Agentic AI: The Next Generation of Support Tools
The Shift Beyond Chatbots
Chatbots handled simple inquiries. Agentic AI is doing more. Adobe just announced last week that over 70% of eligible Experience Platform (AEP) customers are now using Adobe's AI Assistant, not just for simple prompts, but to interact with specialized agents that act, decide, and escalate across complex workflows.
This isn't incremental improvement. This is a fundamental shift in how AI supports business operations.
What Agentic AI Actually Means
Traditional chatbots follow decision trees. Agentic AI systems reason through problems. Adobe's announcement shows exactly what this looks like in practice:
Multi-Step Problem Solving: Adobe's Journey Agent simplifies the creation and orchestration of customer journeys and campaigns across channels such as web, mobile, app, email and more, automatically creating journeys based on defined goals and optimizing touchpoints when customers drop off.
Context-Aware Decision Making: Adobe's Audience Agent enables teams to quickly create, scale and optimize audiences that can be activated for personalization initiatives, providing actionable recommendations and monitoring performance against organizational KPIs.
Autonomous Action Taking: The Site Optimization Agent delivers always-on support for teams to manage brand websites for high performance, automatically detecting and raising issues impacting customer engagement, such as broken back links or low performing pages.
As Adobe's Anjul Bhambhri explains, this represents "unlocking productivity for marketing teams and delivering personalized experiences at scale to drive growth" rather than simple automation.
The Technical Foundation That Changes Everything
What makes Adobe's approach significant isn't just the individual agents, it's the reasoning engine where decision science and language models enable dynamic and adaptive reasoning. This ensures that user intent can be interpreted from natural language prompts, to dynamically determine which agents are activated as part of an orchestrated plan.
Think about what this means for support operations: Instead of routing customers through rigid menu systems, agents can understand complex requests and coordinate multiple specialized AI agents to solve problems end-to-end.
For example, a customer inquiry about a billing discrepancy might trigger:
A data analysis agent to review account history
A product knowledge agent to explain features and usage
A resolution agent to process adjustments or credits
An escalation agent to involve human staff when needed
All coordinated automatically, with context preserved throughout the entire interaction.
Where Reliable AI Gets Its Data
Agentic AI is only as good as the data behind it. Adobe’s agents don’t rely on scraped internet content. They’re grounded in a company’s first-party data: CRM records, support logs, purchase history, web and app interactions. That ensures answers reflect real customer behavior, not generic training sets.
For creative and generative use cases, Adobe leans on its Firefly models, trained on rights-cleared Adobe Stock images. They also integrate with partners like Google Cloud and Medallia to extend insights across ecosystems. And all of this runs through governance and consent controls inside AEP, giving enterprises visibility into how their data is being used.
This is the foundation of trustworthy AI: own your data, govern it well, and let the AI act within those boundaries.
Why This Matters for Support Leaders
The numbers tell the story. When over 70% of eligible customers are already using these systems, we're not talking about early adoption anymore. We're talking about mainstream business operations.
Here's what support leaders need to understand: Adobe's Agent Orchestrator drives contextually relevant and goal-oriented automated actions, with support for refinement using a "human-in-the-loop" approach.
This changes what support agents do: from handling tasks to shaping experiences, coaching AI systems, and managing complex escalations that require human judgment.
Your team's role evolves from answering questions to orchestrating solutions.
Real-World Applications Across Industries
Adobe's announcement includes partnerships with major brands including The Hershey Company, Lenovo, Merkle, Wegmans Food Markets, Wilson Company, showing this isn't theoretical, it's happening now across retail, technology, and consumer goods.
The pattern is clear: organizations using agentic AI aren't just improving efficiency, they're fundamentally changing how they deliver customer experiences.
The Integration Challenge and Opportunity
Adobe addresses a critical issue: interoperability amongst AI agents in different ecosystems is critical. Their solution? Agent Composer equips businesses with tools to drive multi-agent collaboration using the Agent2Agent protocol.
For support organizations, this means you're not locked into a single vendor's ecosystem. You can orchestrate specialized agents from multiple providers while maintaining consistent experience and data governance.
What This Means for Your Strategy
We're entering a phase where 70% adoption of AI assistants is the new baseline, not the aspiration. Systems that can reason through complex problems and coordinate multi-step solutions are available today, not someday.
The critical insight: customers notice whether interactions feel both efficient and human. Organizations that lean too heavily into automation without preserving human judgment and oversight risk losing trust.
The winners will be those who master the human side of agentic AI just as well as the technical implementation.
The Bottom Line
Agentic AI represents a shift from reactive customer service to proactive experience orchestration. The technology exists. The adoption rates prove market readiness. The question isn't whether this is coming, it's whether your organization will lead or follow.