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Decagon vs Sierra
A plain-English comparison to help you choose between them.
The two AI-native enterprise support platforms, and a comparison no feature table settles. Sierra, founded by Bret Taylor and Clay Bavor, deploys one agent across chat, voice, SMS and WhatsApp to resolve conversations end to end; Decagon sells the same brief, agents on chat and voice with escalation that preserves context plus the testing and monitoring tooling around them, and both are quote-only enterprise sales. Pick Sierra when its agent's performance on your own conversation mix proves out in a pilot; pick Decagon when the bake-off runs the other way, because with these two the pilot is the decision.
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Side by side
- Summary
Decagon builds enterprise AI agents for customer support, described on its own site as the AI concierge for every customer: agents that hold real conversations across chat and voice, resolve what they can end to end, and hand the rest to humans with context. It is one of the category's heavily funded independents, self-reporting a large Series D in early 2026.
The commercial shape is strictly enterprise: no self-serve signup, no published pricing, a sales conversation as the only door. Its case studies carry resolution and deflection figures for named customers; those are the vendor's own numbers, and evaluations should treat them as claims to test in a trial rather than benchmarks to plan on.
MoreLess
It belongs on the shortlist for large support operations weighing the new AI-native agent platforms against the AI layers of the incumbent helpdesks.
- Best for
- Enterprise support agents across chat and voice
- End-to-end resolution with human handoff and context
- Large support operations adopting AI-native platforms
- Deployments scoped and priced through a sales process
- Teams comparing agent-native entrants against helpdesk AI layers
- Cost
- Enterprise
- Ease
- Openness
- Hosted service
- Data
- It works inside your support stack and customer conversations, so data-handling, retention and sub-processor terms belong in the contract negotiation, not a settings page after rollout.
- Summary
Sierra is an enterprise AI agent platform for customer service, founded by Bret Taylor and Clay Bavor. One agent resolves conversations end to end rather than deflecting them, deployed across chat, voice, SMS, WhatsApp, email and a ChatGPT channel, with governance and guardrails built in. Ghostwriter turns procedures and transcripts into production-ready agents, Agent Studio manages them, Insights watches conversation quality, and Experiments runs multivariate tests of agent behaviour.
The commercial shape is strictly enterprise: pricing is outcome-based, so you pay for the resolutions the platform delivers rather than for seats, and there is no public rate card. It raised a major funding round in 2026, and it suits large support operations ready to hand real conversation volume to AI agents under supervision.
- Best for
- Resolving customer conversations end to end across chat and voice
- One governed agent deployed across chat, SMS, WhatsApp, email, voice and ChatGPT
- Building production agents from plain-English descriptions with Ghostwriter
- Enterprise support operations with serious conversation volume
- Paying for resolutions delivered rather than seats
- Cost
- Enterprise
- Ease
- Openness
- Hosted service
- Data
- Customer conversations and connected customer records flow through a closed platform. The Agent Data Platform holds agent memory and integrations into your customer systems, so procurement should treat it as a core data processor. Deployments reportedly handle regulated interactions such as insurance claims and financial services conversations, where AI agent outputs need human oversight and audit trails, automated customer decisions may trigger consumer-protection and disclosure obligations, and the platform does not substitute for compliance review of agent behaviour.
Pricing
- Decagon
- No published price
- Sierra
- No published price
By area
Where each one pulls ahead, area by area.
| Area | Decagon | Sierra |
|---|---|---|
| By job | ||
| Customer support | Decagon brings the heavily funded independent's platform to the same enterprise brief | Sierra is built for the operation that wants one governed agent instead of a bot per channel, with the same guardrails applying wherever the conversation starts |
| Operations | Decagon expects the knowledge and integration work around the agent to be planned before a serious pilot starts, which is where the effort actually lands | Sierra turns support economics from headcount plans into resolution forecasts, with containment and conversation quality watched as operational telemetry and cost scaling with resolution volume |
| By task | ||
| Customer support & chatbots | Decagon integrates with the support stack already in place and grows agent scope as measured quality allows, so a team settled on a capable helpdesk AI layer should test that layer first | Sierra's deployments reportedly reach regulated interactions such as insurance claims and financial services, so human oversight and audit trails sit on the agent rather than beside it |
Common questions
How should a support leader actually choose between them?
Run both on the same slice of real volume with resolution criteria agreed in advance. Both vendors publish strong case-study figures, and both sets are vendor claims: resolution tracks your content quality and conversation mix, so the number that matters is the one your own pilot produces.
What does either cost?
Neither publishes pricing. Both sell through scoped enterprise conversations, and deployment size, channels and integration depth set the number. Budgeting starts with a demo on each side, which also means smaller teams wanting self-serve automation are outside both vendors' shape entirely.
Do they handle voice as well as chat?
Both position agents across chat and voice, with Sierra also naming SMS and WhatsApp in its channel list. Voice quality is exactly the kind of claim to test in a pilot rather than take from either vendor's materials, since accent, interruption handling and escalation feel differ by deployment.
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Tool facts last checked August 2026