Sierra vs Decagon: AI Customer-Service Agents, Compared
Sierra and Decagon are the two best-funded CX agents on the market. Here's how they differ on resolution quality, guardrails, pricing, and integrations.
Both Sierra and Decagon arrived with serious pedigree, serious funding, and a serious thesis: that AI agents should resolve customer issues end-to-end, not just deflect them. If you’re evaluating enterprise CX automation in 2025–2026, you’ll run into both. They’re more similar than their marketing suggests — and the differences that actually matter for operators aren’t always the ones leading the pitch deck.
The Core Premise Is the Same
Both platforms position their agents as autonomous workers rather than FAQ bots. Sierra frames itself not as a chatbot tool but as an “agent OS” — the operating layer where a business designs an autonomous agent, gives it goals and guardrails, connects it to backend systems, and lets it actually do things: process a return, update an account, save a cancellation. Decagon’s framing is nearly identical. Decagon builds AI “concierge” agents for enterprise customer support. Unlike basic chatbots that answer FAQs, Decagon’s agents are designed to resolve tickets end-to-end across chat, email, and voice.
The practical distinction: Sierra leans heavily on a multi-model architecture with supervisory agents layered on top, while Decagon has been building its own model stack. Since March 2026, 80% of Decagon’s traffic runs on models trained in-house rather than on third-party providers like OpenAI or Anthropic — trained specifically on customer support conversations, which Decagon says produces better performance for support use cases than general-purpose models.
Resolution Quality
Neither vendor publishes third-party-verified resolution benchmarks, so treat any headline stats as vendor-reported directional signals rather than audited performance figures.
Sierra’s most-cited case study is WeightWatchers. Their Sierra agent reportedly handles close to 70% of customer sessions at a 4.6/5 satisfaction score.
Sonos uses a Sierra agent for product support — speaker setup, troubleshooting, multi-room configuration — and ADT applies it to customer service, billing, and lead qualification. These are real, sizeable deployments; just keep in mind the headline metrics are vendor-reported.
Decagon’s differentiated action layer is its Agent Operating Procedures (AOPs). AOPs let you define agent workflows in natural language rather than code. A non-technical support manager can write instructions like “If a customer asks for a refund over $100, verify their purchase date and escalate to the billing team” and the AI follows that logic.
AI Actions let the agent do things, not just say things. Through integrations with Stripe, Shopify, and Salesforce, Decagon can process refunds, update orders, verify identity, and create tickets — without escalating to a human.
Across operator feedback on both platforms, recurring gripes include context loss in long or complex conversations and difficulty auditing why the agent made a specific decision. A recurring concern among Decagon customers is that it can be difficult to see why the agent made a particular decision. Trace View and Watchtower were introduced to address this, but based on community feedback, the experience of auditing agent behavior remains inconsistent in practice.
For more on how these compare against lighter-weight alternatives, see the Decagon vs. Sierra comparison page, or the Decagon vs. Intercom Fin breakdown.
Guardrails
This is where both platforms invest heavily and where the details matter most.
Sierra’s architecture uses supervisory agents that monitor live conversations and deterministic guardrails baked into its Agent SDK. Sierra builds on an Agent SDK with declarative goals, deterministic guardrails, composable skills, and CI/CD via GitHub Actions — plus supervisory agents that monitor and subtly correct live conversations.
PII shared with Sierra agents is automatically encrypted and masked. Customer data is never shared across organizations and never used to train models. System integrations follow predefined security policies rather than ad-hoc LLM decisions.
That said, guardrail strength is configuration-dependent. The December 2025 jailbreak incident — where a coordinated bad actor exploited a misconfigured guardrail on Gap.com’s agent — is worth noting. Security remains configuration-dependent. How you configure matters as much as what the platform provides.
Decagon’s safety approach centers on Watchtower. Decagon includes systems called Watchtower and Guardrails that monitor every AI interaction in real-time. These check responses against company policies, flag potential hallucinations before they reach customers, and alert human supervisors when the AI encounters situations outside its training.
The platform also uses intelligent segmentation to route different types of issues differently: simple password resets get fully automated handling, complex billing disputes route to specialists, and emotional situations involving frustrated customers trigger immediate human intervention.
Both platforms have honest gaps on the observability side. G2 reviewers report rudimentary user roles that make granular permissions difficult, and audit logs “lack depth, which can cause issues when tracing activity or ensuring compliance.” Neither platform yet gives operators a fully unified view of human and AI activity together.
For a broader look at AI safety tooling, see our Sierra and Decagon tool pages.
Outcome Pricing — and Why the Definition of “Resolution” Matters
Both platforms use outcome-adjacent pricing, but they structure it differently — and the details create real budget risk.
Sierra uses an outcome-based pricing model where you pay when the AI agent achieves a successful resolution. The specific per-resolution rate and definition of “success” are negotiated per contract.
Third-party estimates put annual contracts at approximately $150,000/year and up, with setup fees of $50,000–$200,000 and year-one totals of $200,000–$350,000+. There is no free trial, no self-serve option, and no published rate card.
Decagon gives buyers a choice of model. Decagon generates revenue through two primary pricing models: per-conversation and per-resolution. The per-conversation model charges a fixed rate for each incoming customer inquiry, with volume discounts available.
The per-resolution model, which is higher priced, charges only when the AI successfully resolves an issue without human intervention.
A $50,000 annual platform fee is the baseline, corroborated by multiple third-party sources. It covers access to the platform, all channels, integrations, AOPs, Watchtower QA monitoring, testing tools, and analytics.
The billing risk on both platforms is identical: who defines “resolved”? Per-resolution is outcome-based pricing, but only if “resolved” is defined clearly in the contract to prevent billing disagreements. Push your legal team to nail this down before signing — and get independent audit rights over resolution reporting, not just vendor dashboards.
Integration Depth
Sierra’s integration story is powerful but requires engineering bandwidth. Sierra does not natively integrate into Zendesk, Intercom, Freshdesk, or Salesforce as a marketplace app. It connects to backend systems via its Agent SDK and Integration Library.
Sierra’s system integrations connect agents to internal and external systems via API and let agents take real actions mid-conversation. An agent can process a refund, update a subscription, check an account balance, or verify a customer’s identity without handing off to a human. In April 2026, Sierra added PCI-compliant payments, allowing agents to handle financial transactions directly.
Sierra agents deploy across chat, SMS, WhatsApp, email, voice, and ChatGPT from a single agent configuration.
Decagon’s integration depth is comparable. The company describes some integrations as requiring “no custom code,” though the practical meaning of that depends on how standard your existing setup is. Anything non-standard or API-based will require engineering involvement.
Implementation typically takes four to twelve weeks and requires dedicated engineers on your side.
The critical structural difference: Sierra sits as a separate layer outside your helpdesk. Unlike competitors that plug directly into Zendesk, Intercom, or Salesforce ecosystems, Sierra sits as a separate layer. This creates data dispersion — bot conversation data lives in Sierra while human agent conversations live in your contact center, with no unified view. Decagon’s analytics have a similar blind spot — Decagon’s analytics cover the AI layer only and don’t show how human agents and AI work together. Both require deliberate architecture work to get a single pane of glass view across your full CX operation.
Also worth flagging: both platforms are heavy onboarding commitments. Sierra deployments typically take 3 to 7 months. Decagon’s implementation window is shorter but not trivial — four to twelve weeks, with engineering resources required throughout.
If you’re still mapping the landscape, our Decagon vs. Maven AGI and Sierra vs. Decagon pages cover additional dimensions worth reading before you enter a sales process.
Bottom Line
Sierra is the better choice if voice-first, omnichannel deployment and a deeply-resourced white-glove implementation team are your priority — and if your budget can absorb a six-figure year-one commitment with no self-serve ramp. Decagon’s AOPs and per-conversation pricing model offer a bit more flexibility at the contract level, and its in-house model stack is an interesting differentiator for pure support quality at scale. Either way, operators should treat the resolution-rate headlines from both vendors as directional, nail down the definition of “resolved” before signing, and pressure-test the audit trail — because on both platforms, the guardrails are only as strong as the configuration and contract behind them.