Decagon vs Sierra: AI Support Agents Compared for B2B SaaS (2026)

Decagon and Sierra are the two names that come up first when a B2B SaaS support leader starts evaluating an autonomous AI support agent. Both publish long enterprise customer lists, and both promise to resolve a large share of conversations without a human. The problem is that most "Decagon vs Sierra" content online is either a vendor's own comparison page or a rewrite of one. This post compares the two on what their own websites actually say as of September 2026, then explains where a B2B SaaS team's requirements diverge from the consumer-scale deployments both vendors are built around.

The short version: Decagon and Sierra are closer to each other than either vendor's marketing admits, they differ mainly in how agents get built and how you pay, and neither is designed for the in-product, Slack-first support motion that defines most B2B SaaS companies.

What is Decagon?

Decagon is a platform for building, optimizing and scaling autonomous AI customer support agents across chat, voice and email. Its defining product idea is Agent Operating Procedures (AOPs): you define how the agent should behave in natural language, the way you would write a standard operating procedure for a human rep, and the platform executes it. As of September 2026, Decagon's site also lists Duet (an AI partner that proposes and self-tests agent updates), Experiments, Testing and QA, Insights and Reporting, Watchtower, and Decagon Assist, a copilot for human representatives announced in August 2026.

Decagon's homepage describes the product as the "AI concierge for every customer." Its published customer logos are heavy on consumer and prosumer brands (Duolingo, Chime, Cash App, Ticketmaster, Oura, ClassPass) with a smaller set of business software names such as Rippling and Square. That mix matters for a B2B SaaS buyer, and we come back to it below.

What is Sierra?

Sierra is an AI agent platform whose stated mission is to help businesses "build better, more human customer experiences with AI." It offers agents across voice, chat, email, SMS and WhatsApp, plus a ChatGPT channel, in 59 languages according to its product page. As of September 2026 the product line includes Agent Studio (a no-code builder for customer experience teams), the Agent SDK (for developers), Ghostwriter (build or modify an agent by describing how it should behave), Explorer (natural-language analytics), Horizon (long-horizon agents that work across days or weeks, inbound and outbound), and a Context Engine that connects to systems of record.

Sierra's most distinctive public commitment is its pricing model. Since December 2024 it has described its model as outcome-based: customers pay when the agent achieves a defined outcome such as a resolved conversation, and "if the conversation is unresolved, in most cases, there's no charge." Sierra's customer logos skew large and consumer-facing: Rocket Mortgage, SiriusXM, Hyatt, Wayfair, ADT, CLEAR, Vanguard and Paychex among them, and the site states that 40 percent of the Fortune 50 partner with Sierra.

Decagon vs Sierra: how do they compare?

On the fundamentals, Decagon and Sierra are near-equivalents: both run autonomous agents on chat, voice and email, both offer a natural-language way to author agent behavior, both offer testing and analytics, and both sell into large enterprises with no published price list. They differ in three places that matter to an evaluation: how agent logic is built and owned, how the vendor charges, and which adjacent capabilities each has chosen to build.

The table below is built only from each vendor's own website, checked in September 2026. Where a vendor does not list something, the table says so rather than guessing.

Building and owning the agent

The clearest philosophical split is here. Decagon's position is that the whole agent should be expressible in natural-language AOPs so the support team, not the vendor and not engineering, owns it after launch. Sierra splits the job in two: Agent Studio for what a CX team can build without code, and the Agent SDK for what needs a developer. Decagon's own comparison page frames Sierra's SDK as a limitation ("complex logic moves to an SDK only technical teams can touch"); Sierra frames it as a feature ("developers of all skill levels" can build sophisticated agents). Both readings are fair. If your support org has no engineering allocation, Decagon's one-layer model is simpler. If you expect to build deep custom workflows against internal systems, having a real SDK is not a weakness.

How you pay

Neither vendor publishes rates. Sierra has committed publicly to outcome-based pricing, which in practice means paying per resolved conversation and, in most cases, not paying for escalations. Decagon's December 2024 pricing post says it supports both per-conversation and per-resolution options, and that most customers pick per-conversation. The difference is about who carries the risk of unresolved conversations, not about which is cheaper; that depends entirely on your resolution rate and volume, and you should model both against your own ticket data before believing either vendor's ROI calculator.

Worknet's pricing is also quote-based and not published, so this post makes no claim about which of the three is cheaper. If a vendor tells you a competitor's rate, treat it as a sales claim until you have a written quote.

Adjacent capabilities

Each vendor has started expanding past the autonomous agent. Decagon added a human-agent copilot (Decagon Assist) in August 2026. Sierra added Horizon for long-running, multi-day processes, with the published example being healthcare referral scheduling by phone. These are useful signals about roadmap direction: Decagon is moving toward the contact center floor, Sierra toward orchestration across time. Neither expansion is aimed at the software-adoption problem that generates most B2B SaaS support volume.

Why does B2B SaaS support need something different?

Both Decagon and Sierra are built around a customer who arrives with a problem on a support channel: a delayed order, a billing dispute, a reservation change. B2B SaaS support looks different in three ways. First, a large share of tickets are really adoption failures: the user could not find or configure a feature, so the fix belongs inside the product, not in a chat widget. Second, the primary channel for mid-market and enterprise SaaS customers is increasingly a shared Slack channel or Microsoft Teams, not a web chat. Third, the "customer" is an account with multiple users, a CSM, a renewal date and expansion potential, so a support interaction is also a retention and revenue event.

None of that shows up in either vendor's product pages as of September 2026. Decagon lists chat, voice and email as its channels; Sierra lists chat, voice, email, SMS, WhatsApp and ChatGPT. Neither lists Slack, Teams or in-application guidance on the pages we reviewed. That is not a criticism of the products; it is a description of the market they were built for.

Where Worknet fits

Worknet is an AI adoption agent that lives inside your SaaS product and in Slack and Microsoft Teams, and it treats support as one part of the user lifecycle rather than the whole of it. It reads where a user is in the application, answers questions from your own knowledge, guides them through the workflow they are stuck on, and can take the action for them with permission. The same agent handles onboarding, trial conversion and expansion, so the ticket a Decagon or Sierra agent would resolve after the fact is often the ticket Worknet prevents. It connects to Salesforce, Zendesk, HubSpot, Jira, Notion and Confluence, and to any MCP or REST API, and a Success or Support team writes the goal in plain English rather than building flows.

The honest framing is that these are different categories with an overlap. If your volume is high, consumer-facing, and arrives by phone or web chat, Decagon and Sierra are the shortlist and this post should help you separate them. If your customers are software teams who live in your product and in Slack, and your ticket queue is dominated by "how do I" and "where is" questions, an AI adoption agent addresses the cause rather than the symptom, and it can run alongside whatever helpdesk automation you already have. Worknet's Decagon vs Worknet and Sierra vs Worknet comparisons go deeper on each pairing, and the AI adoption agent page explains the category.

How should you run a Decagon vs Sierra evaluation?

Run both vendors against the same 200 real conversations from your own queue, tagged by intent, before you look at either vendor's benchmark. Insist on seeing agent logic edited live by someone on your team, not the vendor's solutions engineer. Ask for the escalation flow into Salesforce or Zendesk in your sandbox, not a demo tenant. Get pricing in writing under both a per-conversation and an outcome-based structure so you can compare them at your actual resolution rate. And classify your last quarter's tickets by whether the fix lived in the product; if most did, add an in-product agent to the evaluation instead of assuming a support-channel agent will absorb that volume.

Conclusion

Decagon and Sierra are strong, closely matched platforms for autonomous support on chat, voice and email, and the meaningful differences are how agent logic is owned (single natural-language layer versus no-code studio plus SDK) and how you pay (conversation or resolution options versus outcome-based). Everything in this comparison came from the vendors' own websites in September 2026 and will drift, so re-verify before you sign. For B2B SaaS teams whose support volume originates inside the product and in Slack, the more useful question is not Decagon versus Sierra but whether a support-channel agent is the right layer at all. If you want to see what an in-product adoption agent does with the tickets you have today, book a Worknet demo and bring your last month's queue.