Optimizely's Opal and the Future of AI-Ready Experiences

At this year’s Opticon, Optimizely introduced something that felt bigger than a product update. Their new multi-agent system, Opal, isn’t just another AI assistant — it’s a framework for orchestrating AI agents into structured, repeatable workflows.

By coordinating multiple specialized AI capabilities across brand context, data sources, and goals, Opal transforms isolated AI applications into connected agentic workflows. The outcome isn’t just a faster launch, but a foundation for high-performing digital experiences built for both human audiences and artificial intelligence (AI) systems.

From Design File to Live Site in a Single Flow

The first demo showed how AI orchestration tools streamline the data process from start to finish.

Working with a triathlon landing page in Figma, Opal scanned the design, checked against existing CMS content types, and generated only the new ones required. These were saved directly to the CMS for future reuse, while React components were built and pushed to GitHub for developer review.

Marketers could then refine copy, layouts, and images in real time using the Visual Builder, without waiting on developer cycles. Before launch, Opal automatically added metadata and structured Q&A content — ensuring the page was discoverable by humans, AI models, and generative AI crawlers alike.

The demo underscored the importance of data quality and structured content. For brands, this means digital assets become part of durable data pipelines that feed both customers and AI technology with accurate, reusable information.

Experimentation Without the Bottlenecks

A second demo revealed how Opal accelerates AI workflows in testing.

For that same triathlon page, Opal proposed test ideas, generated variants, and built a full plan — including audiences, metrics, and data pipelines. Marketers could make no-code changes in the visual editor, while Opal handled monitoring, notifications, and performance summaries.

Results spoke volumes:

• 79% faster test velocity
• ~10% lift in win rates

By handling repetitive tasks, Opal lets data scientists and marketers focus on outcomes — not mechanics. Experiments become part of a continuous AI application workflow where insights feed directly into future optimizations.

Building for Continuous Improvement

Opal is a signal of where AI solutions are headed. Key enhancements include:

• A rebuilt Visual Editor designed for single-page applications and real-time editing.
• An agent directory for configuring task-specific AI agents (compliance, copywriting, personalization).
• Open source-friendly integrations with Siteimprove, Coveo, Contentsquare, and commercetools that extend AI systems into existing stacks.

These capabilities can be chained together into orchestrated agentic workflows — enabling organizations to shift from project-based updates to continuous AI-driven improvement.

Why This Matters

For marketers and developers, Opal reduces friction without losing enterprise controls. For brands, it creates high-performing, AI-ready experiences that evolve through data rather than costly redesigns. And for both human and machine audiences, it ensures data pipelines are clean, structured, and optimized for new forms of discovery.

Opal highlights a future where digital platforms don’t just support content workflows — they orchestrate them using artificial intelligence and AI orchestration tools that connect people, processes, and platforms.

Ready to Put Opal Into Practice?

We’ve created a 7-Step Opal Implementation Checklist to help you plan, prioritize, and execute with confidence. Download your copy today and start building AI-ready experiences.