2025: Agentic Workflows will transform Data Engineering

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As we approach 2025, the landscape of enterprise data management and analytics is on the cusp of a significant transformation driven by the advent of AI agents. Industry leaders such as Mustafa Suleyman (Microsoft AI), Eric Schmidt (ex Google CEO), and major tech companies like IBM are heralding AI agents as the next frontier in the deployment of AI. Moreover, Gartner's top technology trends for 2025 predicts that agentic workflows are set to have a significant impact on business processes, decision-making and improving operational efficiency.

The coming year will see AI agents deployed to automate business processes and enhance decision-making across most enterprises and industries - with Gartner also forecasting that by 2028 at least 15% of day-to-day work decisions will be made autonomously through agentic AI - up from 0% in 2024.

📥 Understanding AI Agents and Agentic Workflows

Unlike traditional AI systems, these agents are designed to operate with a high degree of autonomy, making decisions, learning from interactions - aka Recursive Self-Improvement, and executing complex tasks with minimal human intervention. Agentic workflows leverage these AI agents to perform intricate processes autonomously. This advancement has the potential to dramatically change the nature of business operations.

🧐 Key Characteristics of AI Agents:

1. Autonomy: Ability to operate independently and make decisions.

2. Learning and Adaptation: Continuous improvement through experience.

3. Goal-oriented: Focused on achieving specific objectives.

4. Multi-step Task Execution: Capability to handle complex, multi-stage processes.

5. 24/7 Operation: Unlike human employees, AI agents can work continuously, potentially tripling productivity in certain areas.

📋 The Current State of AI Agent Development

As of late 2024, AI agent technology is rapidly evolving. Sam Altman, CEO of OpenAI, forecasts that agentic AI will be integrated into our daily lives by 2025. The demo above of an AI Agent ordering 400 chocolate dipped strawberries via voice conversation, may be a little whimsical, but we are seeing voice agents making their debut in commercial settings such as debt collection, surveys and performing tasks that is the purview of a personal assistant. Some significant developments in the field include:

• OpenAI's work on the "o1" model, which Altman describes as a "Level 2" AI capable of advanced reasoning.

• Microsoft's introduction of autonomous agents in Copilot Studio, enabling background task execution and event-driven actions.

• The emergence of AI agents capable of learning from processes, adapting to new conditions, and making decisions without constant human input.

◎ Impact on Operational Efficiency

AI agents are expected to significantly enhance operational efficiency across various aspects of large enterprises, examples include:

1. Automation of Complex Tasks

AI agents will revolutionise task automation by handling multi-step processes autonomously. For data professionals, this likely means:

• Reduced time spent on routine data management tasks

• More focus on strategic data initiatives and innovation

• Need for skills in designing and overseeing automated data workflows

2. Enhanced Decision-Making

AI agents will dramatically improve the speed and quality of data-driven decisions, with rapid analysis of vast amounts of data. Application in predictive analytics to forecast trends and identify potential issues, enabling proactive problem-solving, to minimise downtime and optimise resource allocation. The of the implications for data professionals:

• Shift towards designing decision support systems leveraging AI agents

• Need for expertise in interpreting and validating AI-generated insights

• Focus on ensuring data quality and relevance for AI-driven decision-making

3. Streamlined Data Management Processes

AI agents can be deployed to automate data cleansing, integration, and transformation. There will be opportunities to use them for intelligent data cataloging and metadata management, as well as continuous data quality monitoring and improvement. We would recommend:

• Developing strategies for AI-driven data governance

• Implementing AI-assisted data lineage and impact analysis

• Understand your compliance requirements in AI-managed environments

4. Enhanced Data Analysis and Insights

AI agents will have the potential to excel at extracting actionable insights from large datasets especially in real-time data analysis, identifying inefficiencies in data workflows and being deployed for advanced pattern recognition as well as anomaly detection. Unsurprisingly, Data Engineers will need to focus on:

• Designing data architectures that support AI agent operations

• Developing skills in advanced analytics and machine learning

• Ensuring data accessibility and interoperability for AI agents

5. Intelligent Data Integration

AI agents will facilitate seamless data integration across various systems, for instance automating the mapping and transformation of data between different formats, along with real-time data synchronisation across disparate platforms, and the intelligent resolution of data conflicts and inconsistencies. Some professional considerations for 2025 include:

• Implementing AI-driven data integration strategies

• Developing expertise in API management and microservices architecture

• Ensuring data security and privacy in integrated environments

🎼 Key Considerations

Overall as AI agents and agentic workflows become more prevalent in your workflows, data professionals will be paying more attention to:

1. Data Quality and Trustworthiness

AI agents rely heavily on high-quality, trustworthy data to function effectively. Making rigorous data quality management processes a necessity, along with the implementation of data validation and verification mechanisms, coupled with continuous monitoring of data integrity and accuracy.

2. Ethical AI and Data Governance

As AI agents gain more autonomy, ethical considerations become paramount with the development of frameworks for ethical AI decision-making that ensure transparency and explainability in AI agent operations, and the implementation of robust data governance policies aligned with AI agent usage.

4. Security and Privacy

As AI agents handle sensitive data, security and privacy concerns will be an issue for Data Engineers in 2025. There will be a need to implement advanced data encryption and access control mechanisms, making sure you are in compliance with data protection regulations (e.g., GDPR, CCPA). Another consideration will be developing strategies for secure AI agent-to-agent communication.

5. Agentic Continuous Learning and Adaptation

As AI agents require ongoing refinement and adaptation, you will need to consider mechanisms for continuous learning and model updating, along with strategies for handling concept drift and data distribution changes. Another aspect is enabling AI agents to adapt to evolving business requirements and data landscapes.

6. Integration with Existing Systems

Seamless integration of AI agents with existing data ecosystems will be able to consideration. Developing APIs and interfaces for AI agent interaction with data systems, that ensure compatibility with legacy data storage and processing systems. You will likely be looking at implementing data transformation layers for AI agent consumption.

7. Monitoring and Observability

Effective oversight of AI agent operations will become part of the new normal where you have monitoring systems for AI agent activities, with dashboards for real-time visibility into AI agent performance and alerting mechanisms for anomalies or unexpected behaviours.

✅ Five Ways to “Be Prepared”

As the Scouting motto goes, preparation is key. As 2025 rolls around we recommend Data Professionals prepare themselves with some of these initiatives:

1. Skill Development: Invest in understanding AI models, machine learning, and advanced analytics to effectively work with and manage AI agents.

2. Data Infrastructure Upgrade: Assess and upgrade data infrastructure to support the high-performance requirements of AI agents.

3. Data Strategy Alignment: Align data strategies with your enterprise’s AI agent implementation plans, ensuring data readiness for AI consumption.

4. Ethical Framework Development: Establish clear guidelines and frameworks for the ethical use of AI agents within the data ecosystem.

5. Collaboration Enhancement: Foster collaboration between data teams and AI specialists to ensure seamless integration of AI agents into data workflows.

The integration of AI agents into large enterprise data and analytics operations in 2025 promises to be a transformative force, offering unprecedented opportunities for efficiency, innovation, and insights. While challenges exist, the potential benefits are substantial. Data professionals who proactively embrace and adapt to this technology will play a crucial role in harnessing its full potential.

As we move towards this AI-driven future, it's essential to stay informed, plan strategically, and remain agile, least you get left behind. The next 3 years will likely see a rapid evolution in this field, and those who are prepared will be best positioned to lead their organisations through this transformative time.

If there was one takeaway that we are hearing over and over, in relation to the advent of Generative AI and Data Engineering that is the importance of Data Quality. Coming up next week, a deep dive into Recursive Self-Improvement and the impact it will have in improving Data Quality.

That’s a wrap for this week.

Last of all check out the recently launched Enterprise AI Engineering Classroom and Resource Hub. You will find materials to get you started using IBM Watsonx in an enterprise environment.

Thank you