Introduction to Agentic AI

You must be hearing the term "Agentic AI" a lot and wondering what it means. In this blog, I am going to explain:

1. What is Agentic AI
2. What Agentic AI is not
3. What problem it solves
4. Single-agent and multi-agent Agentic AI systems
5. Architecture of Agentic AI
6. Use cases

What is Agentic AI?

Think of agentic AI as one or more AI agents working toward a goal that requires task planning to achieve it.

To understand agentic AI, first we need to understand what an agent is. An agent is a software system that acts on behalf of a user to fulfil a task or goal.

What is the difference between task and goal? A task is a specific action, such as "summarize this article." A goal is a broader outcome, such as "prepare a publish-ready blog post on space programs." A goal may require many tasks.

An agentic system can include subagents. Agents and subagents are given a specific or specialized role to do a task or achieve a goal. Agentic AI systems can be designed in two common ways: single-agent systems and multi-agent systems. In a single-agent system, one agent plans and performs the work. In a multi-agent system, multiple specialized agents collaborate to achieve the goal.

Eg: an example of Multi Agent Agentic AI: Goal: prepare a publish-ready blog post on space programs. Multi agents we can have with specific or specialized role:

1. one agent researches articles on space programs
2. one agent will shortlist the potential topics
3. one agent will write a focused new article based on it
4. one agent reviews the draft and prepares it for publishing

So, here we have 4 multi agents with specific role and working towards the goal. Agents are the part of the AgenticAI but now the question should be that isn't what all AI tools are doing? Are they agentic AI?

Well, NO. We will get into this in detail in sometime but for now, a basic chatbot or AI tool usually responds to your query. Without tools, permissions, memory, and an execution loop, it cannot reliably accomplish a larger goal on your behalf. Eg: it can't independently research space programs, SEO, and publish blogs on your behalf. At high level Agentic AI flow is:

We will discuss the above design in detail in the Understanding Layers of Agentic AI section.

Workflows vs autonomous AI vs Agentic AI

Agentic AI is not the same as fully autonomous AI. Many systems ask for confirmation before important actions like sending emails, making purchases, or publishing content. This is called human-in-the-loop control.

Now, you know what Agentic AI is not. Agentic AI is not just a chatbot, not just automation, not always a multi-agent system, and not always fully autonomous.

Understanding Agentic AI

• Tools: Any capability the AI can call or use outside its own language model reasoning. It can be public, private, internal, or company-owned. Eg: searching the web, reading files, sending emails, calling an API, running code, etc. Let the agent act.
• Memory: The agent's ability to retain useful information across steps or conversations. Let the agent remember. There are 2 types of memory:
  1. Short-term: what the agent remembers during the current task, such as the user's goal, intermediate results, and previous tool outputs.
  2. Long-term: information stored for future use, such as user preferences, project details, names, or recurring instructions.
• Permission: Controls what the agent is allowed to access or do. This is for security and privacy. A safe agent will have different levels of permission:
  1. Read-only permission: It can look at information but not change anything.
  2. Draft permission: It can prepare an action, but the user must approve.
  3. Execute permission: It can perform the action directly.
• Execution loop: The repeated cycle the agent follows to complete a task. A typical loop looks like this:
  1. Understand the goal
  2. Plan the next step
  3. Choose a tool or action
  4. Execute the action
  5. Observe the result
  6. Decide what to do next
  7. Repeat until the goal is complete

What problem agentic AI solves

Most traditional AI tools are good at responding to a single prompt. For example, you ask a question, and the AI gives you an answer. This works well for simple tasks, but many real-world goals are not one-step tasks.

For example, "write a blog introduction" is a task. But "research a topic, find the best angle, write the blog, review it, optimize it for SEO, and prepare it for publishing" is a goal. This goal requires multiple steps, decisions, tools, and sometimes human approval. This is the problem Agentic AI solves.

Agentic AI helps AI systems work toward larger goals:

• by breaking them into smaller tasks
• planning the next steps
• using tools
• checking results, and
• adjusting the plan when needed.

It is useful when the path to the final outcome is not fixed in advance. Instead of only giving one response, an agentic system can continue working through a sequence of steps until the goal is completed or until it needs human input.

Agentic AI can be implemented as a single-agent system or a multi-agent system. In a single-agent system, one agent handles the planning and execution. In a multi-agent system, multiple specialized agents work together. For example, one agent may research articles, another may shortlist topics, another may write the article, and another may review it.

A multi-agent system can be powerful, but it is usually more complex than a single-agent system because it requires coordination between agents.

Architecture of Agentic AI

Now let's look at the core part of this blog: what does an Agentic AI system look like?

Agentic AI systems can be designed as single-agent systems or multi-agent systems. In this section, we will look at both architectures and understand the common building blocks behind them.

A single-agent system uses one agent to understand the goal, create a plan, use tools, observe results, and adjust its next steps.

A multi-agent system uses multiple specialized agents to work toward the same goal. Eg, one agent may handle research, another may write the draft, and another may review the output. Because multiple agents need to coordinate with each other, a multi-agent system is usually more complex than a single-agent system.

However, both single-agent and multi-agent systems share some common components, such as:

• Goal understanding
• Planning
• Tool use and action
• Observation
• Memory
• Knowledge access
• Monitoring
• Reliability and failure handling
• Governance and security

These components work together to help the system move from a user goal to a completed outcome.

This is how single-agent architecture will look:

In a single-agent architecture, one agent is responsible for most of the work. It receives the user goal, breaks it into tasks, decides which tools to use, observes the results, and continues until the goal is completed or human input is required.

This design is simpler and easier to manage, but it may become limited when the goal requires many specialized skills.

Now let's look at a multi-agent architecture.

In a multi-agent architecture, the work is divided between multiple specialized agents. A manager or orchestrator agent may coordinate the overall process and assign tasks to different agents.

Eg: in a blog-writing workflow, one agent may research the topic, another may create the outline, another may write the draft, and another may review it.

This design can be more powerful for complex goals, but it also introduces more complexity. The system needs to manage communication between agents, avoid duplicated work, handle failures, and decide which agent should act next.

Use cases

A simple mental model for when to use Agentic AI is:

Use Agentic AI when you have a larger goal where the path is not fixed in advance, and the AI needs to break the goal into tasks, create a plan, use tools, make decisions, and continue working until the goal is completed or human input is needed.

Agentic AI is useful when the work is:

• Multi-step
• Tool-based
• Decision-driven
• Dependent on intermediate results
• Too complex for a single prompt-response interaction

Eg:

• Incident Triage Agent — Goal: Find out why the production service is failing and suggest the next best action.
• Customer Support Agent — Goal: Resolve a customer issue or route it to the right team.
• Software Debugging Agent — Goal: Find the cause of a bug and suggest a fix.
• Content Creation and Publishing Agent — Goal: Create a publish-ready blog or social media campaign.
• Personal Productivity Agent — Goal: Help manage daily work and follow-ups.

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