Instant ROI on the metrics that matter.
Leading enterprises across the globe choose Decagon to deliver concierge customer experiences.
Decagon vs. Sierra
Your AI agent should belong to you
Decagon and Sierra both deliver AI agents for customer experience. The difference is who owns the agent after launch.
Why Decagon
Operator-led AI, not services-heavy SaaS
AI agents aren't a one-time implementation. They're living systems that need constant iteration, but a services-heavy model puts every change behind a vendor's backlog.
Glass box architecture
See exactly how your agent thinks. Logic is written in natural language, every reasoning step is traceable, and granular insights show how the agent performs.
No vendor lock-in
100% of your agent logic runs on Agent Operating Procedures (AOPs). Nothing falls back to a proprietary SDK, so your team can inspect and refine the full agent.
Built to scale
Your agent gets better and broader every week, with Duet proposing improvements and new workflows based on real customer conversations.
Side by side
How Decagon and Sierra compare
Both platforms are credible choices for enterprise AI agents. This is where they're similar, and where the differences change your outcome.
Dimension
Decagon
Sierra
How agents are built
100% of agent logic runs on Agent Operating Procedures (AOPs) in natural language for any workflow complexity
Agent Studio for building simple workflows. Complex logic moves to an SDK only technical teams can touch.
Self-improvement
Duet proposes, validates, and self-tests agent updates, with human approval for each change
Separate tools for agent building (Ghostwriter) and conversation analysis (Explorer)
Testing & QA
Git-based versioning, scheduled simulations, and live A/B testing with custom traffic routing
Comparable suite: versioning, simulations, and A/B experimentation
Analytics & observability
Granular intent tagging, full reasoning-path tracing, and Watchtower reviewing every conversation against custom criteria in real time
Agent performance dashboards and custom reporting, with conversation QA via Monitors
Omnichannel & multilingual
70+ languages across chat, voice, email, SMS, WhatsApp, and custom surfaces, plus outbound capabilities
58 languages across voice, chat, email, WhatsApp, and other customer surfaces
Compliance certifications
SOC 2 Type II, HIPAA, GDPR, PCI, and ISO 27001
Similar certifications, plus FedRAMP and ISO 42001
Customer enablement
Decagon University: on-demand courses, live trainings, and certifications
Sierra University: live trainings and workshops
Pricing model
Per-conversation or per-resolution
Per-resolution
Best for
Teams that want to own and iterate on their agent directly
Teams that want the vendor to own their agent
FAQ
Decagon vs. Sierra, answered.
What's the difference between Decagon and Sierra?
Decagon and Sierra both build AI agents for enterprise customer service. The practical difference is where the work happens. Decagon puts everything (building, testing, analytics, iteration) in the product, so your team improves the agent as quickly as your business moves. Sierra's model runs more through their team and their SDK. Both approaches can get you a working agent, but they differ on how fast it gets better and on whose calendar.
What makes Agent Operating Procedures (AOPs) different from Sierra's Agent Studio?
Agent Studio validates the approach Decagon pioneered with AOPs: agent logic in natural language, owned by the teams closest to the customer. The difference is how far it goes. Decagon runs every workflow on AOPs at any complexity, so your whole team builds on a single, unified platform. When a no-code builder is paired with an engineering SDK, complex workflows split across two tools: one your business team can see, one only engineers can change.
How does Decagon's pricing compare to Sierra's?
Sierra prices per outcome (per resolution), while Decagon lets you choose per-conversation or per-resolution pricing. Most enterprises choose per-conversation for predictable costs and to avoid negotiating what counts as a "resolution."
How long does Decagon take to deploy?
Most enterprises go live in weeks rather than months. A forward-deployed team are included with every engagement to accelerate your team’s path to production.
How does Decagon fit into developer workflows?
Decagon is built to feel native to an engineering team: agent logic and tools are Git-backed with versioned workspaces for staging and production, tools are written in Python with MCP support, and the platform deploys headlessly inside your existing infrastructure.
How does Decagon keep agents from going off-script?
Guardrails run before, during, and after every conversation Before launch, simulations and regression testing validate behavior; during conversations, a supervisor model checks responses in real time and a bad-actor model screens manipulation attempts; after, Watchtower reviews 100% of conversations against your criteria. That architecture is why security-conscious enterprises in banking and healthcare run Decagon in production.
The AI concierge for every customer.