Sierra vs Decagon: How to compare them in 2027
- Choose Decagon if your CX and engineering teams work closely and you want direct control of AI agent workflows through natural-language Agent Operating Procedures
- Choose Sierra if you want the vendor to own most of the build and you can accept vendor timelines for changes after launch
- Both platforms still need engineering work for integrations, and neither offers one quality management program covering AI and human agents
- Choose Cresta if you want AI agents, real-time augmentation for human agents, and conversation analytics on one platform trained on your own conversations
If you're adding autonomous AI agents to your customer service operation, you'll likely evaluate Decagon and Sierra.
Decagon gives CX teams direct control over agent workflows through natural-language Agent Operating Procedures (AOPs). But it takes real engineering work to connect backend systems before those teams can own changes.
Sierra, on the other hand, handles most of the build for you and launches fast. But changes beyond its self-service options can depend on Sierra's timelines, and the agent can stumble on unusual requests.
We'll go deeper on each platform's pros and cons to make your evaluation easier. But first, here's how we compared them.
Note: This guide was written on 11/3/2026. The information below can change in the future.
How we compared Decagon and Sierra
We used analyst reports from firms like Gartner and Forrester, user reviews from G2, and our own conversations with customers and prospects who've evaluated and/or used at least one of the two platforms.
Using these inputs, we evaluated both platforms against five criteria:
- Deployment model and ownership after launch: who builds the agent, and who can change it once it's live, decides how fast you respond to policy shifts
- Workflow control and change velocity: how quickly a change moves from request to production decides whether the agent keeps up with your business
- Omnichannel consistency and handoff quality: context has to carry across voice and digital channels and survive an AI-to-human handoff, or customers repeat themselves
- AI oversight, testing, and quality management: generative AI agents vary their responses, so you need testing before release and supervision during live conversations
- Outcome measurement beyond containment: containment is a starting point, but you also need to tie agent behavior to resolution, sales, and CSAT
See how Cresta can support your AI and human agents.
Decagon overview
Decagon is an AI agent platform for customer service across chat, email, and voice. Its core building block is the Agent Operating Procedure (AOP), which pairs natural-language instructions with code-level configuration that connects the agent to your business systems.
Pros
- CX teams can change agent behavior in plain language after setup. Users (like the G2 reviewer below) often credit AOPs with letting them update workflows without rebuilding from scratch
- The agent takes real actions. AOPs connect to backend systems, so the agent can complete tasks like refunds and account changes instead of only answering questions
- Strong financial footing. Decagon raised $250 million earlier this year, enabling them to make significant investments on their team and product for years to come
Cons
- Setup is engineering-heavy. Users on G2 report an average implementation time of 2 months, and many users (like the one below) say that connecting APIs, permissions, and safeguards falls on your technical team
- CX ownership has limits after launch. System permissions, API connectivity, and testing still need internal technical capacity once operations teams take over
- Lack of quality management functionality. There’s no ability to score human agents on the same rubric as AI agents, so you’ll have to invest in additional quality management tooling
Related: The top alternatives to Decagon
Sierra overview
Sierra is an AI agent platform for customer service across voice and digital channels, built on infrastructure it calls Agent OS.
Sierra's model emphasizes managed deployment. Its team handles much of the coding, integration, and launch work, which suits enterprise consumer brands without in-house AI engineering.
Pros
- Fast time-to-launch. Users on G2 say that it only takes 1 month, on average, to implement the platform
- The agent handles conversations end to end across connected systems. Users are particularly complimentary of the agent’s ability to grasp context and take actions rather than give scripted replies. The G2 review below is a good example
- Deep funding behind the roadmap. Sierra raised a $950 million Series E, giving them significant room for investment
Cons
- It can struggle with complex or unusual requests. Users (like the G2 reviewer below) often say the agent can repeat itself and still need a human to step in
- Changes can depend on Sierra's timelines. Updates beyond the self-service options may need Sierra's team, which can slow iteration after launch
- Sierra doesn't support quality management tooling. Like Decagon, this forces you to invest in an additional QM software
Given all the pros and cons of each platform, it can be hard to decide between the two. We'll help you navigate the decision more directly in the next section.
Related: A guide to Sierra competitors
Decagon vs Sierra: which should you choose?
Choose Decagon if your team has in-house technical capacity and wants direct ownership of AI workflow definitions after setup.
Choose Sierra if you want managed deployment, have limited in-house AI engineering, and need a conversational agent live quickly.
If your must-have is one program for AI and human agents, neither platform is built primarily for that.
To that end, ask each vendor how they support handoffs to human agents and what context the human agent can receive during the call to suss out their agent assist tooling’s depth.
Don't settle for Decagon or Sierra
Cresta, the AI operating system for CX, offers a unified platform for human and AI agents.
Here's where Cresta differs from Decagon and Sierra:
- Automation starts from your real conversations: Automation Discovery scores each topic on outcomes, complexity, deviations, and tool needs, then gives you a blueprint for what to automate first and what to leave with people. Sierra starts from the SOPs, documents, and transcripts you upload, and Decagon’s reporting shows topics and trends but doesn’t size the impact of automating them
- Supervisors can steer a live AI conversation: the Agent Operations Center lets supervisors guide or step in during high-risk moments, so problems are prevented or fixed quickly. Decagon’s Watchtower and Sierra’s monitoring only flag and alert on conversations
- One quality rubric scores AI and human agents: the same behavioral scoring covers both, so quality standards hold as automation scales. Sierra’s Monitors grade only the conversations its own AI agents handle
- Human agents get guidance based on behaviors that drive outcomes: Agentic Assist works inside your existing tech stack, and teams can test a guidance trigger on past conversations before agents ever see it. Decagon Assist ties hints to existing AI-agent procedures, and Sierra’s Live Assist is built from its AI agent’s technology
Learn more about Cresta and why it’s a better alternative to Sierra and Decagon by scheduling a demo.
