Decagon vs Cresta: AI Agent Platforms for Contact Centers
Information accurate as of February 2026. AI agent platforms evolve quickly. Verify current capabilities directly with vendors during evaluation.
TL;DR: Decagon and Cresta represent two distinct approaches to contact center AI. Decagon focuses on autonomous AI agents that resolve customer inquiries without human involvement, while Cresta offers a unified platform combining AI agents with real-time human agent coaching and conversation intelligence on a shared data foundation. The right choice depends on whether your organization needs pure automation for digital-first support or a platform that optimizes both automated and human-handled interactions across voice and digital channels.
Contact center leaders evaluating AI agent platforms face a choice that goes beyond feature comparisons. The decision comes down to what you believe about the role of human agents in your operation. Do you need a point solution that replaces human agents with AI? Or do you need one that automates what it can and augments your human agents while making them measurably better on everything else?
Decagon and Cresta answer that question differently. Decagon builds autonomous AI agents designed to resolve customer inquiries without human involvement. Cresta combines AI agents with real-time agent coaching, assistance, and conversation intelligence in a unified platform, where shared data and models connect automation, human augmentation, and analytics under one roof.
This comparison covers how Decagon and Cresta differ in automation philosophy, enterprise governance, quality management heritage, and post-handoff visibility. It also walks through what to ask vendors during evaluation and how to decide which approach fits your operation.
What does Decagon do best?
Decagon positions itself as an autonomous AI agent platform for customer support, with its architecture built around an ensemble of large language models where each model is optimized for specific tasks. The company's published case studies report high resolution rates at customers, including Substack and Flashfood. Publicly referenced customers concentrate among digital-first, fast-scaling technology companies like Notion, Duolingo, and Rippling. Deutsche Telekom is both a strategic partner and investor, though broader adoption across regulated industries like financial services or healthcare remains limited in publicly available references.
Decagon positions deployment speed as a differentiator, with its published content describing timelines ranging from weeks to approximately six weeks for full deployment. Pricing follows outcome-based and usage-based models tied to interaction volume. Buyers should request reference customers operating at similar scale and complexity during evaluation.
Where Decagon creates tradeoffs
Decagon offers Watchtower, a QA tool that flags conversations against custom criteria defined in natural language. Cresta's quality management goes further by identifying which agent behaviors actually correlate with outcomes like CSAT, resolution, and revenue, then tying those into automated scorecards. They require the same oversight and scoring infrastructure that organizations apply to human agents. Vendors that have spent years building those oversight capabilities have an advantage that newer automation-only platforms have not yet matched.
Decagon also concentrates on the automated portion of your operation. When an AI agent escalates to a human, visibility into what happens next typically ends at the handoff point. For organizations where a significant share of interactions still require human judgment, this creates a blind spot across a large portion of your customer conversations.
How does Cresta's unified platform approach differ?
Where Decagon focuses on automating customer interactions, Cresta takes a broader approach. The platform is built on three pillars that share data, models, integrations, analytics, and governance. Cresta Conversation Intelligence (Analyze) covers 100% of interactions across voice and digital channels with outcome inference, predictive customer satisfaction (CSAT) scoring, and topic discovery. Cresta Agent Assist (Augment) delivers real-time guidance, knowledge assistance, and automated summaries during live conversations. Cresta AI Agent (Automate) handles autonomous conversations across channels using a sub-agent architecture with human-in-the-loop supervision through the Agent Operations Center. Forrester named Cresta a Leader in The Forrester Wave for Conversation Intelligence Solutions for Contact Centers, Q2 2025, with the highest Current Offering score and top marks across 16 criteria.
The technical architecture uses 20+ task-optimized models, including proprietary, fine-tuned open source, and third-party large language models (LLMs). Enterprise guardrails operate across four layers covering system-level controls, supervisory blocking, adversarial testing, and automated behavioral quality management (QM).
Where Cresta differentiates from autonomous-only platforms
Three capabilities separate Cresta from platforms focused exclusively on automation. First, outcome inference models classify whether a sale was made, whether a conversation was resolved, and what the CSAT score was directly from conversation transcripts, going beyond keyword matching and sentiment analysis.
Second, Cresta has spent seven years building QM, coaching, and oversight tools for human agents, and that infrastructure now applies to AI agent oversight. Decagon has not built this foundation. Third, when a Cresta AI Agent escalates to a human, Cresta Agent Assist continues supporting that agent with full context and real-time coaching. On platforms where visibility ends at the handoff point, everything after escalation is a blind spot.
Enterprise customers span industries including aviation, telecommunications, financial services, and home security. Snap Finance, for example, improved containment rates 5.5x (from 6% to 33%) and reduced average handle time (AHT) by 40% after deploying all three pillars together.
How do these platforms compare side by side?
What should you evaluate beyond the feature comparison?
Choosing between these platforms depends less on feature checklists and more on how your contact center operates today and where you need it to go.
Match the platform to your operational mix
Most contact centers operate with a mix of automatable and human-dependent interactions. The question is whether you need a point solution for AI agent automation or a platform that improves both AI-handled and human-handled conversations together.
Some operations lose the most value on interactions that could be automated but aren't. Others lose it on human-handled conversations where agents lack real-time guidance or visibility into what behaviors drive outcomes. Most have both problems. The platform you choose should address the full picture rather than optimizing one side while leaving the other untouched.
Conversation visibility before deployment is equally important. Deploying AI agents without understanding conversation complexity, deviation patterns, and what top performers do differently is a common failure mode. Any platform you evaluate should help you build that visibility before you build agents.
Questions to ask both vendors during evaluation
- What happens to visibility after an AI agent escalates to a human?
- How does the platform help determine which conversations are good automation candidates?
- Can the system identify which agent behaviors correlate with business outcomes?
- What does quality monitoring look like for AI agent conversations specifically?
- How are guardrails structured, and what happens when the AI encounters something outside its training?
The 2023-24 ContactBabel US Customer Experience Decision-Makers' Guide found that interaction analytics received the highest positive customer experience (CX) rating of any technology evaluated (90% positive), yet 51% of organizations don't use it at all. That gap between proven impact and actual adoption suggests many organizations have room to gain from conversation intelligence alongside automation.
Cresta's unified platform for the full customer conversation
The core difference between these platforms comes down to coverage. Decagon optimizes the automated portion of your contact center. Cresta optimizes the whole thing, automated and human-handled interactions together, on a single platform where data, models, and governance are shared across Cresta AI Agent, Cresta Agent Assist, and Cresta Conversation Intelligence.
For organizations where a meaningful share of customer interactions still involve human agents, that unified approach means you are not choosing between automation and agent performance, or dealing with separate solutions that don’t communicate with each other. You get both, with the intelligence from one continuously improving the other. And because Cresta's outcome inference models identify which behaviors actually drive CSAT, resolution, and revenue, every part of the platform is oriented around results rather than activity metrics.
Visit our resource library to explore more on AI agent evaluation and contact center intelligence, or request a demo to see how Cresta's unified platform works in practice.
Frequently asked questions about Decagon vs Cresta
Is Decagon or Cresta better for regulated industries like financial services or healthcare?
Cresta has broader verified adoption in regulated industries, with published case studies from financial services companies like Snap Finance. Decagon's publicly referenced customers concentrate in technology companies. Organizations in regulated industries should verify compliance capabilities, data residency options, and audit trail depth directly with both vendors.
Can Cresta handle fully autonomous conversations like Decagon?
Yes. Cresta AI Agent handles autonomous conversations across voice and digital channels. The difference from automation-only platforms is that Cresta AI Agent is one product within a unified platform, so organizations also get conversation intelligence and human agent coaching on the same data foundation.
Do these platforms require replacing existing contact center infrastructure?
Neither platform is a contact-center-as-a-service (CCaaS) provider. Both function as AI intelligence layers that integrate with existing contact center infrastructure. Cresta supports native integrations with major telephony systems, CRM platforms, and knowledge bases. Organizations should evaluate integration complexity as part of any deployment plan.
What if my contact center needs both automation and human agent improvement?
This is where the platforms diverge most clearly. Decagon focuses on the automation side of your operation. Cresta covers both, with AI agents handling automatable conversations while Agent Assist coaches human agents in real time and Conversation Intelligence provides actionable visibility across all interactions. If your operation has a mix of automatable and complex interactions, a unified platform eliminates blind spots between your AI-handled and human-handled volume.
How long does implementation typically take for each platform?
Decagon describes a 3-6 month custom implementation timeline with dedicated technical resources. Cresta's deployment timeline varies by product and scope. Both vendors should provide implementation timelines specific to your use case, and organizations should plan for the change management work that runs alongside technical deployment.
