AI Agents for Data Teams
Data is the driving force of the world’s modern economies, but data teams are struggling to meet demand to support generative AI (GenAI), including rapid data volume growth and the increasing complexity of data pipelines. More than 88% of software engineers, data scientists, and SQL analysts surveyed say they are turning to AI for more effective bug-fixing and troubleshooting. And 84% of engineers who use AI said it frees up their time to focus on high-value activities.
AI Agents represent the next wave of AI innovation and have arrived just in time to help data teams make more efficient use of their limited bandwidth to build, operate, and optimize data pipelines and GenAI applications on modern data platforms.
Data Teams Grapple with High Demand for GenAI
A surge in adoption of new technologies such as GenAI is putting tremendous pressure on data teams, leading to broken apps and burnout. In order to support new GenAI products, data teams must deliver more production data pipelines and data apps, faster. The result is that data teams have too much on their plates, the pipelines are too complex, there is not enough time, and not everyone has the deep tech skills required. No surprise that 70% of organizations have difficulty integrating data into AI models and only 48% of AI projects get deployed into production.
Understanding AI Agents
Defining AI Agents
AI agents are software-based systems that gather information, provide recommended actions, initiate and complete tasks in collaboration with or on behalf of humans to achieve a goal. AI agents can act independently, utilizing components like perception and reasoning, provide step-by-step guidance to augment human abilities, or can provide supporting information to support complex human-led tasks. AI agents play a crucial role in automating tasks and simplifying data-driven decision-making, and achieving greater productivity and efficiency.
How AI Agents Work
AI agents operate by leveraging a wide range of data sources and signals, using algorithms and data processing to identify anomalies and actions, then interact with their environment and users to effectively achieve specific goals. AI agents can achieve >90% accuracy, primarily driven by the reliability, volume, and variety of input data and telemetry to which they have access.
Types of Intelligent Agents
- Reactive and proactive agents are two primary categories of intelligent agents.
- Some agents perform work for you, while others help complete tasks with you or provide information to support your work.
- Each type of intelligent agent has distinct characteristics and applications tailored to specific functions, enhancing productivity and efficiency.
AI for Data Driven Organizations
Enhancing Decision Making
AI agents empower teams by improving data support decision-making processes for you, with you, or by you. Examples of how AI agents act on your behalf include reducing toil and handling routine decisions based on AI insights. In various industries, AI agents optimize decision-making and provide recommendations to support your decisions. For complex tasks, AI agents provide supporting information needed to build data pipelines, write SQL queries, and partition data.
Benefits of broader telemetry sources for AI agents
Integrating telemetry from various platforms and systems enhances AI agents’ ability to provide accurate recommendations. Incorporating AI agents into root cause analysis (RCA) systems offers significant benefits. Meta’s AI-based root cause analysis system shows how AI agents enhance tools and applications.
Overcoming Challenges
Enterprises running modern data stacks face common challenges like high costs, slow performance, and impaired productivity. Leveraging AI agents can automate tasks for you, with you, and by you. Unravel customers such as Equifax, Maersk, and Novartis have successfully overcome these challenges using AI.
The Value of AI Agents for Data Teams
Reducing Costs
When implementing AI agents, businesses benefit from optimized data stacks, reducing operational costs significantly. These agents continuously analyze telemetry data, adapting to new information dynamically. Unravel customers have successfully leveraged AI to achieve operational efficiency and cost savings.
Accelerating Performance
Performance is crucial in data analytics, and AI agents play a vital role in enhancing it. By utilizing these agents, enterprise organizations can make well-informed decisions promptly. Unravel customers have experienced accelerated data analytics performance through the implementation of AI technologies.
Improving Productivity
AI agents are instrumental in streamlining processes within businesses, leading to increased productivity levels. By integrating these agents into workflows, companies witness substantial productivity gains. Automation of repetitive tasks by AI agents simplify troubleshooting to boost overall productivity and efficiency.
Future Trends in AI Agents for FinOps, DataOps, and Data Engineering
Faster Innovation with AI Agents
By 2026 conversational AI will reduce agent labor costs by $80 billion. AI agents are advancing, providing accurate recommendations to address more issues automatically. This allows your team to focus on innovation. For example, companies like Meta use AI agents to simplify root cause analysis (RCA) for complex applications.
Accelerated Data Pipelines with AI Agents
Data processing is shifting towards real-time analytics, enabling faster revenue growth. However, this places higher demands on data teams. Equifax leverages AI to serve over 12 million daily requests in near real time.
Improved Data Analytics Efficiency with AI Agents
Data management is the fastest-growing segment of cloud spending. In the cloud, time is money; faster data processing reduces costs. One of the word’s largest logistics companies improved efficiency by up to 70% in just 6 months using Unravel’s AI recommendations.
Empower Your Team with AI Agents
Harnessing the power of AI agents can revolutionize your business operations, enhancing efficiency, decision-making, and customer experiences. Embrace this technology to stay ahead in the competitive landscape and unlock new opportunities for growth and innovation.
Learn more about our FinOps Agent, DataOps Agent, and Data Engineering Agent.