How to Build Agentic Data Engineering Workflows
Discover how agentic data engineering transforms manual workflows into AI-powered solutions for efficiency and innovation.
The world of data engineering is on the cusp of a revolutionary shift. Imagine a reality where the repetitive and often time-consuming tasks that bog down data teams are seamlessly handled by AI-powered agents, freeing engineers to focus on innovation and impactful projects. Enter agentic data engineering workflows - a transformative approach that integrates AI agents into every layer of the data lifecycle, enabling teams to optimize their workflows, enhance productivity, and reclaim their time.
In this article, we explore the concept of agentic workflows, why they're critical for scaling modern data engineering, and how they operate in practice. You’ll learn how these workflows are reducing toil, enhancing collaboration, and providing a glimpse into the future of AI-driven automation.
What Are Agentic Workflows?
At its core, an agentic workflow refers to a data engineering approach where AI agents - specialized tools powered by large language models (LLMs) - are embedded within the data lifecycle to autonomously perform tasks, solve problems, and optimize processes. These agents are not just simple chatbots; they are intelligent, context-aware operators capable of understanding metadata, executing complex workflows, and collaborating with human engineers.
According to one of the presenters, Sean, the founder of Ascend, these agents allow data teams to move beyond manual integration work and focus on higher-value tasks by automating the "toil" that often dominates their day-to-day activities.
Why Does Data Engineering Need Agentic Workflows?
The Current State of Data Engineering
Data engineering teams are increasingly overburdened. A survey conducted by Ascend revealed that 95% of data teams report being at or above capacity, leaving little room for innovation or the adoption of advanced AI technologies. While other fields - like software engineering and marketing - are leveraging AI to accelerate their workflows, data engineering teams remain stuck in manual processes, such as configuring pipelines, debugging code, and maintaining integrations.
This lag is not due to a lack of technology but rather the overwhelming number of tools and platforms in the data ecosystem. Engineers spend more time connecting disparate systems than creating meaningful data products, hindering their ability to innovate.
Bridging the Gap with AI
Agentic workflows aim to solve this by embedding AI agents into the entire data operations lifecycle, allowing teams to:
- Optimize pipelines for performance and cost.
- Migrate platforms effortlessly.
- Monitor systems for errors and automatically resolve issues.
- Collaborate with agents to debug and improve code.
As Sean aptly puts it, "AI can truly participate in the entire data lifecycle - from development to deployment, observation, and feedback."
How Agentic Workflows Operate: A Technical Deep Dive
The Anatomy of an AI Agent
Cody, the product manager at Ascend, defines an AI agent as more than just an LLM call in a loop - it is a context-aware actor that operates autonomously within an environment. These agents are designed to:
- Understand their environment: They are trained to interpret metadata, user actions, and lineage data.
- Execute actions: Agents can perform tasks like writing SQL queries, identifying pipeline errors, or restructuring code.
- Iterate and learn: Through feedback loops, agents refine their approaches over time, improving their performance.
Key Components of Agentic Systems
- Unified Metadata: AI agents rely on metadata to understand the context of their tasks. This includes data lineage, transformation logic, and infrastructure information.
- Automation Systems: Triggering mechanisms allow agents to interact with external tools, systems, or collaborators, enabling seamless integration.
- Feedback Loops: By incorporating iterative feedback, agents can refine their outputs, ensuring continuous improvement.
- Guardrails and Access Controls: To ensure security and reliability, agents are restricted to specific roles and permissions, similar to service accounts in traditional engineering systems.
Real-World Applications of Agentic Workflows
1. Automated Debugging and Issue Resolution One of the most compelling use cases demonstrated during the presentation was an agent’s ability to identify and resolve pipeline errors. When alerted to a failing data flow in Slack, the agent not only diagnosed the issue but also suggested actionable solutions. For instance, it recognized that tests expecting a single row of data were incompatible with a pipeline producing thousands of rows and automatically updated the tests to align with the actual data.
2. Migrating from Python to SQL Agents can even tackle complex migrations. In the demo, the AI agent converted an entire Python-based pipeline into SQL to accommodate a SQL-centric analytics team. The agent drafted new SQL components while systematically replacing the existing Python files, allowing the team to maintain compatibility with their preferred tools.
3. Enhanced Collaboration Agents enhance collaboration by automating documentation, generating detailed commit messages, and updating version control systems like Git. This ensures that teams maintain best practices without additional effort.
4. Proactive Monitoring Proactive monitoring is another significant advantage. Agents can oversee pipeline performance, anticipate potential bottlenecks, and alert engineers before issues escalate, reducing downtime and enhancing reliability.
Challenges in Implementing Agentic Workflows
While the benefits are clear, implementing agentic workflows does require careful planning. Key challenges include:
- Non-Determinism: LLMs are inherently non-deterministic, meaning their outputs can vary. This necessitates robust testing frameworks and feedback loops.
- Data Quality: Garbage in, garbage out. Agents need access to clean, well-documented data to perform effectively.
- Complexity Management: As agents interact with increasingly complex environments, maintaining clarity and control over their actions becomes paramount.
- Security and Governance: Strict permissions and access controls are critical to prevent accidental or malicious actions, such as unauthorized SQL queries in production.
Key Takeaways
- AI Agents Are Transforming Data Engineering: Agentic workflows are reducing toil and enabling data teams to focus on innovation and strategic tasks.
- Context is King: Agents thrive when they have access to comprehensive metadata and are embedded within robust systems.
- Automation is Key: From debugging to migrations, agents can handle a wide range of repetitive tasks, accelerating productivity.
- Guardrails Are Essential: Permissions, roles, and feedback loops ensure agents operate securely and effectively.
- Iterative Learning Drives Success: Feedback loops allow agents to refine their outputs, improving their performance over time.
- Real-World Benefits Include:
- Proactive error resolution and debugging.
- Streamlined migrations between technologies.
- Enhanced collaboration through automated documentation and commit tracking.
- Optimized pipeline performance and resource usage.
The Future of Data Engineering: Embrace the Agentic Revolution
Agentic workflows are not just a trend - they are a fundamental shift in how data engineering will be executed in the years to come. By integrating AI agents into every layer of the data lifecycle, teams can achieve unparalleled efficiency and innovation. As AI models continue to improve, the opportunities for agent-driven automation will only expand, making now the perfect time to adopt and experiment with these transformative tools.
The journey toward agentic data engineering is just beginning, but the potential is limitless. By embracing this new paradigm, data teams can position themselves at the forefront of AI-driven innovation, powering the future of their organizations with smarter, faster, and more efficient workflows.
Source: "Agentic Data Engineering: From Manual to AI Powered Workflows" - Ascend, YouTube, Aug 25, 2025 - https://www.youtube.com/watch?v=rV-JadTsvvs