Are you looking to build AI agents in 2026, with a platform that goes beyond simple chatbots, built for an enterprise audience? Sierra and Decagon emerged right after the AI boom to help businesses automate customer interactions and get human-like conversations without hiring additional support.
But instead of taking their landing pages and G2 profiles at face value, let’s take a critical look at both tools.
Today, we look at their AI capabilities, ease of setup, pricing model, omnichannel capabilities, and more to help you determine what you need from a platform.
| Pros | Cons | Best for | |
| Sierra AI | – High agent accuracy – Handles large traffic volumes well – User-friendly interface | – Customer support responsiveness may vary – Higher cost compared to some alternatives – Feature expansion may require additional configuration – Deployments are typically long and expensive – Must prep knowledge manually – Must handoff to other systems for escalations | – Large enterprises – Regulated industries – Teams needing deep backend integrations |
| Decagon | – Intuitive interface for CX teams – Clear visibility into agent logic | – The split up building experience: some things happen in the UI, other things in the code Some features are still evolving – Rigid system requires engineers for basic updates – Heavy engineering/dev resources required – Must manually prep knowledge – Long time to valu – Must handoff to other systems for escalation | – CX teams wanting direct control – Teams who want their engineering teams to manage every update |
Get the better alternative to Sierra and Decagon. Book a free demo with Quiq today.
Decagon and Sierra at a glance
Sierra AI and Decagon are popular platforms focused on customer support automation with AI agents that handle complex customer interactions across voice and digital channels.
Sierra was founded in 2024 in San Francisco by Bret Taylor and Clay Bavor with a mission to automate customer service workflows and reduce reliance on human agents.
Decagon launched in 2023, backed by strong venture funding. Decagon positions itself as an enterprise platform where support teams can define agent logic using natural language instructions called Agent Operating Procedures (AOPs). Its agents automate end-to-end support tasks such as refunds, account updates, and order changes.
Sierra’s target audience tends toward larger enterprises with complex customer support needs and strong governance requirements through their Agent OS. Their positioning emphasizes deep customization, compliance, and brand consistency. Decagon, while also enterprise-ready, appeals to more technical teams that need flexibility and cross-channel automation. However, it still requires significant upfront engineering resources.
In early usage patterns, Sierra’s deployments are often tied to high-touch implementation and ongoing support, with forward-deployed engineers helping integrate deeply with existing systems. This is one of the most common reasons businesses look at Sierra alternatives.
Decagon prioritizes reducing technical complexity for customer experience teams to define workflows while still engaging engineers for system integration and guardrails.
Both platforms have the same purpose: to reduce support headcount and deflect routine queries from human agents by automating tasks that were previously handled manually. They also support omnichannel interactions across chat, email, messaging, and voice, and integrate with CRM and ticketing tools so agents operate with full context from customer data.
Both Decagon and Sierra can work for enterprise consumer brands, but they have clear-cut differences you should be aware of.
Let’s compare Sierra and Decagon side by side.
AI capabilities and agent intelligence
Sierra’s AI agents are built to execute multi-step tasks that go beyond surface-level responses. They can access customer data, interpret intent, and trigger actions such as account updates or status inquiries.
Depending on the level of your knowledge, you can use the Sierra Agent Studio (no code builder) or the Agent SDK to create agentic solutions for your unique use case.
The platform combines natural language understanding with integration into backend systems to act as a customer service agent rather than just a conversational AI interface.
Decagon’s strengths lie in its Agent Operating Procedures, which let teams express complex logic and workflows in something close to everyday language. This approach reduces friction for non-technical stakeholders while retaining the ability for engineers to manage technical boundaries and safety guardrails.
In this aspect, Decagon and Sierra are actually very similar. While you can define an AOP (or the equivalent in Sierra), you still have to build and execute it in code, for the most part. This not only adds more work, but also prolongs the onboarding significantly.
Both platforms say they support persistent context so that agents remember prior interactions in a conversation and respond in a way that feels continuous and tailored. However, because neither own voice or messaging channels directly, the touted seamless escalations may not actually be so seamless.
But it’s not the “owning” part that causes friction. The issue is that the channel is often shared with different vendors for a complete handoff. Unfortunately, this makes for a very poor experience for agents and customers.
Sierra’s intelligence stack lets teams test, measure, and refine agent reasoning with analytics tools. These mechanisms help teams understand how decisions are made, which contributes to governance and human oversight.
Decagon similarly preserves context across chat, voice, email, and messaging, letting an agent pick up mid-conversation without loss of meaning. Its design lets teams iterate on agent behavior without rewriting code, which speeds up experimentation and deployment.
In environments where customer data is foundational to decision making, both platforms connect deeply to enterprise systems so agents can act autonomously and accurately while escalating to human agents with full context when tasks exceed automated capabilities.
Workflow automation and integrations
Sierra’s platform integrates directly with CRM, billing, helpdesk, and ticketing systems, so agents not only respond but perform actions based on real data. This allows automation of complex workflows such as updating customer accounts, processing requests, or triggering follow-ups without human agent intervention.
Decagon also connects to core systems and uses data from those systems to guide agent decisions, query customer history, and take actions. Their AOP-driven workflows are offered as a fully managed service that helps teams orchestrate complicated sequences, such as checking order status, then issuing a refund if criteria are met.
Sierra emphasizes enterprise-grade connectors and SDKs to build structured workflows with guardrails that meet compliance standards. Organizations with complex backend systems benefit from the level of control this approach provides.
Decagon’s workflow model enables customer experience teams to define automation in plain language while still involving engineers for secure, reliable system integration.
Both platforms try to reduce reliance on human agents for repetitive stuff like customer support tasks, freeing support teams to focus on higher-value interactions.
One thing that you also have to account for is that neither tool has a way to get data AI-ready, which means that in either case, you’ll have to create and maintain your own knowledge base or product catalog. And for most businesses, that means more work and more tools to maintain.
Omnichannel support (chat, voice, messaging, email)
Decagon’s omnichannel capabilities let teams build an agent that works across chat, email, voice, and messaging channels with consistent behavior and memory. However, note that they don’t actually support the channels that the persistent context gets passed to.
Decagon has also developed voice capabilities that deliver natural responses while connecting seamlessly to backend systems and workflows, which makes interactions behave in a consistent way regardless of channel type, letting you design workflows for simple and complex use cases.
On the other hand, Sierra is primarily designed for omnichannel deployment across the same channels, including chat, email, voice, and messaging, with agents that leverage customer data for personalized interactions. This helps reduce customer frustration by ensuring agents respond accurately no matter where the conversation started.
Omnichannel support is essential for modern support operations because customers expect seamless transitions between platforms and quick access to accurate answers or actions without repeating themselves.
Unfortunately, neither Sierra nor Decagon does this well because of the way the tools are set up. Some context invariably gets lost along the way, and the “omnichannel” part won’t feel that way from a customer’s perspective most of the time.
Human-in-the-loop controls for your support team
If your support team cannot see what an agent is doing, you do not really have control. Human in the loop should mean visibility, clear escalation rules, and smooth handoffs. Not guesswork.
First up, Sierra is built for high autonomy. The goal is for agents to resolve issues without pulling in human agents unless necessary. That works well for high-volume tasks.
However, based on customer feedback, teams struggle with visibility into agent decisions. When something goes wrong, support teams may not immediately understand why the agent responded a certain way or why it escalated. Adjusting escalation logic can require technical involvement before support managers gain meaningful control.
Sierra does include governance layers and supervision tools. But the experience leans more engineering-led than support team-led. If your team wants hands-on control over escalation paths, expect some setup work.
On the other hand, Decagon gives teams more direct influence over agent logic through its workflow definitions. Escalation rules are tied to clearly defined procedures, which makes it easier for support leaders to understand when and why human agents are brought in.
Users often praise how intuitive the system feels. Human agents receive context during handoff, which reduces friction and improves customer satisfaction. However, Decagon doesn’t have an agent console you can rely on.
The tradeoff is speed. Decagon evolves quickly. Teams need to stay involved to keep workflows aligned with real-world edge cases and volume changes.
In the end, neither tool will give you true escalation or handoff because there is always a human element involved. In the end, you’ll just get another tool your team needs to maintain and update.
Pricing models: neither tool has predictable pricing
Pricing for both platforms is generally custom and enterprise-oriented, with costs tied to volume, automation depth, and integration complexity rather than published rates.
Sierra AI follows a custom outcome-based pricing model. You pay for “successful outcomes”, meaning resolved customer interactions completed by AI. This can sound appealing because it ties price to value delivered rather than usage volume. But in practice, it creates three problems:
- You can’t forecast cost until you know your resolution rate, which you won’t know until after deployment.
- The definition of a “resolved outcome” can vary, creating ambiguity in billing.
- Public estimates and industry commentary put typical enterprise contracts around $150,000 or more annually, with additional implementation fees often in the $50,000+ range, excluding internal engineering time.
Outcome-based pricing can align incentives, but it also means you’re betting on future performance without clear benchmarks up front. Smaller teams and mid-market support organizations often find this structure hard to budget against, especially when forecasting spend is tied to something as unpredictable as resolution rates.
Decagon’s pricing is also custom and enterprise-focused for larger companies and there’s no public sticker price. But market data suggests median contract values land near $400,000 per year, with a range from roughly $100,000 to $580,000 depending on volume and use case.
Decagon’s actual charge can take two forms:
- Per-conversation pricing: you pay a fixed fee for every interaction the AI agent handles, regardless of whether it resolves the issue. This makes budgeting easier because costs scale directly with expected contact volume.
- Per-resolution pricing: you pay only when the agent fully resolves a case on its own, which can align cost with success but introduces ambiguity about what counts as a resolution.
Both models have trade-offs. Per-conversation billing is predictable, but you’re paying even for simple or failed cases. Per-resolution billing ties spending to value, but defining what counts can be subjective and lead to disputes or cost creep over time.
Neither vendor publishes predictable pricing, and both require negotiation and volume estimates before you can budget accurately. But the qualitative difference matters:
Sierra’s model can make financial planning feel like a leap of faith because pricing depends on future success outcomes.
Decagon’s usage-based approach offers clearer expectations, particularly if you have reliable volume data, but it still sits in a high-investment tier that may be out of reach for smaller teams without big budgets. You may get some cost savings, but there’s no real difference in enterprise settings.
What real customer reviews are saying
To understand how Sierra and Decagon perform in real environments, it helps to look at direct feedback from users on G2. Below are the most consistent themes, along with verbatim quotes from reviewers.
Note that while both vendors have high average grades, they are pulled from a fairly low number of users.
Sierra reviews on G2
Overall rating: 4.4/5 based on 14 reviews
Many reviewers highlight agent accuracy, scalability, and ease of implementation.
One user writes:
“Sierra is the best tool to create AI agents for all kinds of platforms. The accuracy of the agents is very good. Sierra.ai provides extensive customer support with ease of usage. The implementation and integration have been like a cake walk.”
Another emphasizes performance at scale:
“The platform supports a large amount of traffic at the same time.”
Ease of use also stands out:
“User Friendly Interface, the software has a user-friendly interface that makes it easy for staff to navigate and perform tasks efficiently, reducing the learning curve and improving productivity.”
Across reviews, the positives center on strong agent performance, manageable implementation, and an interface that support teams can handle without excessive friction.
On the downsides, some users mention cost and variability in support quality:
“Cost and customer support, although the company provides support, the quality and responsiveness of customer service may vary.”
In short, Sierra earns praise for accuracy and scalability, but cost and support responsiveness are often flagged.
Decagon reviews on G2
Overall rating: 4.9/5 based on 18 reviews
Decagon receives praise for usability, partnership, and workflow control.
One reviewer describes the platform this way:
“Decagon is a fantastic partner. They quickly rose to the top of our RFP due to how they think about AI Agents, the tools they had to manage AI Agents, and the Decagon itself.”
On usability and control:
“Functionality – Decagon as a tool is highly intuitive, allowing our CX team to manage it effectively across various use cases without extensive technical expertise. One of the key advantages of this solution is its ability to create deterministic workflows, reducing risk while ensuring consistent and accurate responses. It’s quick to implement as well.”
Team collaboration and support also stand out:
“The Decagon team is incredibly easy to collaborate with, providing outstanding support and flexibility. Their team has truly become an extension of ours.”
Another review highlights responsiveness and iteration speed:
“They take the time to understand our needs, are open to feedback, and consistently apply it to improve the service. The best part is the ability to see real-time changes and updates, which makes collaboration incredibly smooth.”
Ease of implementation and responsive support are recurring themes in the pros and cons summary.
On the other hand, the most common concern relates to rapid change and evolving features:
“I’m excited that Decagon is rapidly scaling processes and structures… They move quickly, so it’s helpful to know what net new functionality is coming or if UI is changing.”
In some cases, users note that certain features are still in development.
“The one shortcoming of using Decagon, if you could call it that, is that they are still learning with you as they go. Decagon AI may not have all of the applications you would want currently, but their team is constantly evolving around the needs of their customers.” – G2 review
Overall, Decagon reviews emphasize intuitive workflow management, strong partnership, and flexible deployment options. The tradeoff appears to be fast product evolution, which can require teams to keep up with UI and feature updates.
The takeaway from real users
Sierra users consistently point to agent accuracy, scalability, and straightforward implementation, with concerns around cost and support responsiveness.
Decagon users focus heavily on intuitive agent logic, collaboration with the vendor team, and deterministic workflows that give teams clear control. The main drawbacks are the lack of enterprise features (which is Decagon’s biggest market) and the pace of change as the product evolves.
Which one should you get for the best customer experience?
Choosing a conversational AI support platform is less about Sierra versus Decagon and more about the capabilities your team actually needs to run customer service at scale.
Teams evaluating these tools should think about a few core factors:
- How much control you have over agent behavior
- How easy it is to modify workflows without engineering help
- Safety and guardrails around automated responses
- Omnichannel coverage across chat, voice, messaging, and email
- How the platform handles complex resolution logic
- How transparent the system is when something goes wrong
Both Sierra and Decagon attempt to solve these problems, but each comes with tradeoffs that are worth understanding before committing.
Get Sierra if…
- Your organization prioritizes strict governance and compliance controls over flexibility.
- You already have a large engineering team that can support deep integrations with internal systems.
- Your support automation relies on structured backend actions, such as pulling account data or triggering operational workflows.
- You are comfortable working within a managed AI system where the vendor has significant influence over how the agents operate.
- Enterprise-level contracts and custom implementations are acceptable as part of your rollout.
Sierra can work well in heavily regulated environments, but the setup and operational overhead can be significant for teams that want faster iteration.
Get Decagon if…
- You want to define agent behavior using natural language logic, but are willing to accept that the system can still feel rigid once deployed.
- Your team needs omnichannel support coverage across chat, voice, messaging apps, and email.
- You are comfortable dealing with engineering bottlenecks when workflows need deeper customization.
- Your support experience depends on smooth transitions between channels during a single customer conversation.
- You prefer usage-based pricing models instead of large enterprise contracts.
Decagon gives teams more visibility into how agents behave, but the platform can still introduce friction when workflows grow more complex.
In practice, neither Sierra nor Decagon is a perfect fit for every support organization. The right platform depends on how much control you want over agent logic, how technical your support stack is, and whether your team can support the engineering effort required to maintain these systems over time.
Before choosing either platform, make sure you clearly understand how your agents will be configured, monitored, and improved once they are handling real customer conversations. That operational reality often matters far more than feature lists.
Get the better alternative to Sierra and Decagon
If the tradeoffs between unpredictable enterprise pricing and heavy developer involvement feel too costly, Quiq offers a more balanced path forward.
Quiq is the agentic AI platform that turns customer needs into fast, reliable resolution. Unlike Sierra’s outcome-based pricing model, which ties cost to resolution rates you can’t forecast until after deployment, or Decagon’s expensive contracts, Quiq uses usage-based pricing aligned to conversation volume so CX leaders can plan and budget with confidence from the start.
Where Sierra requires engineering-led configuration to adjust escalation logic, and Decagon demands managing product gaps that enterprises need, Quiq gives your team direct control through Process Guides, a natural language approach to training AI that requires no coding. Your team can define agent behavior, set guardrails, and adapt workflows without waiting on an engineering queue.
Quiq also addresses one of the most common gaps CX leaders flag with both platforms: visibility. Quiq independently verifies every AI decision and provides step-by-step observability into how agents reason and act, so when something goes wrong, you know exactly why, and you can fix it without a ticket to engineering.
On the channel side, Quiq actually supports voice, SMS, chat, WhatsApp, Apple Messages for Business, Google RCS and more, with continuous context maintained across all of them. Customers never repeat themselves when switching channels or escalating to a human agent, because the conversation thread stays intact from first contact through final resolution.
And unlike platforms that treat AI and human agents as separate systems, Quiq unifies both. AI Agents handle resolution autonomously. When escalation happens, human agents receive full context through the Digital Engagement Center and are supported in real time by AI Assistants that suggest responses, enforce guardrails, and automate actions on their behalf.
The result is a platform that scales your brand intelligence, not a generic template, across every interaction your customers have, with the transparency and control that enterprise CX leaders need to stay confident in what AI is doing on their behalf.
Leading brands, including Spirit Airlines, Roku, Urban Outfitters, and Accor Hotels trust Quiq to deliver resolution at scale. Book a demo today to see how Quiq can help you reduce contact volume without giving up visibility, control, or the experience your customers expect.
Get the better alternative to Sierra and Decagon. Book a free demo with Quiq today.
