What I liked most about Decagon was the level of control it gives teams over how the AI manages real customer conversations. While helping a company implement the platform, we found the option to define workflows in natural language especially valuable. It made it much easier to fine-tune the agent’s behavior without having to rebuild everything from scratch each time the client wanted to update a process.

The cross-channel experience was another major strength. Having chat, voice, and email all operate from the same underlying context helped keep customer interactions consistent from one channel to the next. I also found the testing and conversation monitoring features very useful, since we could review how the agent was performing and spot areas that needed improvement, rather than just putting the AI live and leaving it unattended. Review collected by and hosted on G2.com.

One area that could be improved is handling more complex workflows once an agent has accumulated a large number of instructions and integrations. While the natural-language AOP approach makes updates much easier than traditional development, it still requires careful testing when multiple workflows or rules interact.

In our experience supporting the client, this meant spending time validating different conversation paths before pushing certain changes live. More visual workflow mapping, along with simpler ways to spot dependencies between instructions, would make it easier to understand the downstream impact of a change and help speed up the review process. Review collected by and hosted on G2.com.