Delivering real-time, quality customer experiences hinges on an organisation’s ability to ingest data in a fast, efficient manner. Businesses also need to ensure that data is clean, connected and high-quality. However, the work required to deliver customer experiences often remains deeply technical, highly manual and spread across multiple tools — and data engineers and architects are feeling the pressure to keep up.
Many spend countless hours designing scalable data models, building ingestion pipelines, writing transformation logic, enforcing governance and managing dataset hygiene. These workflows are essential, but they also create bottlenecks that slow down the teams relying on timely data to execute high‑impact, high-volume campaigns.
As expectations rise and timelines shrink, technical teams need intelligent systems that can shoulder more of this operational load so they can focus on the strategic initiatives that drive value across the business. That’s where Adobe Data Engineering Agent comes in.
What is Data Engineering Agent?
Powered by Adobe Experience Platform Agent Orchestrator, Data Engineering Agent will soon streamline data life cycles for engineers, architects and data consumers, such as marketing operations specialists. The agent will be able to perform a variety of tasks including data onboarding, SQL preparation, data collection and troubleshooting in Adobe Experience Platform applications, making workflows faster, improving data quality and minimising the need for specialised skills. Data Engineering Agent will also reduce operational tasks, allowing technical experts to focus on higher-value architectural and governance work that advances organisational goals.
Automated onboarding of complex datasets end-to-end.
Traditionally, onboarding datasets such as ecommerce transactions, loyalty activity or customer profiles is slow and error-prone. Data engineers must wrangle messy data source files, reconcile inconsistent naming conventions, handle missing values and build complex transformations to align everything to the required Experience Data Model for Adobe applications. Further data onboarding is manual and fragmented, often taking weeks and sometimes months, creating downstream delays that make it difficult for marketers and analysts to access the data required for real-time activation.
Data Engineering Agent will reduce this complexity, reducing data preparation from weeks to days. Teams can soon onboard new data into Adobe Experience Platform quickly and confidently by guiding the agent through data analysis, schema modelling, validation and ingestion via a safe, AI-guided, human-in-the-loop workflow.
What data onboarding processes can Data Engineering Agent automate?
The agent will soon be able to automate the onboarding of complex datasets end-to-end using guided conversations. It will handle critical components of the workflow, dramatically simplifying processes while ensuring data aligns to Adobe’s standards — shortening time-to-activation and improving data quality by:
- Selecting files and entities to onboard data from a variety of sources such as Amazon S3, Data Landing Zone and Marketo among others.
- Validating data quality and approving AI recommended fields for schema creation.
- Configuring semantic enrichment.
- Creating schemas, validating schema models and correcting mapping errors through AI automations before publishing schemas.
- Creating dataflows using AI Assistant for data ingestion into Adobe Experience Platform.
Faster onboarding means fresher customer data flowing into downstream systems — directly powering real-time personalisation, offer optimisation and retention programmes. By removing manual effort and reducing technical friction, Data Engineering Agent will not only shorten time-to-activation but also improve data quality and reliability. This means marketing and analyst teams will be able to operate off of the most updated information possible to drive real business outcomes, including measurable lift in campaign performance, better customer experiences, stronger ROI and increased customer lifetime value.
Simplified SQL data prep with natural language prompts.
Data teams that span engineering, marketing, production functions and more are responsible for preparing and managing massive volumes of data within Adobe Experience Platform. SQL-based data prep sits at the heart of these workflows, but performing SQL queries can be time‑consuming, highly technical and dependant on platform experts. This slows activation and creates friction between technical and business teams that need clean, reliable data to operate effectively.
Using natural language prompts through the AI Assistant conversational interface powered by Adobe Experience Platform Agent Orchestrator, data engineers and other users will be able to leverage Data Engineering Agent to automate SQL work, including:
- Creating optimised, schema-aware SQL statements, providing previews to users before execution.
- Monitoring and troubleshooting SQL jobs without navigating the queries UI or switching tools.
- Validating dataset readiness and quality before running SQL jobs.
- Surfacing issues early and guiding users through automatic remediation.
This will help to ensure only clean, reliable data flows into downstream Adobe applications such as Adobe Real-Time Customer Data Platform (CDP), Adobe Customer Journey Analytics and Adobe Journey Optimizer.
By automating SQL authoring, streamlining repetitive tasks and proactively catching errors, Data Engineering Agent will broaden access to SQL capabilities across non-technical teams. This access accelerates the entire data prep lifecycle and shrinks the time to insight across the organisation. Teams move faster, collaborate more effectively and unlock real‑time use cases without being slowed by technical complexity.
Streamlined data collection and troubleshooting.
Implementation engineers and web developers often spend extensive time piecing together how data collection components fit together for complex use cases. They jump between documentation, community posts and internal wikis and often schedule multiple meetings with cross-functional teams to validate requirements and align configuration details. When issues arise, troubleshooting is equally painful — users must manually trace data lineage across every object in the chain, reviewing configurations one by one to uncover misalignments or broken dependencies. This entire process is slow, fragmented, highly dependant on expert availability and prone to miscommunication — leading to prolonged implementation timelines and delayed issue resolution.
Data Engineering Agent will soon be able to provide in‑context product knowledge grounded in Adobe Experience League, community forums and public GitHub documentation to explain how data collection components work together for any use case. This conversational guidance will be provided to users as they configure data collection objects and offers operational insights that visualise lineage, dependencies and relationships. With contextual semantic understanding of object relationships, the agent will help users quickly identify misconfigurations, detect unused assets and trace root causes of data issues without manually inspecting each component.
The outcome is faster implementation timelines, cleaner data collection set-up and greater confidence in the accuracy and reliability of data powering downstream applications — enabling smoother activations and more resilient customer experiences.
Reimagine data engineering with conversational, agentic intelligence.
Data is the backbone of every personalised experience and with the rise of the agentic web, organisations can no longer afford slow, manual and fragmented data engineering workflows.
Soon, Data Engineering Agent will bring a new operating model to Adobe Experience Platform and applications — one where complex tasks become conversational, data readiness becomes proactive and engineering teams are empowered to deliver impact at a dramatically accelerated pace. By streamlining onboarding, simplifying SQL data preparation and ensuring quality at every step, the agent helps teams move from reactive data management to strategic data innovation.
With Data Engineering Agent, businesses will be able to unlock faster activation, higher data integrity and stronger cross‑functional collaboration, ultimately driving better decisions, better experiences and better outcomes across the entire customer journey.
Discover how Adobe is shaping the future of AI for enterprise by powering the future of marketing and creativity with agentic AI.
Huong Vu is a senior product marketing manager for Adobe Experience Platform, focusing on leading go-to-market strategies for innovative Experience Platform and AI capabilities and driving awareness and adoption around Adobe’s Customer Experience Orchestration offerings. Vu brings over five years of experience in product and brand marketing. She joined Adobe in 2024 after obtaining her MBA from Kellogg School of Management at Northwestern University.
