#7: The Fundamental building blocks of an Agentic AI system [4-min read]

Exploring #FrontierAISecurity via #GenerativeAI, #Cybersecurity, #AgenticAI.

Feb 06, 2025

"The rise of agentic AI presents a dual-edged sword: while it offers unprecedented efficiency and autonomy in task execution, it also raises significant concerns regarding ethical decision-making and control."

We often hear about Agentic AI as if it’s some distant, futuristic concept, but in reality, we’re already interacting with its earliest forms. Ever had an AI assistant predict what you needed before you even asked? Or noticed how automated customer support agents are getting eerily better at holding a conversation? That’s not just AI responding -- it’s AI thinking, reasoning and acting in structured ways.

AI is evolving rapidly, and agentic AI is at the forefront of this transformation. We’re witnessing a shift where AI systems are no longer just passive tools that respond to commands -- they are becoming active agents capable of autonomous decision-making, planning, and execution. This shift has profound implications, from revolutionizing industries to raising new challenges in safety, ethics, and governance. As we build increasingly powerful AI agents, it’s crucial to understand what makes them work, how to develop them responsibly, and what guardrails must be in place to ensure they operate within human-aligned boundaries. This post explores the fundamental building blocks of agentic AI -- its core components and how they interact to create intelligent, goal-driven systems.

At its core, an AI agent isn’t magic. It’s built on three fundamental building blocks:

[1] The Prompt: The task blueprint, defining what needs to be done.
[2] Memory: The agent’s knowledge and ability to retain past interactions.
[3] Tools: The mechanisms for taking action and executing tasks.

[1]. The Prompt: Your AI’s blueprint for action.

The prompt is the single most important input an agentic AI system receives. It’s the difference between getting a well-organized response or an incoherent one. A well-structured prompt:
✔ Defines the objective –> What is the AI supposed to accomplish?
✔ Breaks down the process –> Are there multiple steps involved?
✔ Assigns responsibilities –> Does the task require specialized AI subagents?

Think of it this way: if you give a GPS vague directions -- “Take me somewhere fun” -- you won’t get very far. But if you specify “Find me the best-rated café within a 5-mile radius”, the system has a clear, actionable goal.

That’s what happens inside an Agentic AI system -- the prompt structures the workflow and ensures all agents operate in sync to achieve a shared goal.

This also raises a crucial question: How much does the user’s ability to craft a good prompt shape AI effectiveness? The growing role of "prompt engineering" makes me wonder if our ability to talk to AI is now just as important as the AI itself.

[2]. Memory: What separates a Smart AI from a forgetful one.

Imagine if every time you interacted with an AI, it had zero memory of past conversations. Every email request, every task reminder -- gone, wiped clean. That’s a chatbot, not an agent.

Agentic AI systems store context, recall past interactions and refine their knowledge over time. Memory allows an AI agent to:
✔ Learn from past interactions –> Retaining previous inputs to improve accuracy.
✔ Build multi-step reasoning chains –> Understanding complex workflows beyond a single query.
✔ Establish contextual awareness –> Adapting responses based on the user’s history and needs.

Some AI agents rely on short-term memory, like remembering your last few messages. Others use long-term embeddings, where AI recalls semantic details from previous exchanges -- similar to how a personal assistant remembers your preferences and past requests.

This is also where things get interesting. As AI stores more memory, how do we ensure it doesn’t become overfitted to biases, misinterpret old requests, or leak sensitive information? A great AI memory is helpful, but only if it’s designed with data security and ethical safeguards in mind.

[3]. Tools: Giving AI the ability to act, not just respond.

Without tools, AI is just words on a screen. Tools give agentic AI the power to execute real-world actions.

✔ APIs –> Connecting AI agents to external apps, databases, or automation services.
✔ Executable Functions –> Allowing AI to trigger workflows, generate reports, or complete transactions.
✔ Data Pipelines –> Enabling agents to fetch, process and synthesize information autonomously.

Think of AI-powered assistants like Zapier, Notion AI or even GitHub Copilot. They’re not just responding -- they’re pulling from databases, integrating with systems, automating workflows and executing code.

Agentic AI takes this a step further, orchestrating multiple tools at once, across multiple agents. It’s not just recommending a restaurant. It’s also:
- Finding highly-rated spots nearby.
- Checking your calendar for availability.
- Making a reservation.
- Ordering your preferred meal in advance.

That’s what true autonomy in AI looks like.

So, how do these components work in a Single-Agent System?

Yes! There are single- and multi-agent AI systems. In a single-agent AI system, these components interact in a closed loop:

[a] The prompt defines the task –> The AI determines what needs to be done.
[b] Memory informs decision-making –> The AI recalls past interactions for better accuracy.
[c] Tools execute actions –> The AI connects with systems, completes tasks, or triggers workflows.

This cycle repeats and improves over time as the AI learns, refines and optimizes its performance.

Looking Ahead: Single-Agent systems in focus

Now that we’ve covered the fundamental building blocks of Agentic AI, the next step is understanding how they function in a single-agent system.

How does an individual AI agent work when acting alone?
How do they manage workflows, optimize efficiency and make real-time decisions without multi-agent collaboration?
What limitations do they face compared to multi-agent systems?

In the next post, we’ll break down how single-agent AI systems function in practice -- where they shine, where they struggle and how they’re already shaping industries today.

Why this matters: The future of Agentic AI in everyday life.

Agentic AI isn’t just for big tech labs -- it’s already shaping how we work, automate and interact with digital systems.

» In business, AI-powered assistants will handle end-to-end workflow automation, from drafting contracts to executing financial transactions.
» In healthcare, agentic AI could autonomously schedule doctor’s visits, recommend personalized treatments and coordinate insurance approvals.
» In personal life, AI will transition from simple task managers to full-fledged life organizers, handling your schedule, smart home and finances seamlessly.

Where is this all heading? To a future where AI isn’t just responding to us -- it’s acting for us.

Are we ready for that shift? How do we balance autonomy with oversight? These are some of the questions I will continue to explore.

Innovating with integrity,
@AIwithKT 🤖🧠

AIwithKT