Agentic AI

Why agentic AI matters for the enterprise (The 'agentic shift')

Beyond the definition, understanding the significance of this 'agentic shift' is necessary for data science and IT leaders. Agentic AI is different from simple automation, robotic process automation (RPA), or basic machine learning (ML) models because these agents act independently towards a set goal and can dynamically adapt their approach.

This capability enables them to handle complex, multi-step tasks that previously required significant coding and/or human effort. For example, in financial services, agents can perform real-time risk analysis on diverse data streams for fraud detection or market monitoring. Insurance companies might use agents to automate multi-stage claims processing, including damage assessment. In life sciences research and development, agents could optimize experimental parameters and analyze results.

For large Global 2000 organizations, this means opportunities to improve efficiency and find new ways to operate. Agentic AI can automate sophisticated processes to help scale operations, enable new kinds of data-driven discovery, create more personalized customer experiences, and improve the speed and quality of business decisions.

Recent progress with large language models (LLMs) helps make this possible. LLMs provide the needed reasoning and language understanding capabilities. This allows agents to interpret complex requests, use different software tools or APIs, and plan effective sequences of actions.

The market growth reflects this potential. Projections indicate that the AI agent market will expand from approximately $5.1 billion in 2024 to over $47 billion by 2030. This rapid growth signals why understanding agentic AI is becoming a strategic requirement. Enterprises need to prepare for adoption to stay competitive. This preparation involves scaling innovation responsibly, managing operational complexity, ensuring compliance across AI systems, and finding deeper value in enterprise data.

Types of agentic AI

Agentic AI isn't monolithic. These systems vary significantly in terms of their underlying complexity, intended function, and architectural design. Understanding these variations helps clarify how they apply in enterprise settings.

Based on capabilities and complexity

• Simple task-specific agents or bots: Think of these as the starting point. They often follow predefined rules or execute single functions, acting as precursors to more advanced agent systems.
• Advanced reasoning agents: These agents use large language models. They can handle complex planning, use different software tools dynamically through APIs or other means, learn from interactions, and manage ambiguous situations. Their capabilities are suited for sophisticated data analysis or adaptive process automation.

Based on function or application

• Data analysis and insight agents: These are designed to explore large datasets autonomously. They can identify complex patterns, generate testable hypotheses, and potentially automate the creation of dashboards or summary reports.
• Automation agents: These agents focus on orchestrating data pipelines. They might automate model validation and testing routines against predefined criteria, manage MLOps deployment workflows across environments, or execute recurring data governance and compliance checks.
• Monitoring agents: These are specialized agents that continuously track model performance metrics and business KPIs. They can detect data drift or operational anomalies in real-time, monitor system resource utilization for AI workloads to optimize costs, or scan external data sources, such as news or regulatory updates, for important events.
• Simulation agents: These agents build and run complex simulations. Data scientists might use them for 'what-if' analysis, such as modeling market behavior under different economic scenarios, simulating patient cohort responses in virtual clinical trials, or stress-testing IT infrastructure resilience under various load conditions.

Based on architecture

• Single-agent systems: In these systems, one agent performs the assigned tasks. This architecture works well for clearly defined, self-contained problems.
• Multi-agent systems (MAS): This concept involves multiple agents working together. They might divide complex tasks, bring specialized skills to a problem, or negotiate outcomes. This approach can be useful for tackling highly complex problems needing diverse expertise or parallel processing, and frameworks like AutoGen help build such systems.

Agentic AI use cases across industries

To make the potential more concrete, let's look at specific ways agentic AI is being applied across key industries relevant to large enterprises.

Financial services

• Agents automate market monitoring to find anomalies for risk management or trading insights.
• They provide personalized financial advice using analysis of customer profiles and goals.
• Systems detect potentially fraudulent transactions in real-time as they occur.
• Agents assist in executing algorithmic trading strategies based on market conditions.
• They can also help automate parts of compliance checks and regulatory reporting.

Insurance

• Agents handle claims processing autonomously, covering steps from initial notice to damage assessment and payout calculation.
• They perform dynamic underwriting and pricing decisions using disparate data sources, like telematics.
• Systems enhance fraud detection by identifying complex patterns in claims data.
• They offer support tools that automate tasks for human insurance agents and brokers.

Life sciences

• Agentic systems accelerate drug discovery by automatically analyzing vast amounts of research data or screening potential compounds.
• They help optimize clinical trial processes, assisting with tasks like patient recruitment matching or analyzing site suitability.
• Agents automate parts of the labor-intensive process of preparing regulatory documents.
• They can orchestrate complex laboratory experiment workflows and subsequent data analysis pipelines.

Public sector

• Internal AI assistants help government employees perform research, summarize documents, or handle administrative tasks more efficiently.
• Agents model complex scenarios to inform policy planning related to infrastructure, resource allocation, or disaster response.
• Systems work to improve citizen-facing service delivery by providing automated information or guidance through processes.
• They assist in automating compliance monitoring for programs and detecting potential fraud or waste in benefits systems.

Agentic AI FAQ

Understanding these different types of agents naturally leads to practical questions. Here are answers to some common queries about agentic AI in an enterprise environment.

1 - How is agentic AI different from Robotic Process Automation (RPA)?

RPA typically involves bots following predefined rules to automate repetitive, structured tasks within specific software applications, often mimicking human actions such as clicks and keystrokes. Agentic AI goes beyond this. Agents are goal-oriented, not just rule-following. They can reason, plan complex sequences of actions, adapt to changing situations, and interact with a wider range of tools and data sources autonomously to achieve an objective, even if the exact steps aren't pre-programmed.

2 - How is agentic AI different from chatbots?

Chatbots are primarily designed for conversation. They respond to user queries, answer questions, or guide users through simple interactions based on predefined scripts or learned conversational patterns. While some advanced chatbots use AI, agentic AI systems have a broader scope. Agents focus on taking actions and completing tasks autonomously based on a goal, which might involve conversation but extends to interacting with software, analyzing data, or controlling systems. They are proactive and task-oriented, rather than merely conversational and reactive.

3 - How is agentic AI different from AI assistants?

AI assistants (like Siri or Alexa) typically respond to direct commands to perform specific, often predefined tasks like setting reminders, playing music, or retrieving simple information. Agentic AI represents a step further in autonomy and complexity. Agents can tackle broader goals requiring multi-step planning, reasoning, learning, and independent decision-making with less explicit instruction for each step. They aim to achieve outcomes, not just execute commands.

4 - How is agentic AI different from generative AI?

Generative AI primarily focuses on creating new content like text, images, or code based on learned patterns. Think of it as an author writing a script. Agentic AI, however, focuses on taking actions and achieving specific goals within an environment. It's more like the actor performing the actions in the script. Agentic systems often use generative AI, particularly large language models, as their 'brain' for reasoning, understanding language, and planning those actions. The key difference is the emphasis on autonomous action and goal completion.

5 - What are some common agentic AI frameworks?

Frameworks provide structure and reusable components that help development teams build agentic systems more efficiently and reliably. For data science and IT leaders in large organizations, they offer benefits like standardization across teams, easier management of dependencies, and abstraction of complex details like memory or tool integration (which can involve underlying standards like the Model Context Protocol (MCP) for managing how agents access external information or capabilities). Some well-known open-source examples include LangChain (for connecting agent components), LlamaIndex (often used for integrating data sources, especially for retrieval-augmented generation), and AutoGen (designed for building multi-agent collaborations). Additionally, major cloud and data platforms offer integrated frameworks, such as Google Vertex AI's Agent Development Kit or options within Databricks.

6 - Can agentic AI automate data science workflows?

Yes, agentic AI can automate certain aspects of data science workflows, particularly routine or well-defined tasks. Examples include assisting with data cleaning suggestions, exploring potential features automatically, executing standard model training scripts, running defined hyperparameter searches, generating baseline performance reports, or triggering monitoring alerts based on preset rules. However, it doesn't automate the entire process. The complex interpretation of results, strategic decisions about problem framing or methodology, novel algorithm design, ensuring that ethical considerations are met, and final validation still require significant human expertise and judgment. Agentic AI currently acts more as an assistant or an automation tool for specific workflow segments, augmenting the data scientist's capabilities.

7 - What are the main challenges in building and deploying agentic AI?

Deploying agentic AI effectively in large enterprises presents several hurdles, often amplified by organizational scale and regulatory demands. Managing the complexity and ensuring predictable, reliable behavior from autonomous systems at scale is a primary concern. Establishing trust and explainability becomes necessary, especially in regulated industries like finance or life sciences, where auditing agent decisions is required. Robust governance frameworks are needed to manage permissions, enforce operational policies, ensure ethical use, and maintain security across the organization. Integrating agents smoothly with diverse existing enterprise systems and legacy data sources can be difficult. Finally, ensuring reliability and robustness, meaning agents handle errors or unexpected situations gracefully, requires careful design and testing.

8 - Does agentic AI replace data scientists?

No, agentic AI is better viewed as a tool that augments the capabilities of data scientists, rather than replacing them. It can take over time-consuming, repetitive, or computationally demanding tasks within a workflow. This shift allows data scientists to focus on higher-value activities. These include strategic problem definition, complex analysis requiring deep domain knowledge, interpreting nuanced results, ensuring ethical AI deployment and fairness, communicating findings effectively to stakeholders, and importantly, designing, overseeing, validating, and managing the governance frameworks for the agentic systems themselves. Human oversight remains essential, particularly for critical decisions and ensuring responsible AI practices.