What is Agentic AI
Agentic AI refers to a new type of artificial intelligence that doesn’t just respond to questions or commands — it takes action on its own.
Unlike traditional models like GPT, which wait for input and then generate output, agentic AI goes a step further. It can:
Plan: figure out what needs to be done,
Act: carry out tasks based on those plans, and
Adapt: adjust its actions when conditions change — all with little to no human supervision.
Think of it like a smart robot with a mission. You give it a goal and some basic instructions, and from there, it figures out the best way to get the job done, even if the situation changes along the way.
In short, agentic AI is about AI that doesn’t just think — it acts.
How Agentic AI work
Perceive
In the Perceive stage, the AI agent collects data from multiple sources, including databases, APIs, and live feeds, ensuring it has up-to-date information for analysis. By understanding the environment and current context, the AI can accurately interpret tasks and prepare for decision-making.
Reasoning – The AI’s Thinking Engine
During Reasoning, the AI acts as the brain of the operation. Large language models (LLMs) interpret data, understand goals, detect patterns, and generate strategies. They coordinate specialized models for tasks like content creation, visual processing, or recommendations, using techniques such as retrieval-augmented generation (RAG) to ensure outputs are accurate and relevant. Predictive models and long-term memory systems help the AI plan effectively and adapt to changing circumstances.
Act
In the Act stage, agentic AI executes the strategies developed during reasoning. It evaluates multiple options, predicts outcomes, and chooses the most effective actions using methods like probabilistic reasoning or machine learning.
Through APIs and connected software, the AI carries out tasks such as writing code, processing documents, running simulations, or managing third-party applications. Built-in safety measures, including human oversight or action limits, ensure that operations remain controlled and compliant. Every action is logged and monitored, providing transparency and governance.
Learn
The Learning stage allows agentic AI to continually enhance its performance. After executing actions, the AI evaluates results and updates its models using reinforcement learning, self-supervised learning, or feedback from humans and other AI agents. Techniques like proximal policy optimization (PPO) and Q-learning help refine strategies over time.
Metrics such as success rate, confidence, and latency track performance, while multi-agent systems share knowledge across communal memory layers. This feedback loop — or “data flywheel” — ensures the AI becomes more effective, efficient, and adaptable with each iteration.
Examples of Agentic AI
Customer
AI agents are reshaping customer support by automating routine queries, providing instant answers, and enabling 24/7 self-service. For example, chatbots like those used by banks handle account inquiries, while digital humans in retail offer lifelike interactions to guide customers through purchases or troubleshoot issues during peak times. These tools reduce wait times, improve satisfaction, and free human agents to tackle more complex problems
Healthcare
AI helps with diagnostics, patient monitoring, and admin tasks, improving accuracy and efficiency while letting professionals focus on critical care.
Content Creation
AI generates text, images, and videos at scale, speeding up production and supporting creativity for marketers and creators.
Software Engineering
AI assists with coding, testing, and deployment, automating repetitive tasks, reducing errors, and boosting developer productivity.
Sales
AI analyzes customer data, prioritizes leads, and automates outreach, enabling sales teams to focus on high-value conversations and close deals faster.
Side Effects of Agentic AI
Unintended Behaviors: AI may optimize too aggressively, causing unexpected outcomes.
Systemic Failures: Multiple agents can create bottlenecks, conflicts, or cascading errors.
Misaligned Goals: Without clear objectives, AI actions may diverge from intended outcomes.
Oversight Challenges: Autonomous operation makes monitoring and safety more complex.