Decagon
AI agent platform purpose-built for customer support, with deep integration into support toolchains.
Decagon builds enterprise AI agents for customer support, positioning them as an AI concierge that resolves issues end to end rather than a chatbot that only suggests articles. Its agents handle conversations across chat, email, voice, and SMS, drawing on a company's knowledge bases, help articles, product documentation, past conversations, and internal policies to answer accurately, and escalating to a human with full context when a request falls outside what they should handle.
The centerpiece of the platform is what Decagon calls Agent Operating Procedures, or AOPs. Instead of building rigid coded decision trees, customer-experience teams write support workflows in plain language, when to issue a refund, when to escalate, how to handle a particular complaint, and Decagon compiles those instructions into executable agent logic. The effect is a division of labor: CX operators own the business logic and can change agent behavior without waiting on an engineering sprint, while engineers manage the underlying integrations and guardrails. Crucially, AOPs trigger real backend operations, so an agent can retrieve and update customer data, process a refund, cancel a subscription, or query a third-party system, not just talk.
Decagon unifies its channels under one intelligence layer with cross-channel memory, so an agent recognizes a returning customer and recalls earlier issues, and a conversation that starts in chat can continue by voice without the customer repeating themselves. Its voice agents handle real-time calls with interruption handling and brand-matched voices, and can also run outbound calls for things like renewals and follow-ups. Increasingly the company routes traffic through language models it has trained in-house on customer-support interactions, alongside leading foundation models.
For the teams operating these agents, Decagon adds tooling for reliability and oversight: monitoring and quality assurance, guardrails, a trace view that shows how a given procedure executed for a specific conversation, and tools that help debug and draft new procedures. It is an enterprise-focused, managed offering, deployed with the help of Decagon's own product managers and engineers rather than through self-serve signup, and priced around resolved conversations rather than per agent seat, aimed at large support operations in sectors like fintech, retail, travel, and consumer technology.
Vendor details
Canonical URL
https://decagon.ai
Category
Customer support agent
Company status
independent
Use cases & customers
Target customers
Deployment options
In practice
Your CX team wants to change how the agent handles refunds without filing an engineering ticket. Decagon's Agent Operating Procedures let them write the workflow in plain language, and the platform compiles it into executable agent logic.
A customer starts in chat, then calls back later. Decagon unifies chat, email, and voice with cross-channel memory, so the voice agent already knows the prior issue and the customer doesn't repeat themselves.
You need an agent that resolves issues, not just answers them. Decagon's agents take real actions like processing refunds, updating accounts, and querying your systems, and escalate to a human with full context when needed.
Agentic Index coverage score
10.0 / 14 capabilities · 71%
| Integrations & Tool CallingAgent Features research report + JSON Feature Rubric | Full |
|---|---|
| Workflow OrchestrationAgent Features research report + JSON Feature Rubric | Full |
| Knowledge Grounding & RAGAgent Features research report + JSON Feature Rubric | Full |
| Human Oversight & GuardrailsAgent Features research report + JSON Feature Rubric | Partial |
| Security, Identity & GovernanceAgent Features research report + JSON Feature Rubric | Full |
| Observability & AuditabilityAgent Features research report + JSON Feature Rubric | Partial |
| Memory & State PersistenceAgent Features research report + JSON Feature Rubric | Full |
| Deployment & Data ResidencyAgent Features research report + JSON Feature Rubric | Partial |
| Prebuilt Agents, Templates & PacksAgent Features research report + JSON Feature Rubric | Full |
| Triggers & Channel CoverageAgent Features research report + JSON Feature Rubric | Full |
| Model Flexibility & RoutingAgent Features research report + JSON Feature Rubric | Unable to verify |
| APIs, SDKs & MCP ExtensibilityAgent Features research report + JSON Feature Rubric | Full |
| Testing, Debugging & OptimizationAgent Features research report + JSON Feature Rubric | Partial |
| Browser & Computer UseAgent Features research report + JSON Feature Rubric | Unable to verify |
The Agentic Index coverage score grades every vendor Full, Partial or Unable to verify against the same 14 buyer facing capabilities, from public evidence only. Each capability links to how all vendors in the index score on it. How this evidence is graded
Recent platform changes
Decagon introduced Browser Actions, a feature that allows its AI agents to access and complete tasks inside any web-based system. This capability enables the agent to navigate and interact with web interfaces directly.
Bears on: Browser/computer use
View sourceDecagon has announced its availability on the AWS Marketplace, enabling enterprises to deploy its conversational AI agents directly through their AWS environments. This move extends existing AWS data residency, compliance, and access controls to Decagon deployments from day one. Additionally, it allows teams using Amazon Connect to plug Decagon directly into their current contact center infrastructure.
Bears on: Deployment / data residency
View sourceDecagon announced an accredited integration with Five9 and availability on the Five9 CX Marketplace.
Bears on: Integrations
View sourcePricing
Contact sales
hybrid
What is public
Decagon (decagon.ai - enterprise AI customer-support platform: autonomous agents resolve tickets end-to-end across chat/email/voice/SMS with cross-channel memory; Agent Operating Procedures (AOPs) = natural-language workflows; Voice 2.0 sub-second latency (+ outbound voice, Spring 2026); Watchtower QA; AI Actions for Stripe/Shopify/Salesforce) does NOT publish a price card. The pricing MODEL is public; figures are third-party only.
Billing mechanics
Usage-based (agents priced as 'workers,' not seats), layered on an annual platform fee. Two usage models: per-conversation (default/most popular - pay for every interaction whether resolved or not) OR per-resolution (higher rate, pay only when the AI fully resolves without human escalation). All custom-quoted; annual contracts; white-glove onboarding included. No native helpdesk (sits on Zendesk/Salesforce/Intercom/Kustomer).
Cost watchouts
~$50,000 annual platform fee baseline BEFORE any usage (corroborated by eesel/Quiq/Featurebase/Intercom/Fin); per-conversation model charges for unresolved/escalated interactions too; 'resolution' definition is ambiguous (Decagon's own glossary admits gray areas can cause billing disputes); no native helpdesk - you still pay for Zendesk/Salesforce/etc.; professional-services/integration fees for custom ERPs.
Variable cost rationale
Cost scales with conversation/resolution volume on top of the fixed annual platform fee; per-conversation exposes you to paying for failed/escalated interactions.
Additional watchouts
Enterprise-only economics (~$50K platform-fee redline + six-figure median ACV); per-conversation billing pays for failures; resolution definition can spark disputes; no public pricing - sales-led, slow procurement.
Overage / add-ons
Usage billed per conversation or per resolution on top of the annual platform fee; volume discounts on larger commitments; per-conversation means paying even for interactions that escalate to a human.
Sales call required
Yes, required for paid access
Free / trial
n/p
Lowest paid plan
n/p
Commercial notes
Founded 2023 (Jesse Zhang/Ashwin Sreenivas); ~$481M raised; $4.5B valuation (Jan 2026 Series D, $250M, Coatue/Index - tripled from $1.5B in ~6mo; employee tender at same valuation Mar 2026); ~$35M ARR; 100+ enterprise customers (Duolingo, Chime, Hertz, Affirm, Dropbox, Notion, Rippling, Oura, Classpass); competes with Sierra, Ada, Intercom Fin, Salesforce Agentforce, Zendesk AI
Key ambiguities
No official figures. Third-party (NOT official): ~$50K annual platform fee; per-conversation ~$0.99 and per-resolution ~$0.50 (a negotiated enterprise rate); Vendr median annual contract ~$386,120 (range ~$95,000-$590,000+); ~$50K redline (below = not a fit).
Cancellation / refund
No free trial, no self-serve signup; annual (sometimes multi-year) contracts; white-glove implementation included in contract price.
Support SLA / resale
White-glove onboarding (dedicated Agent PMs + Forward-Deployed Engineers); integrates with Zendesk/Salesforce/Intercom/Kustomer, Amazon Connect/RingCentral/SIP (voice), Shopify/Stripe; Decagon University for existing customers
Missing data
No official figures (the pricing model is public: a ~$50K annual platform fee + usage via per-conversation ~$0.99 or per-resolution; all dollar figures are third-party). Vendr median annual contract ~$386,120 (range ~$95,000-$590,000+); ~$50K redline below which you're not a fit. 'Resolution' definition can be ambiguous (billing-dispute risk).
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Alternatives to Decagon
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- Sendbird10.5 / 14Adds documented Model Flexibility & Routing
- Glia10.5 / 14Fuller documented coverage on Human Oversight & Guardrails and Observability & Auditability
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- Botpress11.0 / 14Adds documented Model Flexibility & Routing
- Gorgias9.0 / 14Fuller documented coverage on Human Oversight & GuardrailsDecagon vs Gorgias →
Similarity is computed from each vendor's Agentic Index coverage score evidence, axis by axis, not from the totals. How this evidence is graded