Today, when almost two-thirds (65%) of senior executives identify leveraging AI and predictive analytics as primary contributors to growth in 2025, Adobe has taken a step ahead.

Shifting from Customer Experience Management (CXM) to AI-enabled Customer Experience Orchestration (CXO), Adobe unveiled the Experience Platform Agent Orchestrator. This tool allows companies to build and control an entire fleet of AI agents to handle virtually every aspect of marketing and customer experience.

Hyper-personalised!

A key highlight of this innovation is the introduction of Adobe Brand Concierge, described as “the first brand-centric agent”. Introduced by Adobe within its Experience Platform, it hyper-personalises the website’s content, layout, and interactions for each visitor based on their past browsing behaviour, shared preferences, and product or content data.

How It works:

1. Recognises users: It greets visitors by name and remembers their previous interactions.
2. Personalised experience: It can adjust visuals, headlines, and calls to action based on user behaviour.
3. AI-Driven customisation: Uses generative AI to modify the site dynamically in real-time.

This is a multimodal offering, supporting interactions across text, voice, and images while delivering AI-driven recommendations and comparisons.

Agents at work!

Moving beyond the concierge, Adobe also unveiled ten purpose-built AI agents that perform tasks ranging from generating leads to creating content and optimising websites.

Let's explore these ten agents one by one and how they function to transform the CX landscape.

1.     Audience Agent: Intelligently analyses customer interactions across multiple channels to build refined audience segments, enabling businesses to deliver more precise and impactful targeting strategies.

🔹 Example: A travel website wants to increase carrental bookings.
The agent identifies customers with upcoming flights, cruises, or hotel stays. Filters those likely to add car rentals, creating a personalised promotion.

Benefit: Enhances targeting precision, ensuring marketing efforts reach the right customers.

2.     Account Qualification Agent: Assesses B2B opportunities by evaluating account readiness and involvement of key stakeholders, contributing to sales pipeline development.

🔹 Example: A B2B software company wants to target high-value leads.
The agent analyses company interactions, identifying businesses engaging with demos and whitepapers. Prioritises leads likely to convert and routes them to sales teams for follow-up.
Benefit: Increases sales efficiency by focusing on high-value prospects instead of cold leads.

3.     Content Production Agent: Automates the creation and assembly of content by applying predefined brand rules, reducing manual effort in content workflows. ​

🔹 Example: A fashion retailer needs social media content for a seasonal sale.
The agent generates ad creatives for Facebook, Instagram, and TikTok based on brand guidelines. It personalises visuals and text based on customer preferences and past engagement.
Benefit: Speeds up content creation, reducing time and effort needed for marketing campaigns.

4.     Data Insights Agent: Identifies trends and patterns from organisational data sources to support data-driven decisions.

🔹 Example: A coffee chain wants to know which products sell best in the morning vs. evening. The agent analyses POS data from multiple locations. It finds that iced coffee sells 20% more in the morning and suggests morning promotions.
Benefit: Provides data-driven decision-making, improving sales strategies and inventory management.

5.     Data Engineering Agent: Executes routine data operations such as cleaning, structuring, and transforming large datasets, maintaining data quality standards.
🔹 Example: A streaming service wants to clean user behaviour data for better recommendations. The agent removes duplicate and incorrect entries from millions of records. Ensures accurate customer profiles, improving recommendation algorithms.
Benefit: Enhances data accuracy, leading to better insights and more effective personalisation.

6.     Experimentation Agent: Designs and tests variations in personalisation strategies through simulations and controlled experiments to assess performance impact.
🔹Example: A cosmetics brand wants to test different email subject lines to increase open rates. The agent creates A/B tests with variations like “Exclusive Offer Inside!” vs. “Your Free Gift Awaits!” It measures performance and automatically applies the best-performing variant to future campaigns.
Benefit: Optimises marketing campaigns, ensuring higher engagement and conversion rates.

7.     Journey Agent: Orchestrates cross-channel experiences, ensuring personalised customer journeys.
🔹 Example: A fitness app wants to guide new users through onboarding. The agent orchestrates a multi-step journey, sending personalised reminders to complete workouts. It adjusts recommendations based on user progress to keep them engaged.
Benefit: Boosts customer retention by creating personalised engagement experiences.

8.     Product Advisor Agent: Helps users find the most suitable products by looking at their preferences and browsing habits.
🔹 Example: A tech retailer wantsto help customers choose the right laptop. The agent asks users a few questions(budget, usage type). Recommends the best-matching laptop and explains why itsuits their needs
Benefit: Improves shopping experiences, increasing confidence in purchase decisions and reducing returns.

9.     Site Optimisation Agent: Automatically finds and fixes issues on websites that could slow things down or make them harder to use.
🔹 Example: An e-commerce sitewants to reduce checkout drop-offs. The agent detects a high drop-off rate onmobile at the payment step. It recommends fixing slow-loading pages and addinga guest checkout option.
Benefit: Increases conversion rates byfixing friction points in the user journey.

10. Workflow Optimisation Agent: Monitors project health and streamlines approvals, boosting operational efficiency.
🔹Example: A marketing agency needs to streamline client approval processes. The agent tracks project deadlines and sends reminders to team members. Automatically flags delays and suggests priority changes to meet launch dates.
Benefit: Enhances productivity, reducing manual tracking and improving project management efficiency.

Adobe has formed strategic partnerships with major companies, including Acxiom, Amazon Web Services, Genesys, IBM, Microsoft, Rain Focus, SAP, ServiceNow, and Workday, providing businesses with a robust and integrated solution for their customer experience needs.

A peek under the hood: The tech behind!

While Adobe’s Experience Platform Agent Orchestrator dazzles with what it does, it’s equally impressive how it does it. Behind the curtain is a sophisticated tech stack that allows these AI agents to deliver context-aware customer experiences. The overall functioning of the agents is based on the following pillars of conversational AI:

1. Semantic understanding of enterprise data

At the core of the Agent Orchestrator lies a deep, semantic layer. Unlike traditional data systems that just sort and surface information, Adobe’s platform interprets the meaning and context behind data—such as customer intent, tone, behavioural patterns, and content relevance.

🧠 This allows the AI agents to "think" more like a human brand manager, making smart decisions based on nuanced understanding, not just keywords or tags.

2. Unified customer profile with real-time signals

All customer interactions—clicks, purchases, support queries, social engagement—are captured in a real-time profile that updates continuously.

🧠 This lets the AI react in the moment—like offering a promo when it senses hesitation during checkout.

3. Generative AI & machine learning at the core

The platform supports voice, images, video, and text inputs—allowing agents to personalise experiences beyond written content.

🧠 Like a digital stylist that recommends outfits based on your selfie, not just your search history.

4. Integration with enterprise systems

Adobe partners with major platforms like AWS, Microsoft, IBM, SAP, and Workday so that their AI agents can fetch and push data across departments—marketing, sales, HR, finance.

🧠 A returning customer qualifies for a loyalty upgrade. The AI syncs with SAP (customer management), Workday (internal systems), and ServiceNow (support platform) to trigger new rewards and notify support teams—without anyone lifting a finger.

5. Agent collaboration via orchestrator layer

The Agent Orchestrator acts like a maestro, directing which AI agent steps in, when, and how they collaborate for a seamless experience.

Conclusion

This innovation comes at a pivotal moment when leveraging AI and predictive analytics is recognised as a primary growth driver by almost every organisation around the world.

As Adobe pushes the boundaries of what's possible in customer experience, it joinsa competitive landscape of CX players from CCaaS, CRM & Ticketing and Conversational AI domains. The CX vendor landscape is getting hot with players like Adobe and Microsoft making AI-enabled experiences a core value proposition.

These advancements collectively signal a transformative era in customer experience, where AI-driven solutions are not just enhancing but redefining how businesses engage with their customers.