What is an agentic platform?

Move beyond chatbots. Discover what an Agentic Platform is, how it enables autonomous data management, and how it differs from Generative AI.

Defining the core: What is agentic AI?

There’s a never-ending supply of articles reminding us that AI is upending the world. But not all of those articles truly define what type of AI they are talking about. Most often, the major news outlets are reporting on generative AI products — things like ChatGPT, Google Gemini, Anthropic’s Claude, among others. These widely-known Large Language Models (LLMs) most often help consumers with tasks that are fed to the chatbot one at a time. 

Agentic AI is different. Unlike a chatbot or other LLM, agentic AI is an autonomous AI system that can operate with minimal human oversight within given parameters. Agentic AI can think, reason, and take action to complete tasks independently — no spoon feeding of prompts required. 

When we’re talking end-to-end automation, agentic AI becomes a really big deal. And the definition of agentic AI can’t lump LLMs in with AI agent tech. 

Agentic AI vs. Generative AI: What’s the difference?

What’s the difference between generative AI and agentic AI? 

• Generative AI creates content based on prompts, reacting to the information fed to it to create images, text, or even computer code. 
• Agentic AI is a proactive system that can plan and executive multi-step functions in order to meet a defined goal. 

In summary: Generative AI needs a human to tell it to do anything and everything. Agentic AI operates with far less hand-holding, able to filter complex choices and make decisions. 

An AI agent can perform multi-step workflows to complete a task, such as booking a flight, rather than needing human intervention at each step of the way.

Why you need an “agentic platform” (not just agents)

LLMs offer a lot of value to the average consumer. They can synthesize sources and help dramatically reduce the time needed for research or information gathering. But feeding new information into an LLM every time you want to use it to influence your own decision-making still requires the total attention of a human being. An LLM agent can help answer questions or find concert tickets or spot anomalies in a report — once it’s been told what to look for. 

An agentic platform is different. Once set in place, an agentic platform can use the breadth of information available to it to automate workflows and complete tasks, freeing up human employees for other, more complex work. 

Ataccama’s ONE AI Agent can plan and run tasks across an entire data observability platform. Ataccama ONE takes actions, explains the action it took, and delivers results in one connected flow — no babysitting required. 

In a unified, end-to-end platform, autonomous AI agents can perform search and discovery through data quality and orchestration, instead of relying on individual agents to perform isolated tasks. In a single platform, agentic AI operates within the governance embedded in the platform, making sure to operate with trusted data, follow official policy, and scale safely in sensitive use cases, like healthcare and finance.

Orchestration and workflow automation

Agentic AI is a key component of AI orchestration, which is when multiple AI models, software tools, and data sources combine to provide access to cohesive workflows. This agentic AI coordinates tasks to provide increased efficiency and accuracy. 

This is where true workflow automation comes in. Instead of an AI agent pausing at every step along the way for human input to continue its task, autonomous AI agents can synthesize data across systems and make decisions in order to move to the next action at hand. 

An example of potential agentic AI use in healthcare: If a pregnant patient takes a glucose challenge test and the results come back above the recommended threshold, agentic AI could automatically order the more precise three-hour fasting glucose test and send a message to the patient to book the appointment with the lab. As gestational diabetes affects hundreds of thousands of patients each year, the potential efficiencies created by automating this one task would free up hours and hours of time on behalf of the clinician, and could create considerable cost savings. 

Governance and security guardrails

As AI comes under increasing government scrutiny, companies adopting policies that create governance and security guardrails for AI will be ahead of the curve. The two are different, but must co-exist in order to create and execute the privacy and security necessary for a robust AI-powered software. 

The difference between governance and security guardrails in AI: 

• AI governance is when an organization sets the policies and compliance rules for using AI, as well as assigns roles for overseeing that governance. 
• Security guardrails for AI are the enforcement of these policies, where the technical aspects of the software prevent risks such as data leakage and hallucinations. 

When used together, AI governance and security guardrails reduce risk and build trust within the organization and with customers.

The role of data quality in an agentic future

Data quality continues to be one of the most important components of incorporating AI into an organization’s workflow. An AI agent cannot produce consistent and accurate results if the data available to it is incomplete, incorrect, or inconsistent. 

Data quality assurance must remain an end-to-end concern for every role in the company, from CEO to front-line customer service. Trusted, high-quality data paired with the workflow automation possibilities of AI agents is what is fueling potential growth in many high-tech markets. 

Real-world use cases for agentic platforms

There are many real-world use cases for AI agents. Anywhere an organization handles data-heavy, highly repetitive tasks is an opportunity for agentic AI to step in and provide cost-saving workflow automation. 

Some examples of agentic AI use cases: 

• Education — Not just in schools themselves, but agentic AI could level up a company’s in-house training. Tasks like onboarding new employees and instilling company policies could be automated to take the administrative task burden off of HR departments. 
• Finance — Loan approval departments handle high volumes of data on a constant basis. By having AI agents gather financial data and complete risk assessments, companies not only save time and money, but also ensure compliance and security. 
• Healthcare — There is no end to the amount that agentic AI could assist in the healthcare world. From automating messaging between providers and patients to analyzing large data sets and presenting next steps or potential diagnosis for physicians to review, AI agents could transform the future of healthcare delivery. 
• Supply Chain Management — Managing supply chains is an area where agentic AI could create significant efficiencies. An AI agent could monitor current and future component stock, predict when that stock would exhaust, and place an order for more — even if the threshold for replacements occurs during an overnight manufacturing shift when purchasing departments aren’t typically staffed. 

Preparing your data estate for the agentic era

Just as data quality is a prerequisite for adopting agentic AI, knowing the full condition of your data estate is essential to prepare for the agentic era. 

Here are some steps you can take to ensure that your data is ready for agentic AI: 

• Focus on data quality: Consolidate, clean, and continuously monitor your data so it is of the highest quality possible. AI agents need accurate data in order to work in the way they are intended. 
• Ensure end-to-end data observability to monitor data quality: If you aren’t already, consider adopting an end-to-end unified data trust platform like Ataccama ONE. This gives you real-time insights into the quality of your data across the entire data chain. 
• Create and implement data governance and security protocols: In order to responsibly guide future AI systems, your data governance and security protocols must be up-to-date, industry-specific, and followed by all levels of the organization. 
• Manage data automatically: Have in place a comprehensive data quality management strategy, defining the roles responsible for data quality and routine quality assessments where data is scrubbed for quality assurance. Automating this with an agentic AI platform like Ataccama ONE Agentic takes data quality management to the next level. 

Organizations that prepare their data estate for agentic AI will be leaps ahead of those who do not. 

FAQs

How is an Agentic Platform different from a standard AI chatbot?
A standard AI chatbot creates text, images, and other content based on the information it has been given, while an agentic AI platform can reason and take independent action to complete complex tasks. 

Why is data quality important for Agentic AI?
Agentic AI relies on top-notch data quality in order to make informed, concise decisions. Without robust data quality, agentic AI cannot independently make accurate decisions. 

What are examples of autonomous AI agents in business?
Autonomous AI agents can book flights, automate loan approvals, ensure risk compliance, and more.

Is Agentic AI the same as AGI (Artificial General Intelligence)?
No — Agentic AI focuses on using AI to complete approved tasks within workflows, while Artificial General Intelligence is still a theoretical future possibility where AGI has broad-spectrum human-level intelligence.