Decagon is one of the more credible AI customer-support products shipping in 2026. It was founded in 2023 by Jesse Zhang and Ashwin Sreenivas, and it builds AI support agents that resolve customer issues for enterprises (Decagon, retrieved 2026). Its distinctive hook is Agent Operating Procedures, or AOPs, which let support teams encode their workflows in natural language rather than in code, paired with analytics on resolutions. Publicly referenced customers include companies like Duolingo, Notion, Rippling, and Eventbrite (Decagon, retrieved 2026). It is a strong, focused product.
This piece walks through what Decagon is in 2026, what Gravity does differently, and the moments where one wins decisively over the other. The honest summary up front: Decagon is a dedicated customer-support deflection product, and Gravity is a general self-serve operator platform. They are not the same purchase. Some categories go clearly to Decagon. Accuracy matters more than selling the wrong tool.
What Decagon is, and where it actually shines
Decagon builds AI customer-support agents for enterprises. The product is purpose-built for one job: resolving customer issues at scale, consistently, and with measurable results (Decagon, retrieved 2026). It is not a general assistant or a build-anything canvas. It is a support product, and that focus is its strength. For a support leader drowning in ticket volume, a tool designed end to end around resolution is more useful than a general agent retrofitted to the job.
Agent Operating Procedures
Decagon's signature idea is the Agent Operating Procedure. Instead of asking engineers to code every support workflow, a team writes out, in natural language, how a particular kind of issue should be handled, and the agent follows that procedure. This gives support managers direct control over resolution logic without an engineering ticket for every change. It is a thoughtful design choice that fits how support teams actually work, and it is a genuine differentiator in the category.
Resolution at enterprise scale
Decagon is built for serious volume and is measured on resolution. The analytics layer tracks how the agent performs so a support org can see what is being resolved and where to improve. Publicly referenced customers include well-known companies like Duolingo, Notion, Rippling, and Eventbrite, which signals it is trusted with real consumer and business support load (Decagon, retrieved 2026). For context on this whole space, our roundup of notable AI agents in 2026 places Decagon-style products alongside the broader field.
Where Decagon is excellent
Three places where Decagon is the clear pick. A support organization that wants to automate customer-service resolution at enterprise scale. A team that wants to own and tune resolution logic in plain language through AOPs. And any org that lives by deflection and resolution metrics and wants a dedicated product, not a side feature. For that buyer, Decagon is a strong, focused option, and a fair comparison should say so without hedging. The enterprise AI agent platforms buyer guide covers how products like this sit in a wider stack.
What Gravity does differently
Gravity is not a customer-support product, and that is the point. You describe the outcome you want in plain language, and Gravity matches you with an expert-built agent that runs it in about 60 seconds. There is no scoping call, no deflection model to configure, no support stack to integrate before you get value. An expert already built the agent, tested it, and brought it to the platform. You pay per use, $1 buys 1,000 credits, with no subscription required. For the shape of the whole platform, see how Gravity works.
The trade-off is honest. Gravity is a general platform for the operator doing their own work, not a dedicated engine for resolving your customers' tickets at brand scale. Decagon goes deep on one job and instruments it: AOPs, resolution analytics, and a managed deployment built around support. Gravity goes broad across many tasks and optimizes for time-to-result. If the job is customer-service deflection at enterprise volume, Decagon is purpose-built for it and Gravity is not. If the job is getting your own work done across research, drafting, and operations, that is where Gravity fits.
How the platform is structured
Gravity is the platform that runs the agents. Users describe an outcome and pay per use, and Gravity carries the execution cost and the platform overhead. Expert builders build and maintain agents for Gravity, and Gravity pays them for that work and stays responsible for the service. That structure is the difference from a dedicated support deployment: Decagon builds and manages one support agent for one enterprise, while on Gravity an expert builds an agent once and the platform runs it for many operators. For how to weigh platforms like this, see how to evaluate AI agent platforms.
Side-by-side comparison
The honest comparison runs along ten dimensions. Below is how the two products stack up as of 2026. Where a current Decagon detail is uncertain, that is flagged rather than invented.
| Dimension | Decagon | Gravity |
|---|---|---|
| Primary job | Resolve customer-support tickets at scale | Run expert-built agents for your own work |
| Buyer | Enterprise support and CX leaders | Operators, SMBs, and individual teams |
| Go-to-market | Sales-led, scoped enterprise deployment | Self-serve; pre-launch waitlist now |
| Signature feature | Agent Operating Procedures in natural language | Describe an outcome, run a tested agent |
| Pricing model | Enterprise contract, quoted per engagement | Pay per use, $1 = 1,000 credits, no subscription |
| Setup time | Scoped integration with your support stack | Describe the outcome, run in about 60 seconds |
| Who builds the agent | Decagon, tuned with your AOPs and knowledge | Vetted expert builders, for the platform |
| Measurement | Resolution and deflection analytics | Did the run produce the outcome you asked for |
| Scope | Deep on customer support | Broad across many task types |
| Best fit | Dedicated, managed support automation at scale | Fast, flexible, many tasks, no procurement |
A few rows favor Decagon outright. The dedicated support focus, the AOP model for encoding resolution logic, and the resolution analytics are exactly what a support organization wants, and Gravity does not try to compete there. A few favor Gravity: breadth across task types, speed to first result, and self-serve pay-per-use access without a sales cycle. Several rows are simply describing two different products for two different buyers. Weight them by what your work actually is.
Dedicated support product vs general platform
The deepest difference is not a feature; it is focus. Decagon is a vertical product. It picked one hard problem, customer-support resolution, and built everything around it: the AOP model so support managers can own the logic, the analytics so they can prove deflection, and the managed deployment so reliability is the vendor's job. That depth is the reason to buy it. A general tool can rarely match a focused product on the focused product's home turf, and for support at scale Decagon is on its home turf.
Gravity is a horizontal platform. It picked breadth: run an expert-built agent for whatever outcome you describe, across many kinds of work, in about 60 seconds. The depth on any single vertical is not the promise; the promise is that you do not have to build, integrate, or own reliability for a long list of everyday tasks. This is the same dedicated-versus-general tension we discuss in build vs buy AI agent: a dedicated product wins on fit for its one job, a platform wins on coverage and speed across many.
There is also a category point worth being precise about. A Decagon agent is a customer-support representative judged on resolution. A Gravity agent is judged on whether it completes the task you describe. The words agent, assistant, and chatbot get used loosely, so if the distinction matters to your purchase, see AI agent vs chatbot vs assistant. And because any customer-facing agent must be dependable, our guide to reliability testing applies whichever vendor you choose.
Ownership, hosting, and pricing reality
Decagon is an enterprise vendor, and the fair framing is that it is built for buyers who run a security review. A support agent that touches customer conversations and account data will pass through procurement, data-protection, and compliance checks, and that is correct. Decagon's managed model means it owns the hosting, the agent behavior, and the integration with your support stack, which is part of what an enterprise is paying for. Confirm data-residency, retention, and compliance specifics with Decagon directly during scoping, the same diligence you would apply to any vendor handling customer data.
On pricing, Decagon sells enterprise contracts quoted per engagement rather than a public self-serve price. This piece does not pin numbers that may be stale or that vary by deployment. The honest note is that a dedicated, managed support product is typically an enterprise spend, and that is appropriate for the value it delivers at scale.
Gravity is operated by XAI Technologies Pvt Ltd, based in Bangalore, and is in pre-launch waitlist as of 2026. Its model is pay per use rather than per seat: $1 buys 1,000 credits and you spend them only on runs you actually use. As a pre-launch product, its data-residency posture at general availability is still being finalized, so this piece does not overclaim it. For how the two leading autonomous-agent products compare on this axis, our Gravity vs Lindy and Gravity vs Manus breakdowns go deeper.
When Decagon is the right choice
Three signals say Decagon is the better purchase. First, the job is customer support and the volume is real; you need to resolve tickets at enterprise scale. Second, you want to own and tune the resolution logic yourself in plain language, which is exactly what AOPs are for. Third, you live by deflection and resolution metrics and want a dedicated, managed product instrumented around them rather than a general-purpose agent.
If those three are true, Decagon is a serious, focused option and Gravity is not really competing for that purchase. Because a customer-facing agent must behave safely under pressure, see AI agent safety and guardrails before any rollout, whichever vendor you select.
When Gravity is the right choice
Three opposite signals say Gravity is the better purchase. First, the job is your own work, not your customers' tickets; you want research, drafting, analysis, and operations done, not a deflection engine. Second, you want to start now without a sales call; describe an outcome and run a tested agent in about 60 seconds. Third, pay-per-use economics fit better than an enterprise support contract; you want to spend only on runs you use, with no seat to carry in slow months.
The deeper bet is the platform one. As experts build and test agents for Gravity across many task types, the catalogue of finished, trustworthy agents grows, and the value of commissioning each one yourself decays. For more on that approach, see about Gravity.
Using both together
These two products are not either-or, because they handle different jobs. A company can run Decagon as its dedicated customer-support resolution agent while its internal team uses Gravity to run agents for back-office work: research, drafting, reporting, and operations. Decagon deflects and resolves customer tickets at the front line; Gravity helps your own people get tasks done faster behind the scenes. If your support stack already runs on tools like Zendesk or Intercom, our guides to an AI agent for Zendesk ticket triage and an AI agent for Intercom auto-responder show where lighter, self-serve automation fits alongside a dedicated support deployment.
Frequently asked questions
What is the main difference between Decagon and Gravity?
Decagon is a dedicated enterprise customer-support product. It builds AI support agents that resolve tickets and lets teams encode workflows as natural-language Agent Operating Procedures. Gravity is a general platform where you describe an outcome and run an expert-built agent for your own work. Decagon deflects support tickets; Gravity helps operators get many tasks done.
What are Decagon's Agent Operating Procedures?
Agent Operating Procedures, or AOPs, are Decagon's way of letting support teams encode their workflows in natural language rather than code. A team writes how a given issue should be handled, and the agent follows that procedure. It is a thoughtful design for support orgs that want control over resolution logic without engineering for every change.
Is Decagon self-serve or sales-led?
Decagon is a sales-led enterprise product. You engage their team to scope a deployment, connect it to your support stack and knowledge, and roll it out as a resolution-focused support agent. There is no instant self-serve signup. Gravity is self-serve: join the waitlist now, then describe an outcome and run an agent yourself, paying per use.
When is Decagon the right choice?
Decagon is the right choice when you run a support organization that wants to automate customer-service resolution at enterprise scale, when you want to encode workflows in natural language and measure deflection and resolution, and when you want a dedicated, managed product purpose-built for support rather than a general-purpose agent platform.
When is Gravity the right choice?
Gravity is the right choice when you want to get your own work done across many task types, when you would rather run an expert's tested agent than commission a support deployment, when you want to start in about 60 seconds with no sales call, and when pay-per-use economics fit better than an enterprise support contract.
Can Decagon and Gravity be used together?
Yes, because they handle different jobs. A company can run Decagon as its dedicated customer-support resolution agent while its internal team uses Gravity to run agents for research, drafting, reporting, and operations. Decagon deflects and resolves customer tickets; Gravity helps your own people get back-office work done faster.
Three takeaways before you close this tab
- Decagon and Gravity are different products. One is dedicated support automation; the other is a general work platform.
- The fit test is the job. Is it your customers' support resolution, or your own tasks across many types?
- They can coexist. Run Decagon at the support front line, run Gravity in your back office.
Sources
- Decagon, "Product home", retrieved 2026, decagon.ai
- Decagon, "Customers", retrieved 2026, decagon.ai
- Decagon, "Pricing", retrieved 2026, decagon.ai
- Gravity, "How it works", gravity.fast
- Gravity team, "Gravity vs Manus", 2026, Gravity vs Manus
- Gravity team, "Enterprise AI agent platforms", 2026, Enterprise AI agent platforms