Agentic-AI

Agentic AI is a type of artificial intelligence that doesn’t just follow instructions step by step, it can make decisions and take actions on its own to reach a goal. Instead of waiting for humans to guide every move, these AI agents use reasoning, planning, and memory to figure things out.

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Why Agentic AI Was Brought In

The motivation behind Agentic AI was simple: static models were too limited.

• A chatbot can answer “What’s the weather in Mumbai?” but it cannot open your calendar and reschedule your trip because of the rain.

• Businesses wanted AI that could not just “chat” but also do real work like analyzing data, connecting with APIs, and automating workflows.

This demand gave rise to Agentic AI frameworks where models could reason, use tools, and act like digital employees.

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We didn’t invent agentic AI because chatbots were boring. We invented it because the old way of using AI ask a question, get a sentence hit a ceiling the moment real work showed up. A travel bot could quote flight prices but couldn’t hold a budget line while juggling dates, loyalty points, and seat preferences. A support assistant could explain a refund policy but couldn’t actually file the refund and update the ticket. A dev copilot could write a function but stalled when it had to open an issue, run a test suite, and post the results to Slack. In each of these cases the problem wasn’t “intelligence,” it was agency. We didn’t need a better autocomplete; we needed software that can pursue goals, make choices, call tools, and carry the task to done safely.

What an “agent” really is

An agent is just software with a goal, a way to look at the world, a way to decide what to do next, and the ability to act. It keeps state, so it remembers what already happened; it has access to tools, so it can do more than talk; and it follows a policy often driven by a language model that plans and revises as new information arrives.

How Does Agentic AI Work?

Agentic AI usually follows a four-step process:

1. Perceive – The agent collects and processes data from different sources (databases, sensors, apps, or websites). It identifies important details, objects, or entities in its environment.

2. Reason – A large language model (LLM) works as the brain of the system. It understands tasks, plans solutions, and coordinates specialized tools like recommendation engines, image processors, or content generators. Techniques like retrieval-augmented generation (RAG) help it access the right data for more accurate answers.

3. Act – Once it has a plan, the AI agent takes action using APIs or external tools. Guardrails are often built in to keep it safe. For example, a customer service agent may automatically process claims under a certain limit, while larger claims still need human approval.

4. Learn – Agentic AI improves over time. Each interaction creates new data, which feeds back into the system (a data flywheel). This helps the agent learn from mistakes, get smarter, and become more efficient.

At the center is a language model acting as the policy brain. It doesn’t “know” everything; it knows how to choose what to do next. Around it you mount three things.

1. First, memory. Short-term state carries the conversation or task. Long-term memory lives in a vector database so the agent can retrieve relevant past interactions or domain documents on demand. This is the difference between “what’s your budget again?” and “I kept you under the ₹65,000 cap you set last time and reused your preferred airline.”

2. Second, tools. These are plain functions behind APIs: search inventory, get weather, price flights, run SQL, trigger a refund, create a Jira issue, launch a workflow in Zapier or UiPath, drive a headless browser with Playwright—whatever your domain uses to make things happen. The agent doesn’t reinvent these; it learns when and how to call them.

3. Third, guardrails. Because the agent can act, it needs a constitution. You enforce schema checks so outputs are machine-parsable, policy checks so actions stay within limits, and human-in-the-loop checkpoints where risk or cost cross a threshold. In finance that might mean “auto-approve refunds under ₹2,000; escalate above that.” In healthcare it might mean “never send a diagnosis without a clinician sign-off.”

Memory Systems in Agentic AI

A defining feature of agentic AI is persistent memory, which makes it different from traditional LLMs that forget past interactions.

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1. Short-Term Memory – Maintains context within a single task or session.

2. Long-Term Memory – Stores information across multiple sessions using vector databases such as Pinecone, Weaviate, or FAISS.

3. Episodic Memory – Records sequences of events to allow the agent to recall how a decision was made.

4. Procedural Memory – Stores step-by-step instructions on how to complete recurring tasks, such as refund processing.

When developers set out to build agentic AI, they usually bring together a mix of models, frameworks, memory systems, and execution layers. At the core sit the large language models GPT, Claude, Llama 3, or Mistral which act as the reasoning engines. These are what allow the agent to understand context, make decisions, and generate responses.

A simple agent that decides which tool to use

function weatherTool(city) {
  return `It's sunny in ${city} today.`;
}

function newsTool(topic) {
  return `Latest news about ${topic}: AI is trending!`;
}

function agent(input) {
  if (input.includes("weather")) {
    return weatherTool("Mumbai");
  } else if (input.includes("news")) {
    return newsTool("technology");
  } else {
    return "I don't know, but I can learn!";
  }
}

// Example usage
console.log(agent("Tell me the weather")); 
console.log(agent("Give me tech news"));

Agentic AI Tools You’ll Actually Use

Agentic AI frameworks make it easier to connect models, memory, and tools into a system that can reason and act. Here are the main ones:

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1. LangChain – Orchestrating LLMs and Tools

• Purpose: Build agents that can reason, plan, and execute tasks using LLMs.

• Key Features:

  1. Tool registration (APIs, calculators, etc.)

  2. Memory adapters (short-term and long-term memory)

  3. Chains & agent loops for reasoning

2. LangGraph – Visual Planning and Execution

• Purpose: Represent reasoning and tool calls as a graph, making it easy to debug complex agent plans.

• Use Case: Large workflows with multiple steps, dependencies, or specialists (researcher, planner, executor).

3. LlamaIndex / Haystack – Retrieval-Augmented Agents

• Purpose: Handle document-heavy tasks, like searching PDFs, wikis, or internal databases.

• Why it matters: Prevents hallucinations by quoting your source of truth.

• Example Use Case: A customer support agent retrieves FAQ answers from your knowledge base instead of guessing.

What an Agentic Workflow Is

An agentic workflow defines the step-by-step loop an agent follows to achieve a goal, combining tools, memory, reasoning, and execution. Think of it as the “how the agent moves from request → action → result” map.

The agentic workflow is the skeleton, and frameworks like LangChain, LangGraph, CrewAI are the muscles and nerves that make it function. Without a defined workflow, tools alone don’t produce reliable autonomous action.