Decagon vs Sierra
Published September 10, 2026 · Reviewed by the Fidiora team
Decagon and Sierra both build enterprise AI agents that resolve customer conversations and take actions. Both sell under quoted agreements to larger companies. The differences that matter in practice are implementation model, action-taking guardrails, and how each defines and bills a resolution.
Key takeaways
- Both are enterprise-first, quoted, and implementation-heavy.
- Action-taking guardrails and audit trails are the substantive comparison, not answer quality alone.
- Both assume an existing support stack underneath.
- Neither is a realistic fit for a small team wanting to be live this week.
Decagon vs Sierra at a glance
| Criterion | Decagon | Sierra |
|---|---|---|
| Category | AI support agent platform | Conversational AI agent platform |
| Buyer | Larger companies | Enterprises |
| Commercial model | Quoted, commonly outcome linked | Quoted, commonly outcome linked |
| Action-taking | Yes, within configured scope | Yes, a central emphasis |
| Implementation | A project | A project |
| Brand control | Configurable | A stated emphasis |
| Internal ownership | Dedicated | Dedicated |
| What to verify first | Permission scoping and audit logging | Permission scoping and audit logging |
What each product is
Decagon
Decagon builds AI support agents aimed at resolving customer conversations end to end, sold primarily to larger companies with significant volume.
Commercial model: Quoted enterprise agreements, commonly linked to resolution volume.
Best for: Enterprises wanting a language-model-native support agent with a dedicated programme behind it.
Sierra
Sierra builds conversational AI agents for customer experience, positioned at enterprise buyers and emphasising brand-consistent, action-taking agents.
Commercial model: Quoted enterprise agreements, commonly outcome linked.
Best for: Enterprises prioritising brand-consistent, action-taking agents across the customer experience.
The differences that actually matter
Guardrails matter more than eloquence
Both will demo well. The questions that separate them in production are how narrowly permissions can be scoped, whether every action is logged, what happens on partial failure, and whether risky actions require confirmation.
Implementation model
Ask each vendor exactly who does the work: their team, yours, or a partner. That answer drives your timeline and your total cost more than the licence does.
Outcome definitions again
Both price on outcomes, and both write the definition. Ask about abandonment, handoffs, reopens, spend caps, and audit rights, and get the answers in the contract rather than the deck.
Vendor pricing and packaging change often. Confirm current details on Decagon and Sierra before deciding.
These are large commitments aimed at large companies. Teams that want grounded AI resolution without an enterprise programme can start with Fidiora: live in under an hour, grounded in existing content, priced per genuine resolution, with handoffs never billed.
Questions
What should I ask an enterprise AI support vendor?
Are these platforms safe for action-taking?
How long until value?
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