Introduction
By the end of 2025, global data volumes will exceed 175 zettabytes. For today’s business leaders, this isn’t just an IT concern but a defining business imperative.
The ability to transform data into strategic insight is now a key differentiator between market leaders and those left behind. To compete and win, executives must champion modern, scalable, and cost-effective data strategies that fuel innovation, enhance agility, and drive measurable outcomes across the enterprise.
As the AI-driven analytics market is expected to exceed $190 billion by 2028, intelligent agents will fundamentally reshape the infrastructure of modern data ecosystems. Whether it's automating and refining data operations, ingesting and extracting large data sets, or overhauling transformation and loading (ETL), these technologies will prove to be critical in the development of scalable data products and supporting intricate workloads across hybrid and multi-cloud infrastructures, all while dramatically shortening the path from raw data to actionable insights.
For data architects and engineers, this evolution demands a strategic shift in how platforms are designed and operated. The focus has extended beyond data management towards engineering intelligent and adaptive systems capable of scaling in sync with rapidly evolving data demands.
We'll examine this transformation through the lens of emerging roles like AI Data Engineer, AI DataOps Engineer, AI Performance Engineer, and AI FinOps Engineer, with a focus on the principles of efficiency and cost-effectiveness.
AI Agents: The new grownup “kid” on the block
AI agents are intelligent systems designed to autonomously perform tasks by leveraging AI techniques such as Machine Learning (ML), Natural Language Processing (NLP), Generative AI, and Knowledge Reasoning Algorithms. These agents can perceive their environment, process information, and act accordingly to achieve predefined goals without needing constant human intervention.
Types of AI Agents
AI Agents, sometimes referred to as LLM Agents (which in our opinion is very narrow in its use case), come in several types.
- Reflex Agents: These are the simplest types that employ selecting actions based solely on the current perception of the environment without considering past history.
- Goal-Based Agents: These agents use a model of the world and have explicit goals they try to achieve, planning their actions to reach those goals.
- Learning Agents: These agents can learn from their experiences, adapting their behavior over time to improve performance.
- Utility-Based Agents: These agents act to maximize a utility function, which measures the desirability of different states of the environment, enabling them to make more nuanced decisions.
- Hierarchical Agents: These agents are organized into a hierarchy, with higher-level agents managing lower-level ones, enabling them to handle complex tasks.
- Collaborative Agents: These agents work together with other agents (which could be other AI agents or humans) to achieve common goals, requiring communication and coordination.
Gartner Research views AI agents as “Autonomous or semiautonomous software entities that use AI techniques to perceive, make decisions, take actions and achieve goals in their digital or physical environments.”.
According to similar research by Gartner, these AI Agents come in four types:
- Prebuilt agents: Productivity, task-specific
- No-code agent builders: Tools for builders
- Agent development platforms: Development platform
- Agent training platforms: Simulation platforms
AI Agents are distinguished by few key characteristics, mainly:
- Autonomy: Can the agent operate independently, reducing the need for constant human intervention?
- Adaptability: Can the agent modify its behavior in response to changes in its environment?
- Goal-Directedness: Are the agent's actions directed towards achieving specific objectives?
- Reasoning: Can the agent process information and make inferences to inform its actions?
Within data management and analytics, AI agents will likely be domain-specific to operate within the data ecosystem, driving a shift towards more intelligent and automated processes. The type of agent employed will depend on the specific data task. For instance, a reflex agent might be used for simple data cleaning, while a collaborative agent system is used for complex data integrations across multiple sources.
The Evolving Landscape of Data Management & Analytics
The medallion architecture popularised by Databricks (starting in 2020) may seem like a recent approach to data design but, in reality, is actually an evolution spanning 50 years. Mass migration to cloud-only architectures has slowed a bit in the past few years due to unrealized ROI and economic missteps. Compounding this transformation challenge is the installed base of 2,000+ commercial, homegrown legacy tools, platforms, and capabilities.
A typical data ecosystem will have the following capabilities:
- Data Acquisition and Integration: Collecting and combining data from diverse sources is crucial. AI agents, particularly collaborative agents, can automate tasks like schema matching and data transformation, reducing manual effort and potential errors.
- Data Storage and Management: Optimizing data storage and retrieval is essential for cost-effectiveness. AI agents, especially utility-based agents, can help with data tiering and lifecycle management.
- Data Quality and Governance: Maintaining data quality is paramount. AI agents, including learning agents, can automate data quality checks and anomaly detection, ensuring data trustworthiness.
- Data Transformation and Processing: Transforming data for analysis can be resource-intensive. AI agents, such as hierarchical agents, can optimize transformation processes.
- Data Analysis and Interpretation: Deriving insights from data is the ultimate goal. AI agents, including goal-based agents, can augment human analysts by automating tasks like feature selection and model optimization.
- Data Visualization and Reporting: Presenting data insights is essential for effective communication. AI agents can assist in generating visualizations and reports.
- Data Security and Privacy: Protecting sensitive data has become hypercritical. AI agents can play a role in automating security and compliance tasks.
- Data Spend and Optimization: Managing the process of analyzing and controlling data infrastructure costs to maximize efficiency, performance, and ROI across cloud and analytics environments.
According to Gartner Research, the rate of adoption of AI Agents in Data Management is quite low, hovering somewhere in the single-digit range currently.
However, we are seeing a quick change and expect it to play a critical role in many aspects of Data Management and Analytics.
Current Challenges
The challenges experienced by data practitioners continue to persist with the ongoing surge in cloud costs, rapid demand for DataOps, and the growth in data for AI use cases continue to exacerbate them.
- Cost Optimization: Managing the escalating cost of data storage, data processing, and computing.
- Data Complexity and Heterogeneity: Integrating diverse data sources and formats.
- Data Silos and Fragmentation: Overcoming data silos to achieve a unified view.
- Real-Time Data Processing: Processing and analyzing streaming data for timely insights.
- Scalability and Performance: Ensuring data systems can handle growing data volumes.
- Lack of Qualified Resources: Teams continue to be understaffed, and upskilling teams to leverage modern innovations is becoming increasingly impossible.
It is clear that AI Agents or equivalent will play a critical role in addressing and solving many of these challenges over time.
AI Agents: Transforming Data Management & Analytics
With the belief that AI agents will help transform data management and analytics by addressing many of the challenges by capitalizing on emerging trends, it is important to note that there is a paradigm shift underway.
This transformation drives the need for specialized skills and roles within data teams whose roles will involve significant changes over time.
- From Reactive to Proactive: AI agents, especially predictive learning agents, will enable systems to anticipate and address issues before they arise.
- From Manual to Automated: Repetitive and time-consuming tasks will be automated by reflex agents and hierarchical agents.
- From Siloed to Integrated: Collaborative agents can facilitate seamless data integration and interoperability.
- From Human-Driven to Agent-Augmented: AI agents will augment human capabilities, allowing data professionals to focus on higher-level strategic initiatives.
For greenfield initiatives with no installed legacy data systems, there is absolutely no reason to go backward and get stuck in the past!
For legacy projects, AI Agents can assist in targeted ways as well. A few key transformations that AI Agents can lead now include:
- Automation: AI agents will automate data integration, quality management, and other routine tasks, freeing up human experts. This is a core focus for AI Data Engineers, who will design and deploy a variety of agent types.
- Cost Optimization: By optimizing resource utilization and automating tasks, AI agents will drive significant cost savings. This is a key concern for AI FinOps Engineers, who will leverage utility-based agents to make cost-effective decisions.
- Enhanced Efficiency: AI agents will streamline data workflows, improving processing speed and reducing latency. AI DataOps Engineers will leverage this capability, often using hierarchical agents for complex workflow management.
- Improved Accuracy: Automated data quality checks and anomaly detection will enhance data accuracy and reliability. AI Data Engineers are crucial in implementing these systems, often using learning agents.
- Deeper Insights: AI-powered analysis and visualization will enable organizations to extract more meaningful insights from their data. AI Performance Engineers will ensure these systems operate optimally.
The Rise of the AI-Driven Data Team
The increasing adoption of AI agents or similar capabilities in data management and analytics is leading to the emergence of new roles and specializations within data teams. These roles are focused on leveraging AI to accelerate the building of data products, build efficient pipelines, optimize data operations, improve efficiency, and drive cost savings.
Instead of getting stuck in minutiae about AI Agents, Agentic AI, etc., why not transform your D&A initiatives by augmenting your team with specialized skills that use AI with agentic capabilities?
Newly emerged roles (shortlist) for Data Management now include:
- AI Data Engineer: Focuses on building and maintaining AI-powered infrastructures and systems that enable intelligent automation of data management tasks. This includes designing and implementing various types of AI agents (reflex, goal-based, learning, etc.) for data integration, data quality, and data processing tasks.
- AI DataOps Engineer: Specializes in automating and streamlining the data lifecycle using AI agents. This role is responsible for monitoring performance and ensuring efficient data delivery, often using hierarchical and collaborative agents for complex orchestration.
- AI Performance Engineer: Concentrates on optimizing the performance of data ecosystems. This involves using AI agents to identify performance bottlenecks, tune system parameters, and ensure that data processing and analysis are carried out efficiently.
- AI FinOps Engineer: Focuses on the financial aspects of managing all data tools, platforms, and services. This role uses AI agents, particularly utility-based agents, to monitor data spending, identify cost-saving opportunities, and optimize resource allocation to maximize ROI.
Example Implementations
The transformative potential of AI agents and their impact on the roles of AI-driven data teams are illustrated by these examples:
1. AI DataOps Engineer: Pipeline Orchestration
AI DataOps Engineer manages complex data pipelines, dynamically adjusting parameters and scaling resources based on design and real-time metrics.
2. AI Performance Engineer: Intelligent Query Management
AI Performance Engineer uses reinforcement learning to optimize query execution plans, rewriting queries to minimize resource consumption to eliminate systems downtime.
3. AI Data Engineer: Automated Data Quality
AI Data Engineer with learning capabilities detects data anomalies, identifies quick fixes, and predicts potential data quality issues, enabling proactive intervention and reducing the need for costly remediation.
4. AI FinOps Engineer: Automated Cloud Cost Management
AI FinOps Engineer monitors cloud spending, identifies cost-saving opportunities, and provides recommendations for optimizing cloud resource utilization, ensuring maximum ROI on data investments.
Roadmap: Adopting AI Agents in Data Management & Analytics
Successfully adopting AI agents or equivalent requires a structured approach that balances technical implementation with organizational readiness.
Step 1: Assess Current State: Evaluate your organization's capabilities
Step 2: Define Objectives: Set clear and bold goals
Step 3: Build Foundational Infrastructure: Prepare for AI agents and equivalent
Step 4: Choose the Right Tools: Select AI agent frameworks suited to your needs:
Step 5: Pilot Projects: Start with small-scale yet transformative use cases
Step 6: Scale Operations: Expand the scope of AI agents across departments
Step 7: Upskill Teams: Train employees now
Conclusion
Even though AI agents are relatively new, they are helping by automating key capabilities for data management and analytics now and will eventually transform it in the future. The popular medallion architecture will evolve in the near future as well!
The technology landscape is rapidly evolving, with commercial solutions offering agentic capabilities that support these emerging roles. Revefi, for example, is offering skilled agentic AI solutions for various data management tasks. These skills, available as augmentation, viz., AI Data Engineers, AI DataOps Engineers, AI Performance Engineers, and AI FinOps Engineers, work with your data team and ecosystems to provide superior ROI.
These roles, supported by AI-driven platforms and tools, will be instrumental in helping organizations thrive in the data-driven future. As AI technology advances every day, the transformative impact of AI agents will only intensify, ultimately reshaping the way we work with data and unlocking new possibilities for innovation and growth.
