Parlant vs Decagon
Parlant vs Decagon
Decagon lets customer-service teams build and improve agents by writing procedures in plain language, on one integrated platform.
Parlant also lets you describe your agent in plain language, but you build it yourself with your coding agent. Your agent's code and the open-source engine that runs your procedures are yours, so you can change how procedures are applied, not just the procedures themselves. Your coding agent does most of the work, so changes are quick, and you pay your model provider directly.
If you're comfortable working with a coding agent and want control over the engine, your models, and your hosting, Parlant fits. If you'd rather not build with one, Decagon does.
- How writing procedures compares in each
- What owning the whole agent means, beyond owning your procedures
- What you'd give up by choosing Parlant
- How to tell whether Parlant is worth it for your service
These are the questions CX teams usually ask when weighing the two:
Our CX team can already write Decagon procedures in plain language. Why consider Parlant?
Decagon's Agent Operating Procedures (AOPs) combine natural-language instructions with code. Customer-service teams can define workflows, while engineers control integrations, guardrails, and versioning. That gives the people who own service procedures a direct way to change the agent.
Parlant's policies and procedures are plain language too. They live in your own agent project, which your coding agent writes and changes for you: you describe the procedure, and it writes the policy, connects the systems it needs, and runs test conversations against it. As the customer's needs change during a conversation, Parlant brings in every rule that applies, so a procedure step and your general rules work together. It can also check actions before they run and replies before customers see them.
The reason to choose Parlant is that the whole agent is yours, including the open-source engine underneath. You can connect it to any system your code can reach and change how it works when a requirement goes beyond editing a procedure.
Decagon tracks versions and explains its decisions. What more does "owning it" mean with Parlant?
Decagon's AOP tooling supports Git-based tracking, ownership of your code, and explanations of agent decisions. Those are useful for reviewing changes and investigating behavior.
Decagon describes its agent engine as proprietary. With Parlant, you own the engine as well as your procedures. You can pick the model for each job, change how its checks work, or have it keep customer data in the database your application already uses. Every reply can still be traced back to the rules and actions behind it with diagnostics.
Owning your procedures and owning the engine are different levels of control. Most agents won't need engine changes, but when a requirement does, the option is there, and your coding agent makes the change.
What happens when one customer request touches several of our policies?
Try a request with connected conditions: a customer wants to return a damaged order, change the pickup address, and get a refund that needs approval. A good agent keeps all of that straight and tells the customer exactly which actions went through.
Decagon's AOPs use entry conditions, conditional instructions, and references to tools and information. Parlant brings every relevant policy and action into one agent's view at once. These are two approaches to judge against the same customer outcome.
In Parlant, you can turn on action checks and reply checks for the sensitive parts. If approval is still pending, test that neither agent issues the refund or says it went through. Having policies or guardrails doesn't tell you how either agent will actually behave; testing does.
What would we give up by choosing Parlant?
Decagon combines chat, voice, email, simulations, experiments, and monitoring. Its Duet tools help teams find workflow gaps and generate, test, and refine procedures. That integrated environment is the right choice when your customer-service staff don't feel ready to build with a coding agent.
If they are, you give up little. Someone on your team owns the agent and its connections, and the coding agent fits Parlant to your business, connects your systems, and tests the result. You test changes in the Dashboard with simulations and review real conversations there. In return, the agent can use any system your code can reach, not only a connector catalog. You can use managed hosting, or self-host and take on the servers too. Parlant's built-in voice interface comes with the commercially licensed Compass Pro.
For cost, Parlant itself is free, you pay your model provider directly, and the Dashboard is a flat monthly fee; see pricing and the worked example against Intercom Fin. Compare that with an actual Decagon proposal. Extra reply checks add model work and response time where you turn them on.
How can we tell whether Parlant is worth it for us?
Build one procedure in a Parlant test project with your coding agent, and compare it with the same procedure in Decagon. Use real conversations: changed requests, approval exceptions, and actions that fail. Note which actions succeeded, whether the replies were accurate, how long customers waited, and how much work it took to find the cause of each problem.
Then change the approval policy and add a related request. Have the people who would look after each version make and test the change. If you want to make changes yourself and control what the agent connects to and how it works, Parlant is built for that, and you can compare its running cost directly with Decagon's proposal. Decagon is the right choice if your team doesn't feel ready to build with a coding agent and its integrated tools already cover what you need.
Comparison as of September 2026, based on the linked product documentation. The capabilities described here are not a measured reliability ranking.