News Analysis
The Gist
- Adobe moves AI deeper into the dev stack. New AEM coding agents go beyond content generation, analyzing site architecture and proposing code changes inside real projects.
- Skills-based agents reshape implementation work. Refactoring, scaffolding, error diagnosis and content-model updates can now be handled by specialized agent “skills” that reduce routine developer load.
- Faster delivery—with governance required. AI speeds migrations and reduces context switching, but strict guardrails, permissions and review workflows are essential to keep production environments safe.
- Partners see early productivity gains. Agencies and integrators report quicker migrations, fewer bottlenecks and more focus on higher-value architecture work.
- AI co-developer era begins. Agents won’t replace developers, but they will automate repeatable tasks—pushing AEM teams toward oversight, validation and architectural decision-making.
Adobe is piloting a new class of AI “coding agents” inside Adobe Experience Manager, designed to help developers understand site structure, propose code updates and apply changes across AEM projects. Built on top of Edge Delivery Services and AEM’s content model, the agents can be given tasks, equipped with skills, and then set loose on routine implementation work that otherwise eats up developer time.
The move pushes AEM beyond content-focused AI features into AI-assisted development. For AEM customers, the promise is faster implementation and fewer context switches. For consultants and enterprise teams, it raises bigger questions about skills, governance and how far AI should be allowed to go in modifying production systems.
Table of Contents
- Adobe Brings AI Agents to AEM Development
- What AEM’s AI Coding Agents Actually Do
- Why This Matters for AEM Customers and Partners
- Solving for Classic AEM Holdups
- Architectural and Governance Implications
- Developer and Consultant Reactions
- Agentic AI Reshapes Digital Experience & Content Platforms
- From Static to Dynamic Orchestration
- Content Management Evolution
- Business Impact of AEM Coding Agents
Adobe Brings AI Agents to AEM Development
Adobe has unveiled a new class of AI coding agents within AEM that are designed to work directly in the developer workflow. According to the AEM.live announcement, these agents can ingest project documentation, analyze component architecture, propose code changes and in some cases apply them across live AEM sites.
How The Agents Embed Themselves Inside Real AEM Workflows
"Adobe has long helped businesses deliver engaging experiences to their customers, by turning digital data into actionable insights. We are now leveraging agentic AI to build specialized agents and embedding them into data, content and experience creation workflows," said Anjul Bhambhri, senior VP of engineering at Adobe Experience Cloud.
The agents sit within AEM projects and are enabled via the Edge Delivery Services layer, giving them access to AEM’s content model and APIs. For developers working on AEM builds and integrations, this marks a shift: the agent isn’t just an assistant prompting suggestions; rather, it becomes an active participant in the dev pipeline.
Related Article: What to Expect at Adobe Summit 2025: GenAI, AEM Upgrades and More
What AEM’s AI Coding Agents Actually Do
Although the mechanisms behind the decisions that AI makes are often shrouded in obscurity, the way they function is actually well-defined. Adobe’s new AI coding agents are designed to understand an AEM project from the inside out. They learn by pulling from three main inputs: the project’s documentation, the Git repository that defines component logic and structure and the content model stored within AEM. By combining these sources, the agent constructs a working mental model of how the site operates, identifying which components depend on which data and where code changes will have downstream effects.
Where Agent Skills Fit Into AEM’s Development Ecosystem
A key part of the system is Adobe’s idea of “giving agents skills.” Instead of a single, monolithic agent, AEM relies on specialized plug-in skills:
- One for refactoring components
- One for generating boilerplate
- One for diagnosing errors
- One for updating content models
Each skill gives the agent a bounded capability, reducing the risk of unintended changes and making it easier for teams to control what the agent is allowed to do inside the development workflow. Right now, the feature set is a blend of production-ready functions and early prototypes. Some skills, such as code scaffolding and documentation-aware suggestions, work reliably today. Others are still experimental and limited to controlled environments.
"Delivering a unified customer experience requires a much more agile and streamlined operation that solves real customer pain points. Adobe is uniquely positioned to help brands meet this moment, with deep expertise in unifying AI, data and content production workflows to execute the right digital experiences with precision, while uncovering unseen problems," said Amit Ahuja, senior vice president of digital experience business at Adobe.
Why This Matters for AEM Customers and Partners
Adobe’s move into AI-driven coding has real implications for how fast AEM projects can ship. By letting agents generate boilerplate code, update content models or diagnose issues automatically, teams can shrink the backlog of routine development tasks that normally slow down implementation timelines.
AEM Coding Agents: What They Change for Teams
This table summarizes how Adobe’s new agentic capabilities reshape development, delivery and governance across AEM environments.
| Area of Impact | What Changes With AEM Coding Agents | Why It Matters |
|---|---|---|
| Development Workflow | Agents analyze project structure, generate boilerplate, propose refactors and diagnose errors. | Reduces manual coding time and helps teams move faster with fewer context switches. |
| Content Model & Architecture | Skills update content models, map dependencies and surface structural impacts. | Prevents downstream breakage and speeds validation cycles. |
| Migration & Modernization | Agents assist in full-site migrations, component rewrites and legacy clean-up. | Accelerates upgrades and lowers service hours for agencies and enterprise teams. |
| Maintenance & Support | Automates regression fixes, component tweaks and documentation cleanup. | Frees senior developers to focus on architecture and higher-value work. |
| Governance & Safety | Requires role-based permissions, audit logs and strict guardrails. | Keeps automated updates safe in production environments and aligns with enterprise governance. |
| DXP & Industry Direction | Positions AEM as an early adopter of autonomous development tooling. | Raises the larger question of how quickly other platforms will adopt agentic AI. |
The Operational Bottlenecks These Agents Are Designed To Remove
"These agents allow customer experience teams to significantly up-level, by orders of magnitude, what they’re doing. Maybe you’re managing tens of customer journeys right now. You ought to be able to manage hundreds of journeys going forward," said Daniel Sheinberg, senior director of product & strategy at Adobe.
AEM development has always required constant switching between tools, codebases and content structures, which creates a heavy cognitive load. AI agents reduce this overhead by handling the structural groundwork that normally slows down AEM teams.
Nik Kale, principal engineer of CX engineering, cloud security & AI platforms at Cisco, told CMSWire, "Adobe's integration of agentic AI into AEM is more than just a feature update; it's a transformative moment. This technology shifts the most time-consuming tasks, such as structural analysis, boilerplate generation and component wiring, into automated first drafts."
Agencies and systems integrators also stand to benefit. In managed services environments, AI agents offer a way to handle repetitive maintenance work: component tweaks, regression fixes and documentation clean-up, without tying up senior developers. Instead of replacing agency work, these agents free specialists to focus on architecture, governance and higher-value customization. For large AEM partners, this could shift delivery models toward more proactive optimization and less reactive support.
Solving for Classic AEM Holdups
"Adobe’s AI agents will be another unlock for our organization. By shortening the time it takes to identify key decision makers and orchestrate compelling cross-channel journeys, we can boost account engagement and accelerate deal closure," said Brett Rafuse, VP of demand marketing at Cisco
AEM projects also suffer from long pauses caused by dependency checks, version mismatches and waiting for someone to validate a content model or unlock a component. Once an agent understands the project structure, those gaps shrink because it can propose updates immediately and surface the dependencies that would normally take a developer several steps to uncover.
Arthur Balabanskyy, CTO at TapForce and head of engineering at Parsnip, told CMSWire, "Context switching is what notoriously bogs down AEM development…agents reduce this overhead and help developers move quicker via giving an explanation structure and proposing updates that are safe."
Even with these gains, humans remain the control layer. Developers still review code before it enters production. Architects validate that component updates align with the site’s structure. Governance teams set the boundaries for what agents can and cannot change. AI handles the heavy lifting, but oversight ensures that every modification fits the brand’s standards, security requirements and long-term roadmap.
Related Article: Medallia and Adobe Expand Partnership With AI Agent Integration
Architectural and Governance Implications
AI coding agents will sit inside the most sensitive parts of an AEM project: the content architecture, component library, and front-end model that ties everything together. That means their impact is not limited to speeding up development. Anything an agent generates must fit the site’s component structure, naming conventions, accessibility goals, and performance standards. An update that looks correct in isolation can still break something upstream or downstream if it does not respect the architecture.
Why Stronger Guardrails Become Essential In AI-Assisted Builds
The risks are real. Agents can generate inefficient code, introduce regressions, or create security issues if dependency changes are not handled carefully. AEM projects often rely on intricate build pipelines, and even small deviations can ripple across the site. This is why guardrails become essential. Teams will need strict role-based permissions for what an agent can modify, logging for every action taken, and rollback paths when something does not behave as expected. These controls mirror the same governance practices emerging across the broader AI ecosystem, and they apply directly to AEM development.
While other platforms have introduced coding helpers or AI-driven recommendations, few have delivered agents that understand project structures and apply targeted updates inside real AEM builds. This puts Adobe on a path where development work becomes more about oversight, validation and architectural thinking, and less about repetitive code creation.
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The arrival of automated component creation suggests a shift toward AI-assisted software assembly lines, where agents handle repeatable tasks while developers supervise and direct the system’s intent. Kale suggested that "We may see the early formation of AI-augmented software factories where agents take care of tasks such as scaffolding, compliance checks, migrations, and performance linting."
Developer and Consultant Reactions
Early reactions from AEM developers, architects, and consultants suggest that Adobe’s coding agents are already reshaping expectations for site delivery, migration workflows, and long-running development tasks.
What Practitioners Are Seeing In Real Migrations And Refactors
Cedric Huesler, senior director of product for digital experience composition at Adobe, said the shift was already visible in live demos, including a full migration from a third-party CMS to AEM using agent skills. He noted that Adobe has expanded the skill library from six to 14, adding capabilities that automate complex refactors and even full-site migrations. Huesler said these enhancements indicated how quickly long-running generation jobs were evolving, bringing AEM closer to a point where “no manual coding is required for an on-brand web experience.”
Consultants responding to the release focused on the business impact. Several noted that the shift feels as disruptive as the arrival of Edge Delivery Services, with meaningful improvements in time to market, ROI acceleration and reduced implementation costs. Others described the integration of Claude skills into AEM’s workflow as a “real productivity boost” that smooths out some of the traditional friction in component development.
Developers testing the new agents emphasized the importance of understanding the skills behind them. Some advised teams to read the skill files before activating them, describing them as a fast, practical way to understand project structure and support code review. Others reported significant improvements when combining Claude skills with IDEs like Cursor, calling the experience “a huge step forward” for AI-guided development.
Agentic AI Reshapes Digital Experience & Content Platforms
Autonomous systems are transforming how enterprises manage content and customer experiences.
Agentic AI is driving a shift in digital experience platforms (DXPs) and web content management systems, moving beyond static automation toward dynamic, autonomous orchestration. Unlike traditional AI, agentic systems can reason, adapt and act independently, enabling them to manage complex workflows and personalize experiences without constant human oversight.
From Static to Dynamic Orchestration
In DXPs, agentic AI functions as connective infrastructure, orchestrating data, content and customer journeys across channels. These platforms now analyze user behavior, recommend actions and optimize experiences in real time. AI-driven recommendation engines process contextual signals—browsing patterns, purchase history—to deliver personalized messaging in milliseconds, according to industry analysis.
This approach compresses multi-step workflows into minutes, boosting marketing agility and reducing operational costs. Early adopters report faster campaign launches and improved personalization capabilities.
Content Management Evolution
Web content management systems are also evolving. Agentic AI automates content creation, metadata tagging and translation while maintaining compliance and governance standards. Platforms like Kontent.ai are leveraging these capabilities to help marketing teams manage large content portfolios and streamline localization.
By treating content as structured data, agentic AI enables more efficient search, retrieval and reuse. This approach aims to reduce duplication and outdated information across enterprise content systems, according to industry observers.
Business Impact of AEM Coding Agents
The technology is turning digital experience and content management platforms into adaptive ecosystems. Organizations implementing these systems report significant efficiency gains as agentic AI becomes the command layer unifying marketing, content and customer experience operations.
