Agentic AI 101: Why It Matters and How It Actually Works
With it's perks and highlights in tow
Agentic AI
Agentic AI refers to AI systems that can autonomously plan, decide, and act to complete tasks or achieve goals—much like a human assistant. These models aren’t just reactive (responding to individual prompts); they’re proactive and goal-driven, capable of managing entire workflows with minimal human intervention
Think of the leap from a calculator to a virtual executive assistant that takes a business goal, breaks it down into subtasks, researches information, writes emails, schedules meetings, and loops you in only when needed. That’s agentic AI.
We’re now entering a phase where the focus is shifting from “smart models” to autonomous agents—AI that’s not just intelligent but actionable.
Agent
An AI agent is a software system powered by AI models that can perceive its environment, make decisions, and take actions to achieve specific goals. It’s like having a highly capable intern that works 24/7—one that can browse the web, write code, query databases, and more.
Agents typically include:
Memory: Keeps track of past actions and conversations.
Planning module: Breaks a goal into manageable steps.
Tools/Plugins: Lets the agent interact with APIs, apps, and services.
Reasoning engine: Decides the best next action based on current context.
Popular frameworks like Auto-GPT, LangChain, OpenAgents, and CrewAI make it easier to build and deploy these agents today.
How Does an AI Agent Work?
AI agents function like intelligent assistants that can act with some level of autonomy. Here's a breakdown of how they work:
Human or Another Agent (Oversight Layer)
The process typically begins with a human, or sometimes another AI agent, providing goals and tasks.
This entity plays the role of orchestrator, offering oversight and direction.
Environment (Sensing Layer)
The agent senses the environment, meaning it gathers data from the world around it—this could be via sensors, data streams, APIs, or user input.
System Prompt
A carefully crafted system prompt provides the AI with structure or initial instructions on how to behave, helping guide its outputs and decisions.
Memory
AI agents often need memory for continuity and learning:
Short-term memory: Used for immediate context (e.g., remembering the last few messages).
Long-term memory: Used for persistent information like user preferences, previous tasks, and stored knowledge (can be vector-based, SQL, file-based, etc.).
Agentic Loop (or Thought-Action-Observation Loop)
This is the core process behind how agents operate in the loop:
Thought: Decide what to do next.
Action: Call a tool or API, generate content, or retrieve data.
Observation: Process the result and update memory/context.
Repeat until the task is done or handed off to a human.
It’s a loop of self-reflection, execution, and iteration—enabling autonomy far beyond simple prompting.
Multi-Agent Systems
These are setups where multiple agents with different capabilities work together—sometimes even negotiating or delegating among themselves—to solve complex tasks. For example:
One agent writes code
Another tests it
A third documents it
A fourth handles deployment
Systems like CrewAI, OpenDevin, and Autogen by Microsoft are pushing the boundaries of coordination and specialization across agents.
Task Execution vs. Tool Use
A key trait of agentic AI is tool use. Instead of relying solely on internal knowledge, agents can:
Access real-time web data (RAG)
Run Python code
Trigger webhooks
Query databases
Use third-party APIs (e.g. Salesforce, Slack, GitHub)
This ability to interact with tools in an orchestrated, multi-step way gives agents practical utility in real-world environments—something traditional chat models can’t easily match.
Planning vs. Prompting
Traditional LLM use is prompt-based: You ask a question, get an answer.
Agents are plan-based: You assign a goal, and they figure out how to get there—often across multiple steps, tools, or even hours.
Prompt: “Give me a list of 10 blog topics.”
Agentic plan: “Create a full content calendar for Q3, including keyword research, topic clustering, draft assignments, and deadline tracking.”
Key Agentic Components
Planner: Builds a structured strategy or to-do list.
Executor: Carries out the plan step-by-step.
Memory: Tracks context over time.
Tool interface: Executes actions beyond pure text generation.
Critic or Evaluator (optional): Reviews agent outputs to improve quality.
Guardrails: Ensure safety, limits, and oversight.
Frameworks like LangGraph (from LangChain) let you compose agents using these modular pieces.
Limitations & Risks
Agentic systems are powerful, but not without challenges:
Unpredictability: Agents can get stuck in loops or go off-task.
Latency: Multi-step reasoning can be slow.
Safety: Autonomy introduces risks of unintended actions.
Debugging complexity: It's harder to trace what went wrong in multi-step workflows.
That's why leading platforms often build supervisory agents or human-in-the-loop systems for oversight.
AI Agents vs. Agentic AI
This section clears up the confusion around the evolving terminology of Agentic AI vs AI agents
Nov 2024 View:
“Your non-autonomous workflows aren’t agentic. Stop calling them AI agents 1.0.”
By this stage, it was acknowledged that early AI agents were too static or brittle—they couldn't truly adapt or act independently.
The critique here is that if workflows are too rigid or human-dependent, they shouldn’t be considered agentic.
June 2025 View:
“Those are AI agents, not agentic AI. Only agentic AI can be fully agentic.”
A more nuanced take: just calling something an AI agent isn’t enough—it has to exhibit true autonomy and adaptability to qualify as Agentic AI.
Takeaway:
“Don’t waste time on academic debates. What matters to users is actual behavior.”
The key insight here is user experience matters more than labels.
If an AI system feels autonomous and helpful, it's useful—regardless of its academic classification.
Multi-Agent Systems
This refers to systems where multiple AI agents interact with each other, either cooperatively or independently, without a central controller.
These are important for complex workflows or tasks requiring specialization.
Claude Multi-Agent Research System
An example of a practical multi-agent system:
Claude Desktop runs multiple specialized subagents:
Lead agent (acts as orchestrator).
Citations subagent (verifies or fetches sources).
Search subagents (retrieve specific data).
Memory (holds context and history).
Generative AI vs. Agentic AI
Generative AI
Reactive: Responds to prompts; doesn’t act on its own.
Creates content: Text, images, code, music, etc.
Single-step tasks: One prompt = one output.
No memory or planning: Doesn’t retain context beyond the session (unless specifically designed).
Tool use is manual: Requires the user or a developer to integrate it with tools.
Example: “Write me a blog post on AI ethics.”
Agentic AI
Proactive: Takes goals and works toward them autonomously.
Plans and reasons: Breaks tasks into steps, makes decisions.
Multi-step execution: Works iteratively, monitors progress.
Uses memory: Can recall past actions, preferences, or context.
Uses tools independently: Accesses APIs, apps, and files on its own.
Example: “Plan and launch an email campaign for my top customers.”
Use Cases
Agentic AI is reshaping workflows across industries:
Coding: Agents like Devin, SWE-Agent, and Codey complete dev tasks end-to-end.
Customer support: Automates entire support tickets with escalation logic.
Sales & marketing: Agents build lead lists, send personalized emails, and follow up.
Research: Autonomous literature reviews or competitive analysis.
Operations: Workflow agents that manage invoices, documents, onboarding, etc.
Enterprise-focused platforms like Adept, Fixie, Cognosys, and Orby are building agentic infrastructure for the workplace.
Agent vs. Assistant
There’s often confusion here:
Assistant: Responds helpfully to you.
Agent: Acts on your behalf—without needing to be prompted every time.
You don’t manage an agent like a chatbot. You delegate and let it run.
What Makes an Agent Actually Work?
Reliable planning and execution
Proper access to tools and memory
Context-aware reasoning
Tight integration with workflows
Continuous evaluation and improvement (via evals)
What is Agentic AI RAG?
Agentic AI RAG is a powerful evolution of traditional RAG (Retrieval-Augmented Generation). While basic RAG enhances LLMs with factual grounding from external documents, Agentic RAG gives that process autonomy, memory, tools, and decision-making capacity.
Traditional RAG
LLM gets a query.
Searches a vector database for relevant chunks.
Uses the results to generate a grounded response.
Agentic RAG
Adds agency to the RAG pipeline:
The agent decides how to search, what to search, and when to refine or re-query.
Can chain reasoning across multiple sources.
Uses memory, tools, and subagents (e.g., search, summarizer, critic).
Can interact with structured + unstructured data.
Example Workflow of Agentic RAG:
User asks: “Summarize key AI agent launches in the past 6 months.”
Agent:
Searches multiple sources (news, GitHub, reports).
Filters by date.
Summarizes across tools and formats.
Decides to include visuals or citations.
Updates long-term memory for future follow-up.
Agentic AI represents the next frontier in artificial intelligence—where models aren’t just generating content, but actively reasoning, planning, and taking action with autonomy. When combined with RAG, it evolves into a powerful system that’s not only grounded in real data but also capable of managing complex tasks end-to-end. As tools, memory, and agent frameworks mature, Agentic AI is set to transform how we build assistants, automate workflows, and interact with intelligent systems.