AI Agents

What are AI Agents?

AI agents are intelligent, autonomous systems capable of reasoning, taking independent actions, and adapting dynamically to changing conditions. Unlike traditional robotic process automation (RPA), which relies on rigid, deterministic rules to automate repetitive enterprise tasks, AI agents utilize probabilistic reasoning to handle ambiguity and uncertainty. 

This probabilistic approach, seen in advanced tools like ChatGPT, enables AI agents to provide varied outputs based on context, likelihood, and real-time data—rather than simply producing the same result from the same input every time, as traditional AI systems would.

Moreover, AI agents differentiate themselves from Large Language Models (LLMs), such as ChatGPT, through their dual capabilities: they must not only "know" things (like LLMs, which store and recall information) but also "do" things. AI agents actively take action in response to inputs and evolving situations rather than merely providing information.

Organizations can orchestrate AI agents collaboratively, automating complex, end-to-end business processes that require both contextual understanding and adaptive decision-making.

Key capabilities that elevate AI agents above traditional RPA include:

• Goal-orientation: AI agents pursue comprehensive goals rather than merely executing discrete tasks.

• Adaptability: AI agents continuously assess and adjust their approach as new data, processes, or external conditions emerge.

• Reusability: Once built, AI agents can be shared, customized, and expanded upon by others to achieve similar or enhanced objectives.

An example of an AI agent in action is an IT tech support chatbot. Such an AI agent understands and interacts with users through plain language, troubleshoots technical problems, resets passwords, and proactively creates support tickets for human follow-up—actively combining knowledge and action to deliver value.

Key benefits of using AI Agents

Like traditional RPA and other automations, AI agents can take on tedious, repetitive, error-prone tasks, so human workers have more time to focus on more important, rewarding, and strategic work. This leads to increased productivity, improved customer experiences, and faster access to insights.

Additional benefits of using AI agents include: 

• Business growth through AI agent-driven customer experience enhancements that give customers better, faster access to accurate information, product recommendations, and customer service expertise.

• Greater efficiency for human workers by improving accuracy, accelerating decision-making, increasing confidence, and providing more time for cognitive thinking.

• Increased business scalability as AI agents can do more work without the risk of increased errors or reduced quality.

• Increased productivity to support and assist human workers as a company grows and expands.

• Non-stop effort for 24x7 processing without human intervention.

How AI agents enhance data intelligence

Data intelligence facilitates how organizations find, understand, and trust data to enable self-service analytics, improve data governance, and create AI-ready data. AI agents enhance data intelligence by ensuring high-quality, governed, and appropriate data forms the basis of human and AI decisions. 

Data intelligence improves automation by guiding AI agents to high-quality data to reduce errors, bias, and misguided decisions. But AI agents also improve data intelligence by managing data complexity with automated data curation and discovery, AI-enabled data governance, and more. These combined capabilities improve data accessibility and accuracy across the organization so businesses have the confidence to extract meaningful insights and make actionable recommendations.

AI agents also bring automation, adaptability, and autonomy to data intelligence through continuous data curation, metadata enrichment, data documentation, and governance without constant human oversight. AI agents can monitor data repositories to close gaps in data catalog descriptions, data steward assignments, and metadata for easier discoverability.

With AI agents built for data intelligence, organizations ensure that AI agents deliver trustworthy outcomes while human workers have faster access to higher-quality data.

Use cases of AI agents in data intelligence

AI agents can augment and extend data intelligence efforts through increased productivity and more intuitive user experiences. Specific ways AI agents are used across industries include:

• Monitoring data access and customer data to ensure regulatory compliance in the financial services industry.

• Managing healthcare patient data to ensure security and regulatory compliance based on access and data sensitivity.

• Analyzing metadata to evaluate supplier performance, inventory, and logistics in manufacturing settings.

• Creating targeted promotions by integrating data from marketing sources and customer behaviors.

How to deploy AI agents

With modern technology, deploying AI agents has become more accessible than ever, especially when leveraging the trusted data foundation provided by a robust data catalog. A data catalog like Alation serves as a critical platform for building effective AI agents, offering several key advantages:

• Comprehensive metadata to ensure AI agents have accurate context and understanding.

• Streamlined data discovery that allows agents to easily locate and access relevant datasets.

• Enhanced data governance capabilities that promote responsible and secure usage of data.

• Improved data quality and trust, enabling AI agents to produce more reliable insights and decisions.

Building upon these advantages, the Alation AI Agent SDK leverages the rich capabilities of the Alation data catalog platform, standardizing connections between diverse data sources and analytic tools. This ensures AI agents have immediate, secure access to high-quality data, significantly enhancing search and discovery processes, AI reasoning capabilities, and overall operational efficiency—while reducing complexity for developers.

Integrating Anthropic’s Model Context Protocol (MCP) further enables Alation AI Agent SDK to offer seamless integration with external data sources. Rather than building integrations from scratch, developers benefit from rapid, flexible development cycles that leverage Alation’s trusted and curated data environment to quickly deploy powerful, context-aware AI agents.

Alation’s agentic data intelligence platform for AI-driven automation

Alation Agentic Data Intelligence Platform empowers enterprises to automate data documentation, quality, and governance and gain the power to scale and accelerate AI enablement, business intelligence, and governance and compliance efforts.

In addition to Alation’s foundation of security, automation, and open connectors, Alation Agents deliver capabilities such as:

• Documentation Agent for accurate, consistent, and scalable data governance.

• Data Quality Agent to scale data quality monitoring and rule application.

• AI Agent SDK for seamless data intelligence integration for secure AI agent development.

Alation eases data lineage efforts 

Alation makes it easy to provide end-to-end lineage, enabling stakeholders to understand data flows, relationships, health, and impacts across the data lifecycle.

Key features include:

• Visualizations for data relationships at a business level and in detail to improve governance and highlight process issues.

• Data maps to increase transparency, identify duplicate data, and guide users to compliant, trusted data.

• Insights to eliminate waste, accelerate cloud migrations, and quickly identify the root cause of issues.

Next steps: Learn more about self-service analytics

Dive deeper into AI agents and how they improve data intelligence by checking out the following resources: