Getting To Know Agentic AI

Key Takeaways

• Agentic AI refers to AI systems that exhibit a level of autonomy and decision-making capability, enabling them to act independently in pursuit of goals.

• Agentic AI can learn from its experiences and improve its decision-making abilities over time.

• The rise of agentic AI could transform industries like healthcare, transportation, and customer service by automating complex processes.

Imagine having a smart assistant who doesn’t just answer questions but takes charge, makes decisions, and adjusts plans when things don’t go as expected. That’s Agentic AI in a nutshell!

This teachable content will walk you through what Agentic AI is, how it works, and why it’s exciting—all in simple, everyday terms.

To make it fun and relatable, we’ll use a cooking example where the AI acts as your chef, guiding you step-by-step to create a unique dish. Let’s dive in!

What Is Agentic AI?

Agentic AI is a type of artificial intelligence that can act on its own, solve problems, and adapt to different situations. Unlike traditional AI, which might just give you information (like a weather forecast) or predictions (like suggesting a movie), Agentic AI goes further. It interacts with the world, takes actions, and learns from what happens next. Think of it as an AI with agency—it has the freedom to decide what to do and how to adjust when surprises pop up.

Here’s what makes Agentic AI special:

👉 It acts independently: It doesn’t need you to hold its hand for every step.

👉 It adapts: If something unexpected happens, it changes its approach.

👉 It works with and responds to the real world: It can’t physically do things itself (like pick up a spoon), but it tells humans or other systems what to do.

👉 It uses feedback: It listens to what happens after its actions and decides what to do next.

Types Of Agents In Agentic AI

Agentic AI isn’t just one thing—it’s made up of different types of agents, each with its own way of handling tasks. Here’s a quick look at them:

• Reactive Agents: These are the fast responders. They look at what’s happening right now and act instantly, without worrying about the past or future. Picture a chef who notices the stove is too hot and turns it down, but doesn’t think about the whole recipe—just the moment.

• Deliberative Agents: These are the planners. They keep a mental map of the world and think ahead to reach their goals. Imagine a chef who plans every step of a meal, knowing exactly how each move affects the final dish.

• Learning Agents: These are the improvers. They get better over time by learning from what they’ve done before. Think of a chef who tweaks their cooking based on past meals, mastering their skills with every dish.

• Hybrid Agents: These are the all-rounders. They mix reacting, planning, and learning to tackle anything that comes their way. They’re like a super chef who can fix a burning pan, plan a fancy dinner, and learn a new recipe—all at once.

The Anatomy Of An AI Agent

Just like a human worker needs certain things to do their job, an AI agent has five key components:

1. Brain (Large Language Model – LLM) – This is the core intelligence (like GPT, Gemini, Claude). It understands language, reasons, and figures out how to follow instructions and use its tools. Without the brain, the other parts are useless.

2. Instructions (Prompting) – These are the specific directions you give the agent’s brain. You program the agent’s behavior, personality, and tasks largely through carefully written instructions in natural language, rather than complex code.

3. Memory – This allows the agent to remember recent parts of the conversation or task progress. It prevents it from forgetting everything instantly, enabling more coherent interactions. Most platforms handle this automatically.

4. External Knowledge (Optional) – LLMs have general knowledge up to a certain date. This component lets you feed the agent specific, up-to-date, or private information (like company documents, product lists, FAQs) so it can handle tasks related to your specific context.

5. Tools (Crucial for Action) – These are what allow the agent to do things beyond just chatting. Tools give the agent access to other software, websites, or databases via APIs (Application Programming Interfaces). Think of APIs as digital “buttons” the agent can press to perform actions like checking data, updating a spreadsheet, sending an email, booking something, etc.

How To Build AI Agents (The “Three Ingredients” Framework)

While there are five components when you actually build an agent, you mainly focus on mixing three core “ingredients” (like a chef):

1. Prompting: Designing the core instructions, role, and behavior. This is the glue holding everything together.

2. Knowledge: Deciding what, if any, external data the agent needs access to.

3. Tools: Deciding what actions the agent needs to be able to take and connecting the necessary tools (via APIs).

The Building Process (Conceptual Steps)

1. Define the Goal: What task do you want the digital worker (the agent) to perform? (e.g., qualify leads, assist sales reps, answer customer questions).

2. Choose a Platform: Select a no-code/low-code platform (like Relevance AI, Voiceflow, N8N, Agentive) that suits your task. These platforms simplify the process.

3. Configure the “Ingredients”:

👉 Write the Prompt: Craft clear instructions defining the agent’s role, the task, how it should behave, and when and how it should use its knowledge and tools.

👉 Add Knowledge (If Needed): Upload relevant documents (like PDFs, website data) to the platform’s knowledge base feature.

👉 Connect/Build Tools: Use pre-built integrations offered by the platform (like Gmail, Google Sheets). ensure tools have clear descriptions (schemas) so the agent understands what they do, what information (inputs) they need, and what results (outputs) to expect. The platform often helps generate or requires you to provide these descriptions.

4. Connect via APIs (For Tools): Understand that tools work by making API calls.

👉 Get Requests: Fetching information (like checking weather, getting website data).

👉 Post Requests: Sending information (like updating a CRM, sending an email).

5. Test and Iterate: Test the agent thoroughly. Debug issues by looking at how it uses tools and interprets information (platform debug modes help). Refine the prompts, tool logic, or knowledge based on test results.

6. Deploy: Connect the agent to the desired channel (website chat widget, WhatsApp, phone number, internal tool).

Fundamental Components That Make Up An Intelligent Agent

Sensing The World (Perception)

• What it does: The agent gathers information about its environment using sensors or data inputs.

• How it works: This is the agent’s way of “seeing” or “hearing” what’s going on around it.

• Examples:

👉 A robot uses cameras to detect obstacles or microphones to pick up sounds.

👉 A chatbot reads your text messages or listens to your voice commands.

• Why it matters: Without perception, the agent would be blind and deaf—unable to understand its surroundings.

Storing Knowledge (Knowledge Representation)

• What it does: The agent keeps a “mental notebook” of useful information, including facts about the world, its own abilities, and its goals.’

• How it works: This knowledge can be stored as rules, databases, or even machine learning models.

• Examples:

👉 A self-driving car knows maps and traffic laws.

👉 A virtual assistant remembers your schedule and preferences.

• Why it matters: Knowledge gives the agent context to make sense of what it perceives and plan its next move.

Thinking And Deciding (Reasoning And Decision-Making)

• What it does: The agent processes what it knows and decides what to do next—its “brain” in action.

• How it works: It might follow pre-set rules, create plans, or use advanced techniques like machine learning to pick the best option.

• Examples:

👉 A chess AI thinks several moves ahead to outsmart its opponent.

👉 A smart thermostat decides when to adjust the temperature based on your habits.

• Why it matters: This is where the agent’s intelligence shines, turning raw data into smart choices.

Taking Action (Action)

• What it does: The agent carries out its decisions by interacting with the environment through effectors or outputs.

• How it works: Actions can be physical (like moving) or digital (like sending a message).

• Examples:

👉 A robot vacuum moves to clean a dirty spot.

👉 A chatbot types a reply or speaks an answer.

• Why it matters: Actions are how the agent makes a real impact, turning decisions into results.

Learning From Experience (Learning)

• What it does: The agent improves over time by learning from its successes and failures.

• How it works: It updates its knowledge or decision-making process based on feedback or new data.

• Examples:

👉 A recommendation system gets better at suggesting movies you’ll like.

👉 A self-driving car learns to avoid potholes after hitting a few.

• Why it matters: Learning keeps the agent adaptable and effective as its environment changes.

Extra Features (Common But Not Always Required)

Some AI agents go beyond the basics with these additional components:

• Memory: Stores past experiences or interactions to inform future decisions.

• Example: A virtual assistant recalls your last conversation to provide better answers.

• Communication: Allows the agent to interact with humans or other agents.

• Example: A customer service bot chats with you to solve a problem.

How It All Works Together: Real-World Examples

Let’s see these parts in action with a couple of familiar AI agents:

Example 1: A Self-Driving Car

• Sensing: Uses cameras, radar, and GPS to “see” roads, traffic, and obstacles.
• Knowledge: Stores maps, traffic rules, and safety protocols.
• Thinking: Plans the fastest route while avoiding collisions and obeying laws.
• Acting: Steers the wheel, accelerates, or brakes as needed.
• Learning: Improves its driving by analyzing data from every trip.

Example 2: A Chatbot (Like Siri or Alexa)

• Sensing: Listens to your voice or reads your text input.
• Knowledge: Knows your calendar, contacts, and a vast database of facts.
• Thinking: Interprets your request and decides how to respond (e.g., “Set a reminder”).
• Acting: Speaks a reply, sends a text, or schedules an event.
• Learning: Fine-tunes its understanding of your voice or preferences over time.

Final Thoughts: Why Understanding These Matters

The anatomy of an AI agent—sensing, storing knowledge, thinking, acting, and learning—reveals how these systems digitally mimic human problem-solving. 

Whether you are curious about how your smart devices work or interested in building AI yourself, knowing these core parts shows what makes AI agents tick!