What is Decagon AI? Use cases, limitations, and better alternatives for enterprise support

Decagon promises to replace your support agents with LLMs. But can it replace your support operation?

As customer expectations grow and support volumes spike, more businesses are turning to generative AI — not for static flows or scripted chatbots, but for systems that can understand context, take action, and scale.

Platforms like Decagon have emerged to meet that demand, offering fast, automated resolutions through advanced LLM-powered agents. And for many fast-moving teams, that’s a compelling pitch.

But as operations grow more complex — spanning channels, geographies, BPOs, and SLAs — the question isn’t just whether Decagon can handle volume. It’s whether it can handle the whole operation.

That’s where more complete platforms like Assembled come in. Built on a workforce management foundation, Assembled delivers the same generative power — with added strengths in omnichannel orchestration, hybrid team collaboration, and real-time performance insight.

For support leaders who need to move faster and operate smarter, the right AI solution doesn’t just deflect tickets. It orchestrates your entire support system.

What is Decagon AI?

Decagon is an enterprise AI platform that helps support teams automate customer service using large language models (LLMs). Its AI agents can autonomously handle tasks like answering product questions, processing refunds, and canceling subscriptions — helping businesses reduce support costs and scale faster.

Founding and funding

Co-founded in 2023 by Jesse Zhang and Ashwin Sreenivas, Decagon has quickly emerged as a leading player in AI-powered customer service. The company has raised $200 million in funding across four rounds — including a $100 million Series C in mid-2025 led by Andreessen Horowitz and Accel, valuing the company at $1.5 billion.

Growth and product traction

Decagon’s early traction has been driven by its ability to replace outsourced support labor with AI agents that can handle common tasks like technical troubleshooting, order processing, and subscription cancellations. The company reports over $10 million in signed annual recurring revenue.

Technology and architecture

Its agents are built on top of foundation models from OpenAI, Anthropic, and Cohere, layered with company-specific data from help centers and historical conversations. In 2024, Decagon partnered with ElevenLabs to launch voice agents for more natural, human-sounding conversations.

While the platform excels at fast automation, its architecture is optimized for speed and autonomy, often favoring self-contained resolution over collaboration, context sharing, or deeper operational integration.

Decagon’s key features and capabilities

Decagon offers a set of advanced AI features designed to automate and improve digital customer service. Below is a breakdown of its core capabilities — and how they appeal to enterprise support teams looking for faster, more efficient ticket resolution.

Context-aware responses

Powered by LLMs and proprietary NLP models, Decagon can generate human-like responses in real time. It draws from past interactions, customer data, and conversation history to provide replies that feel tailored and contextually accurate. This enables more natural interactions — and reduces the need for repeat questions or rigid flows.

Continuous learning from interactions

Decagon’s AI agents are designed to improve over time. As the system engages with more conversations, it uses those interactions to refine its intent detection, improve prompt grounding, and learn how to handle nuanced or previously unseen queries. This feedback loop enables the AI to get better at resolving issues autonomously without needing constant manual updates.

Scalable omnichannel support

The platform supports integration across major customer communication channels, including web chat, email, and voice. Decagon can manage these interactions simultaneously and transfer conversations between channels with continuity, offering a single AI experience across multiple touchpoints.

Actionable insights and analytics

With tools like Watchtower, Decagon gives teams visibility into how their AI agents are performing — including resolution rates, fallback rates, and areas where the model may need retraining.

While helpful for monitoring AI outcomes, these insights are limited to the AI layer and don’t show how human agents and AI work together, or how performance trends across the full support team.

Who are Decagon’s customers?

Decagon’s AI agents are primarily used by fast-scaling, digital-first companies with high support volumes and a strong appetite for automation.

Notable customers

Its customer base includes prominent tech and consumer brands such as:

• Notion
  
• Duolingo
  
• Substack
  
• Bilt
  
• Rippling
  
• ClassPass
  

These organizations prioritize speed, scale, and operational efficiency — making them well suited to Decagon’s automation-first approach.

Common customer traits

These businesses typically share a few key characteristics:

• High volume of chat, email, or in-app interactions
  
• Repeatable workflows that lend themselves to automation
  
• Pressure to reduce costs and increase agent productivity
  

In these environments, Decagon shines by deflecting routine inquiries and handling basic requests with minimal human involvement. One customer, Bilt, reportedly reduced its support headcount from hundreds to just 65 using Decagon. ClassPass uses its agents to handle over 2.5 million customer conversations, reducing cost per reservation by 95%.

Where customer needs outgrow Decagon

But as support environments expand across geographies, channels, BPOs, and SLAs, Decagon’s standalone architecture can start to show limitations. Teams needing tight coordination between AI and human agents often struggle with gaps in routing, context transfer, and real-time workforce visibility.

In these cases, the lack of deeper workforce management integration or flexible escalation logic can hinder support quality and slow down resolution — especially when customer expectations are high and tolerance for friction is low.

Common Decagon use cases

Decagon AI is most commonly deployed to automate high-volume, repetitive support tasks — especially in fast-moving environments where rapid response times are critical. Its generative AI models are designed to deflect tickets from human agents by delivering contextual answers, surfacing relevant help center articles, and managing basic troubleshooting workflows.

Common use cases

Automating routine inquiries
Decagon can resolve straightforward queries related to billing, account access, shipping updates, and password resets. By handling these questions autonomously, it helps teams reduce backlog and improve time to first response.

Managing peak demand periods
During seasonal spikes or product launches, Decagon is often used to absorb overflow volume. While it can scale responses across chat and email, the lack of real-time staffing alignment or smart routing can lead to bottlenecks when agent escalation is needed.

Real-time contextual assistance
The platform uses conversation memory and historical context to personalize replies, enabling a more fluid and human-like experience. This helps reduce customer frustration when navigating multi-step issues.

Sentiment detection and tone management
Decagon can identify when a customer is confused or frustrated and adjust tone accordingly — or trigger fallback logic to escalate to a human. However, that escalation process isn’t always smooth.

Complex workflows like refunds and returns
Even seemingly simple tasks like refunds aren’t always straightforward — especially for businesses managing loyalty programs, product categories, return conditions, and internal approval rules. What looks like a basic request often requires pulling data from multiple systems and applying nuanced logic. Decagon may handle the front end of these requests, but it often falters when real complexity kicks in. Assembled, by contrast, supports hybrid workflows and smart handoffs that ensure nothing slips through the cracks — no matter how edge-case the request.

Support doesn’t run in a vacuum — it runs on messy systems, shifting workflows, and real human expectations. Decagon’s LLMs may be strong, but without deep operational orchestration, even simple tasks can fall apart in production. What looks seamless in a demo often struggles in the wild, especially when teams rely on brittle integrations or lack the context to coordinate effectively between AI and human agents.

Where Decagon’s use cases fall short

While Decagon handles many common queries well, it tends to struggle in edge cases — particularly when human intervention is required or when complexity increases. For example, teams that need seamless AI-human collaboration often run into limitations. Decagon doesn’t always pass full context to agents, leading to repetition or confusion on the customer’s end.

By contrast, Assembled’s AI agents are built for hybrid support environments, where AI and human agents work in tandem. Real-time agent copilots, intelligent handoff logic, and deep workforce management integration ensure that escalations are smooth and customer satisfaction doesn’t drop when AI reaches its limits.

In other words: Decagon is strong on automation. Assembled is strong on orchestration — across systems, channels, and teams.

Decagon pricing

Decagon does not publicly list pricing on its website, and no official pricing tiers are available through third-party marketplaces or documentation. That said, it’s been reported that Decagon offers two usage-based pricing models:

• Per-conversation pricing: A flat rate for every AI-handled interaction, regardless of the outcome.
  
• Per-resolution pricing: A higher rate, charged only when the AI agent fully resolves the customer’s issue.
  

The second model is positioned as more performance-driven, but in practice, most teams default to the per-conversation model. It’s easier to explain internally, more predictable to forecast, and avoids the murky work of defining what counts as a “resolution.” (Even Decagon notes that this can be up for debate.)

That flexibility might seem appealing, but it doesn’t eliminate uncertainty. Decagon doesn’t publish any baseline rates, usage thresholds, or real-world cost examples — making it hard for support leaders to estimate spend, especially during volume surges or seasonal spikes. And because both models are usage-based, costs can compound quickly without clear guardrails.

Why that matters for support teams

For teams managing tight budgets, shifting staffing models, or executive pressure to prove ROI, this kind of unpredictability adds risk. Even if automation promises cost savings, those gains are hard to validate when pricing is opaque and spend varies based on conversation volume or loosely defined success metrics.

By contrast, platforms like Assembled offer AI pricing that’s transparent, scalable, and grounded in real operational outcomes — not just raw usage. Teams get clear visibility into both AI and human performance, direct integrations with tools like Zendesk and Salesforce, and pricing that scales with actual value delivered. That makes it easier to right-size investments, forecast spend, and prove ROI — without relying on vague seat licenses or flat fees.

When your support operation runs on complexity, you need more than flexible pricing — you need pricing that’s predictable, measurable, and built to scale with your business.

Where Decagon falls short

While Decagon offers a compelling solution for teams looking to automate customer interactions, its product shows real limitations when evaluated through the lens of operational scale, human-AI coordination, and workforce readiness. For support teams managing unpredictable demand, blended workforces, and high service expectations, these gaps can lead to real risks.

Limited workforce management integration

Decagon was built to resolve tickets, not to run support operations. It doesn’t offer native integrations with workforce management tools or staffing systems, which makes it difficult for CX leaders to connect AI activity with real-time agent coverage, scheduling accuracy, or intraday performance. This disconnect creates blind spots — especially in environments where human agents, BPOs, and AI agents are all in play.

That also limits how quickly teams can identify new automation opportunities. Without visibility into agent queues, case timelines, or resolution patterns, it’s harder to spot the low-hanging fruit — the kinds of tasks that AI can reliably take on without hurting quality. In contrast, Assembled gives teams deep insight into queue-level performance and case lifecycle data. That means support leaders can see which tickets agents resolve fastest, which queues are most repetitive, and which workflows are already handled by Tier 1 or BPO teams — all strong signals for automation readiness.

It’s the benefit of having workforce management and AI built into one platform: unified data, a single UX, and shared intelligence that accelerates both efficiency and innovation. Instead of guessing where automation might help, teams using Assembled can target it precisely — and measure its impact in real time.

Challenges with scalability during demand surges

Decagon’s automation promises fast responses and high resolution rates — but several reviewers report performance degradation under peak load. During high-volume periods, customers have cited slower response times, unanticipated AI errors, or the need to route more tickets back to human agents than expected.

For support leaders trying to avoid SLA breaches or agent burnout during peak periods, this unpredictability undermines the core value AI is meant to deliver. Without dynamic scaling features or adaptive routing logic, Decagon’s automation can become brittle under pressure.

Gaps in AI-human collaboration

Decagon’s design centers on autonomous resolution, but it can fall short when customer conversations require a smooth handoff to human agents. In some cases, support teams have noted challenges with passing full context during escalations, resulting in customers repeating themselves or agents having to backtrack. Delays can also arise from unclear escalation paths or handoff logic.

This lack of seamless collaboration between AI and humans can frustrate customers — and add drag to the support process. In contrast, modern support operations require tight AI-human alignment, where agents are empowered with full context and copilots can step in without losing the thread.

Alternatives to Decagon

Decagon may be one of the more visible names in AI-powered support, but it isn’t the only — or the most complete — option for modern support teams. For organizations that need trustworthy automation and operational depth, Assembled AI agents offer a more flexible, integrated path forward.

Assembled’s AI agents are built on top of a workforce management foundation, which means they don’t just resolve tickets — they help run your entire support operation more efficiently. From omnichannel orchestration to real-time performance analytics, Assembled’s approach fills in the gaps where standalone AI platforms like Decagon fall short.

Where Decagon automates, Assembled orchestrates. And in complex support environments, orchestration — not just automation — is where the real leverage lives. Automating a single conversation is table stakes. The real value comes from coordinating across people, channels, workflows, and systems to deliver consistent, high-quality support — even under pressure.

Key differences between Decagon and Assembled AI agents

If you’re evaluating top alternatives to Decagon AI, here’s how Assembled AI agents stack up.

What to look for in a Decagon alternative

When evaluating alternatives to Decagon or any generative AI tool for support, it’s critical to prioritize capabilities that go beyond scripted deflection or isolated channels. Here are three must-haves to look for:

Omnichannel excellence

Modern support isn’t limited to a single touchpoint. The best AI agents can engage across channels — and do it with full context. Assembled AI agents unify conversations across voice, chat, and email, so nothing gets lost in translation and customers get consistent, high-quality responses no matter where they show up.

Comprehensive analytics

It’s not enough to know whether an AI agent resolved an issue. You need visibility into how it performed, how it impacted team SLAs, and where it fits into the bigger picture.

Decagon offers analytics focused on AI agent performance — like resolution rate, fallback frequency, and retraining needs. But for teams running hybrid operations, that view is incomplete. It doesn’t show how human agents and AI copilots are working together — or whether those tools are actually driving better outcomes.

Assembled fills that gap. With a unified performance dashboard, teams can track both AI and human metrics side by side — including how individual agents perform with and without copilot support. That kind of visibility helps leaders fine-tune training, measure adoption impact, and confidently scale what’s working.

If your agents are using AI every day, you should be able to measure its value — not just assume it’s helping.

Adaptability during surges

AI is most valuable when things get unpredictable. Assembled’s AI agents are built to adapt to fluctuations in volume, hand off to human agents intelligently, and scale without sacrificing response quality. Whether you’re facing seasonal spikes or a surprise campaign, your support quality stays consistent.

Unlock better results with Assembled AI agents

Decagon has made a name for itself in the AI support space — and for good reason. Its generative models handle routine queries with speed and accuracy. But for support teams that need more than just automation, the gaps quickly show: no deep workforce alignment, limited analytics across agents and AI, and rigid escalation flows that can break under pressure.

Assembled AI agents fill those gaps with a fundamentally different approach. By combining LLM-powered automation with real-time orchestration, Assembled helps teams not just respond faster, but operate smarter. From intelligent routing to human-AI collaboration to built-in performance tracking, it’s everything modern support teams need to stay agile, efficient, and customer-first.

Don’t settle for isolated AI. Book a personalized demo to see how Assembled can power your entire support operation — from first message to final resolution.