What is Agentic AI
Agentic AI is an advanced form of artificial intelligence focused on autonomous decision-making and action. Unlike traditional AI, which primarily responds to commands or analyzes data, agentic AI can set goals, plan, and execute tasks with minimal human intervention.
Agentic AI systems ingest vast amounts of data from multiple sources to analyze challenges, develop strategies, and complete tasks independently. Over time, AI agents learn and improve by creating a data flywheel, where human and AI feedback is used to refine models and improve outcomes.
How It Works
Agentic AI uses AI agents—autonomous entities designed to perform specific tasks. These agents work by using sophisticated reasoning and planning to solve complex, multi-step problems. At its core, agentic AI operates through a continuous cycle of perception, reasoning, planning, action, and reflection.
When you provide a goal, the agent breaks it down into subtasks, executes each step using available tools and systems, handles obstacles through replanning, and delivers results—all while learning from feedback to improve future performance.
Core Components
Agentic AI systems are designed to operate with a higher degree of autonomy through these essential components.
The agent gathers information from its surroundings and different sources—sensors, databases, APIs, and user interfaces. This involves analyzing text, images, or other forms of data to understand the current situation.
Using a large language model (LLM), the agent analyzes gathered data to understand context, identify relevant information, and formulate potential solutions. It can parse complex inputs to understand the task's purpose and requirements.
The agent develops a plan using gathered information. This involves setting goals, breaking them into smaller steps, determining the best sequence of actions, and identifying the tools needed to achieve each step.
Based on its plan, the agent takes action—performing tasks, making decisions, calling APIs, interacting with databases, automating browsers, or communicating with other systems to complete objectives.
After taking action, the agent learns from results. It evaluates whether actions were successful and uses this feedback to adjust plans and improve future performance. This continuous cycle enables ongoing improvement.
Agents interact with external tools and systems—APIs, databases, browsers, code interpreters, and applications—extending their capabilities beyond pure reasoning to actually execute tasks in the real world.
Understanding the Difference
While both are forms of artificial intelligence and can be used together, they have distinct functionalities.
Focused on creating new content—text, images, code, or music—based on input prompts. The value is generated by what the model can produce. You can generate or edit content and perform simple function calling.
A subset of generative AI centered around orchestration and execution of agents that use LLMs as a "brain" to perform actions. Goes beyond content creation to execute actions in underlying systems to achieve higher-level goals.
Example: Generative AI could create marketing materials, while agentic AI could then deploy these materials, track their performance, and automatically adjust the marketing strategy based on results. Agentic AI can use generative AI as a tool to achieve its goals.
Applications
Agentic AI is being applied across various industries to automate tasks and optimize processes.
Agentic AI can empower human agents to tackle more complex problems by managing customer inquiries, resolving issues, and delivering personalized support autonomously.
By analyzing data from sales, inventory, and shipping, agentic AI can optimize supply chains, predict demand, and automate logistics operations end-to-end.
Doctors and researchers can use agentic AI to help with diagnosis, treatment planning, and drug discovery by analyzing medical records, research papers, and clinical trials.
Agentic AI can help automate fraud detection, risk assessment, and investment strategies by analyzing market data, customer behavior, and financial statements.
By automating code generation, debugging, and testing, agentic AI can help accelerate the development cycle and improve code quality across teams.
Automated incident response, infrastructure management, and system monitoring—agents can detect issues, diagnose problems, and implement fixes autonomously.
Best Practices
Implementing agentic AI in a business requires careful consideration.
Identify the specific problems that AI agents will solve and how they align with business goals.
Agentic AI systems rely on high-quality data to make accurate decisions. Ensure data is accurate, complete, and properly formatted.
Address potential biases and ensure human oversight is in place. Be transparent about how agentic AI is being used.
Implement robust security measures to protect AI systems and the data they use from breaches and unauthorized access.
Invest in systems that provide insights into decision-making processes for better debugging and trust building.
Continuously monitor AI agent performance, address issues, and refine algorithms as needed.
Getting Started
Describe what you want accomplished in natural language. Set boundaries, permissions, and approval requirements.
Review the agent's proposed plan before execution. Adjust steps, add constraints, or approve directly.
Watch tasks complete in real-time. Get notified on exceptions. Access complete logs of every action taken.
