Agentic AI: A complete guide for organizations

Agentic AI vs. generative AI

While both agentic AI and generative AI are subsets of artificial intelligence, they serve different purposes and exhibit distinct characteristics.

Generative AI: The creative engine

Generative AI focuses on creating new content—such as text, images, music, or code—based on patterns learned from existing data. It operates reactively, producing outputs in response to specific prompts or inputs. For instance, tools like ChatGPT or DALL·E generate human-like text or images when given a prompt. These models rely on deep learning techniques to identify patterns and generate content that mimics human creativity.

Agentic AI: The autonomous executor

Agentic AI, on the other hand, emphasizes autonomous decision-making and action-taking to achieve specific objectives. It operates proactively, initiating tasks without the need for explicit prompts. Agentic AI systems perceive their environment, reason about the best course of action, plan steps to achieve goals, and execute those plans—adapting as necessary based on feedback. Examples include autonomous vehicles navigating traffic or AI-driven personal assistants managing schedules and communications.

Key differences

• Primary function: Generative AI is designed for content creation, producing novel outputs based on learned data patterns. Agentic AI is built for autonomous operation, making decisions and taking actions to achieve defined goals.
  

• Operational mode: Generative AI is reactive, requiring specific prompts to generate outputs. Agentic AI is proactive, capable of initiating actions based on its understanding of the environment and objectives.
  

• Human interaction: Generative AI typically requires continuous human input for each task. Agentic AI operates with minimal human intervention, making it suitable for automating complex, multi-step processes.
  

• Learning approach: While both can incorporate machine learning, agentic AI often utilizes reinforcement learning to adapt its behavior based on feedback from its actions, enhancing its decision-making over time.
  

In essence, generative AI is about content creation, whereas agentic AI is about autonomous operation and problem-solving.

How does agentic AI work?

Agentic AI systems function through a continuous cycle that mirrors human cognitive processes, enabling them to operate autonomously and adaptively.

1. Perception

Agentic AI systems begin by gathering data from their environment using various sensors or data inputs. This could include visual data from cameras, auditory data from microphones, or digital data from online sources. The goal is to build an accurate understanding of the current state of the environment.

2. Reasoning

Once data is collected, the AI system processes and interprets this information to understand context, identify patterns, and predict potential outcomes. This reasoning process allows the system to make informed decisions about how to proceed toward its objectives.

3. Planning

Based on its reasoning, the agentic AI develops a sequence of actions or a plan to achieve its goals. This planning stage involves setting sub-goals, determining necessary resources, and establishing timelines.

4. Action

The AI system then executes the planned actions, interacting with its environment to effect change. This could involve physical movements, digital communications, or other forms of interaction.

5. Adaptation

After acting, the system observes the outcomes of its actions and compares them to the expected results. If discrepancies are found, the AI adapts its future behavior accordingly, refining its perception, reasoning, and planning processes.

This cycle—perception, reasoning, planning, action, and adaptation—enables agentic AI systems to operate autonomously, learn from their experiences, and improve over time.

Agentic AI architecture and frameworks

Building agentic AI involves several key components:

• AI agents: Autonomous entities capable of performing tasks.
  

• Orchestration layer: Coordinates multiple agents to work together.

• Knowledge base: Stores information and experiences for decision-making.

• Communication protocols: Enable agents to interact with each other and external systems.

Frameworks like Microsoft's AutoGen and LangChain facilitate the development of such architectures. 

Agentic AI workflows and design patterns

Effective agentic AI workflows incorporate:

• Goal definition: Clearly specifying objectives for agents.
  

• Task decomposition: Breaking down complex tasks into manageable subtasks.
  

• Agent assignment: Allocating subtasks to appropriate agents.
  

• Feedback loops: Allowing agents to learn from outcomes and adjust actions accordingly.

Design patterns such as the "perceive-reason-act" loop are commonly used to structure these workflows.

Agentic AI in data and cloud security

In the realm of data and cloud security, agentic AI is emerging as a transformative force, enhancing organizations' abilities to detect threats, ensure compliance, and manage access controls with unprecedented efficiency and autonomy.

Threat detection

Agentic AI systems excel in real-time threat detection by continuously monitoring network traffic, user behavior, and system activities. Unlike traditional security tools that rely on predefined rules, agentic AI employs machine learning and behavioral analytics to identify anomalies and potential threats, including zero-day vulnerabilities and advanced persistent threats (APTs). Upon detecting suspicious activity, these systems can autonomously initiate responses such as isolating affected systems, blocking malicious access, or alerting security teams, thereby reducing response times and mitigating potential damages. 

Compliance monitoring

Ensuring adherence to data protection regulations is a complex and ongoing challenge. Agentic AI simplifies compliance monitoring by automating the tracking and reporting of data handling practices. These systems can continuously assess operations against regulatory requirements, flagging deviations and generating audit-ready reports. This proactive approach not only aids in maintaining compliance but also reduces the risk of penalties associated with regulatory breaches.

Access control

Managing user permissions dynamically is critical in preventing unauthorized access. Agentic AI enhances access control by evaluating contextual factors such as user behavior, location, and device security posture to make real-time decisions about granting or revoking access. This adaptive mechanism ensures that access rights are aligned with current risk assessments, thereby strengthening the organization's security posture.

By integrating agentic AI into data and cloud security frameworks, organizations can proactively protect sensitive information, streamline compliance efforts, and maintain robust security postures in an increasingly complex threat landscape.

How Cloudera leverages agentic AI

Cloudera integrates agentic AI into its platform to provide:

• Advanced analytics: Enabling autonomous data processing and insight generation.
  

• Machine learning: Facilitating the development of intelligent models that adapt over time.
  

• Data engineering: Streamlining data workflows and pipeline management.
  

By incorporating agentic AI, Cloudera enhances its ability to deliver scalable, secure, and intelligent data solutions.

Implementing agentic AI in your organization

To adopt agentic AI:

1. Assess needs: Identify areas where autonomy can add value.
   

2. Choose tools: Select appropriate frameworks and platforms.
   

3. Develop agents: Build AI agents tailored to specific tasks.
   

4. Integrate systems: Ensure seamless communication between agents and existing infrastructure.
   

5. Monitor and refine: Continuously evaluate performance and make improvements.
   

This structured approach facilitates the effective deployment of agentic AI solutions.

Agentic AI tools and platforms

The rise of agentic AI has spurred the development of various tools and platforms designed to facilitate the creation, deployment, and management of autonomous agents. These tools cater to different aspects of agentic AI systems, from orchestration and memory management to multi-agent collaboration.

LangChain and LangGraph

LangChain is a prominent framework that enables developers to build applications powered by large language models (LLMs). It provides a suite of tools for integrating LLMs with external data sources, APIs, and user interfaces. LangGraph, an extension of LangChain, focuses on constructing stateful and complex agentic applications, supporting features like memory retention, conditional workflows, and inter-agent communication. 

AutoGen by Microsoft

AutoGen is an open-source framework developed by Microsoft Research for creating multi-agent AI applications. It allows developers to design systems where multiple AI agents can communicate, cooperate, and perform tasks together. AutoGen supports various use cases, including automated workflows, multi-agent collaboration, and dynamic task-solving. 

Open Agent Platform by LangChain

The Open Agent Platform (OAP) by LangChain is a no-code, browser-based interface that enables users to create, deploy, and manage AI agents with ease. Built on LangGraph, OAP supports agent orchestration, multi-agent supervision, and integration with external services through Model Context Protocol (MCP) servers. It aims to democratize AI agent creation, making it accessible to non-technical users. 

AutoGen Studio

AutoGen Studio is a no-code developer tool for building and debugging multi-agent systems. It offers a web interface and a Python API for representing LLM-enabled agents using a declarative specification. Features include a drag-and-drop UI for workflow specification, interactive evaluation, and a gallery of reusable agent components. 

These platforms and tools are instrumental in advancing the capabilities of agentic AI, providing developers and organizations with the resources needed to build sophisticated, autonomous systems.

Agentic AI risks and governance

While agentic AI offers numerous benefits, it also introduces specific risks that organizations must address through robust governance frameworks.

Key risks

• Autonomy and unpredictability: Autonomous agents may behave in unforeseen ways, leading to unintended consequences.
  

• Security vulnerabilities: Agentic systems can be susceptible to attacks if not properly secured, potentially leading to data breaches or system compromises.
  

• Accountability: Determining responsibility for the actions of autonomous agents can be challenging, especially in complex, multi-agent environments.
  

Governance strategies

• Ethical guidelines: Establish clear ethical principles to guide the development and deployment of agentic AI systems.
  

• Transparency and explainability: Ensure that agentic AI systems are transparent in their decision-making processes and can provide explanations for their actions.
  

• Monitoring and oversight: Implement continuous monitoring of agentic AI systems to detect and address issues promptly.
  

• Regulatory compliance: Adhere to relevant laws and regulations governing AI and data protection.
  

By proactively addressing these risks through comprehensive governance, organizations can harness the power of agentic AI while mitigating potential downsides.

Understand how advancements in large language models (LLMs), natural language processing (NLP), and agentic AI is revolutionizing automation and decision-making.