with billing questions might get confused if the customer suddenly pivots to a technical problem. Without different, specialized agents for different jobs, the system can only get so smart.

Pro Tip: When you're looking at any AI platform, ask about this. Does it use one agent for everything, or can you build and manage multiple, specialized bots? For anyone with complex support needs, the answer to that question is really important.

Decagon AI pricing: Paying for what you use

Decagon's pricing is a little different. Instead of paying per human agent seat, you pay based on what the AI agent actually does, which makes sense. They give you two main options: paying for conversations or paying for results, as detailed in the graphic below.

1. Pay per conversation: You pay a flat rate for every single conversation the AI touches. It doesn't matter if it solves the problem or has to pass it to a human. This model is good for teams who need their monthly costs to be predictable.

2. Pay per resolution: With this option, you only pay when the AI agent successfully solves a customer's problem all by itself. The rate per resolution is higher, but you're only paying for successful outcomes.

Here’s a quick comparison:

Pricing Model	How it Works	Best For	Potential Downside
Per-Conversation	Pay a fixed fee for every conversation the AI handles.	Teams that need predictable, easy-to-forecast monthly costs.	You pay even if the issue is escalated to a human agent.
Per-Resolution	Pay a higher fee only when the AI successfully resolves the issue.	Teams focused on a direct, outcome-based ROI and high deflection rates.	The cost per resolved ticket is higher, and defining a "resolution" can be ambiguous.

So, does it save you money? If you have a huge volume of simple, repetitive tickets, then yes, it can be a lot cheaper than hiring more people. But for teams with lower ticket volumes, the cost might be tough to justify.

Alternatives for more control

Look, Decagon is a solid choice for huge companies with big engineering teams. But for a lot of teams, the complexity, the "black box" nature, and the fuzzy pricing are just too much.

If you want powerful automation but don't want to give up control or wait months to get started, you might want to check out eesel AI. It’s built for teams that want to move fast without needing a developer to hold their hand.

Here’s how eesel AI tackles the main challenges we talked about with Decagon:

• No more "black box" mysteries. With eesel AI, you're never guessing what the AI is doing. You can test your AI in a safe sandbox environment using your own past tickets before it ever speaks to a real customer. This means you can see exactly how it will perform and tweak it until you're comfortable. You decide when the AI gets involved and what it's allowed to do.

• Setup is actually simple. You can forget about begging your engineering team for help. eesel AI connects to your help desk (like Zendesk or Freshdesk) with just one click. It starts learning from your old tickets, help center, and even Google Docs right away, getting you up and running in minutes.

• You can build multiple, specialized bots. Instead of one agent trying to do everything, eesel AI lets you create different AI bots for different jobs. You could have one bot for simple Tier 1 questions, another for your internal team on Slack, and even one to help the sales team. This multi-bot setup is just more reliable and doesn't get confused like a single-agent system can.

• Pricing is straightforward. Decagon’s pricing can be hard to pin down. eesel AI has simple, interaction-based pricing that’s easy to predict. Plans start at $299 a month for 1,000 interactions, so you don't get punished with a higher bill just because your automation is working well.

The final verdict: Is Decagon AI right for your team?

After this Decagon AI review, what's the bottom line? Decagon is a seriously powerful platform with a lot of funding and some big-name clients. It's a great choice for large companies that have plenty of tickets and an engineering team ready to handle its complex setup. For them, it can be a true automation powerhouse.

But for many other teams, the downsides are hard to ignore. The lack of control can feel like a gamble, the setup is a major hurdle for non-technical folks, and the pricing can be confusing.

If you're looking for a path to automation that's faster, clearer, and keeps you in the driver's seat, it might be time to look at a different kind of tool, one where you build, test, and manage the AI yourself instead of hoping a black box gets it right.

Ready to see what it's like to have an AI agent that you're actually in control of? Start your 7-day free trial with eesel AI and you can build your first bot in a few minutes.

A YouTube video that supplements this Decagon AI review by showing the platform in action.