![AGENTIC AI Aaryan
calendar and emails, schedules meetings, and
follows up on tasks. It’s still not fully autonomous,
but far more capable than a simple chatbot.
• Agentic AI (Level 2): Fully autonomous multi-
step problem-solvers. These “AI systems [are]
designed to autonomously make decisions and
act, with the ability to pursue complex goals with
limited supervision” . Agents within an agentic
system can split sub-tasks, share memory, and
even adapt their own strategy. For instance, an
agentic AI might take a high-level goal like
“launch marketing campaign,” break it into
researching demographics, drafting emails, and
scheduling ad buys – all without
micromanagement. As IBM notes, agentic AI
“takes autonomous capabilities to the next level
by using… LLMs, machine learning, and NLP to
perform autonomous tasks on behalf of the
user” , focusing on decisions rather than mere
content creation .
5](https://image.slidesharecdn.com/agenticai-250528081506-61c0e0e4/85/Agentic-AI-Explained-The-Next-Frontier-of-Autonomous-Intelligence-Generative-AI-pdf-5-320.jpg)
Agentic AI Explained: The Next Frontier of Autonomous Intelligence & Generative AI.pdf
- 1. AGENTIC AI Aaryan AGENTIC AI Agentic AI: The Next Frontier in Autonomous Intelligence 1
- 2. AGENTIC AI Aaryan Imagine you built your startup prototype by day’s end, while your AI intern handled the grunt work researching competitors, drafting emails, fixing bugs all without constant hand-holding. That’s the promise of Agentic AI, the next stage beyond today’s ChatGPT-like systems. If you’ve been marveling at GPT writing poems or generating images, buckle up: agentic AI takes that foundation and adds true autonomy. These systems “reason, plan, execute and adapt” on their own . In other words, they’re not just fancy autocomplete – they’re digital co-workers. Today’s generative AI (think ChatGPT or DALL·E) is reactive: it waits for your prompt and then crafts content . Agentic AI is proactive: it can set its own sequence of actions to achieve human-defined goals . Major firms see this as a game changer Deloitte predicts that by 2027 half of companies using GenAI will have agentic AI pilots running . The tools and frameworks (like LangChain, Microsoft’s AutoGen, LangGraph, CrewAI, etc.) are emerging now, ready for savvy engineers to compose. This ebook unpacks all that: what generative AI and agents are, how “levels” of AI stack up, the guts of building agentic systems, and why developers must care. 2
- 3. AGENTIC AI Aaryan Breakdown: From Generative AI → AI Agents → Agentic AI Before diving into tools or code, let’s lay the groundwork. AI today can be thought of in levels of autonomy: • Generative AI (Level 0): Systems that generate content from a prompt. For example, ChatGPT writing you an essay or Midjourney drawing a landscape. They “create original content… in response to a user’s prompt” . They rely on human instruction and massive training data, but 3
- 4. AGENTIC AI Aaryan don’t take initiative or maintain long-term memory on their own. • AI Agents (Level 1): Systems that can act in an environment and plan step-by-step, but still need human goals and checkpoints. Think of an agent as a “software program that can interact with its environment, collect data, and use the data to perform self-determined tasks to meet predetermined goals” . Unlike pure GenAI, agents carry context, use tools (APIs, databases), and can loop through steps: “action → observe → decide → act” until done. For example, a virtual assistant that reads your 4
- 5. AGENTIC AI Aaryan calendar and emails, schedules meetings, and follows up on tasks. It’s still not fully autonomous, but far more capable than a simple chatbot. • Agentic AI (Level 2): Fully autonomous multi- step problem-solvers. These “AI systems [are] designed to autonomously make decisions and act, with the ability to pursue complex goals with limited supervision” . Agents within an agentic system can split sub-tasks, share memory, and even adapt their own strategy. For instance, an agentic AI might take a high-level goal like “launch marketing campaign,” break it into researching demographics, drafting emails, and scheduling ad buys – all without micromanagement. As IBM notes, agentic AI “takes autonomous capabilities to the next level by using… LLMs, machine learning, and NLP to perform autonomous tasks on behalf of the user” , focusing on decisions rather than mere content creation . 5
- 6. AGENTIC AI Aaryan In short, we’ve leveled up: Level 0 generates content, Level 1 acts with some oversight, and Level 2 owns a goal end-to-end. Next we’ll explore each level in detail. Level 0: Generative AI Generative AI refers to models that can produce new content text, images, audio, code from a prompt. We already live in this world: examples include ChatGPT (text), Stable Diffusion (images), and music generators. These systems use large language models (LLMs) or similar neural networks to “identify and encode patterns” from massive data and then synthesize new outputs . Key points: • User-driven content creation: You give a prompt (“Explain quantum computing”) and the AI creates. Generative AI is reactive it needs you to kick it off. IBM defines it as AI that “can create original content… in response to a user’s prompt” . • Examples everywhere: Writing blog drafts, summarizing reports, designing marketing visuals, composing emails, or generating code snippets. Microsoft’s GitHub Copilot is essentially 6
- 7. AGENTIC AI Aaryan a Generative AI agent for coding it writes and refactors code when you prompt it. • Strengths & Limitations: These tools excel at creativity and ideation. They can draft a product description or mock up a logo in seconds, freeing you from mundane content work. But limitations include consistency and follow-through. A generative model won’t, for example, remember details across sessions or autonomously take multiple steps unless you keep prompting it. It won’t, on its own, google facts, access your databases, or decide to switch context unless engineered to do so. In practice, think of Generative AI as your extremely clever, but slightly hyperactive, assistant. It does exactly what you ask (provided your prompts are clear), and sometimes more but it won’t proceed without you. For example, ChatGPT can debate insurance options or write a cover letter, but after it finishes it waits. It won’t say “Hey, I noticed you mentioned insurance let me re-run a query with the latest data I remember.” That’s where agents come in next. Level 1: AI Agents Enter AI Agents. If Generative AI is a “tool,” AI Agents are like “mini-robots” wielding that tool they carry memory, they interface with external systems, and they 7
- 8. AGENTIC AI Aaryan can plan a bit. Per AWS’s definition, “an AI agent is a software program that can interact with its environment, collect data, and use the data to perform self- determined tasks to meet predetermined goals.” The human still sets the goal (“Book me a flight to Berlin”), but the agent decides how to achieve it by calling flights API, emailing confirmations, etc. How are Agents di ff erent from plain Generative AI? • Perception & Action Loop: Agents follow a loop: observe (read input or environment), think (plan next action), act (execute a step, e.g. call a tool), then repeat. They leverage generative models for parts of this reasoning, but also use external tools. For example, an agent might use a GPT model to interpret a natural-language query, then call a Python function or search the web, then feed the result back into the model, and so on. • Memory & State: Unlike one-off prompts, agents often have memory or context chains. They can recall past steps or information. Microsoft research notes agents gain autonomy by using memory, entitlements and tools . In plain English: they remember ongoing tasks instead of treating each question in isolation. 8
- 9. AGENTIC AI Aaryan • Tool Use: One defining trait: tool integration. Agents can invoke other systems. Want an agent to summarize recent tweets about your product? It might call the Twitter API, ingest results, then use the language model to summarize. This tool- use loop is a key step toward autonomy. • Examples: A simple agent could be a home assistant: “Adjust thermostat to 70°F by calling the smart thermostat’s API” when a certain schedule or command is received. A more complex one is Microsoft’s Copilot in Microsoft 365: it drafts your email, populates meeting invites, etc., based on your calendar and documents. These use cases go beyond single prompts. IBM draws a helpful distinction: “agentic AI is the framework; AI agents are the building blocks within the framework.” . In our terms, an AI Agent is one autonomous character (like the thermostat agent, or a scheduling agent), whereas Agentic AI might orchestrate many agents for a larger goal. What can Level-1 agents do? • Perform multi-step tasks given user direction: e.g. “Prepare my monthly report.” The agent queries databases, generates charts, formats slides, etc. 9
- 10. AGENTIC AI Aaryan Each step might involve different tools or model calls. • Maintain some continuity: The agent can remember what it did earlier in the workflow and use it later (conversation memory, RAG databases, etc.). • Handle simple exception logic: If tool A fails, try tool B; if answer is uncertain, ask the user for clarification. • Automate routine work within apps: e.g. email triage, ticket routing, basic “if-this-then-that” tasks that currently require human back-and-forth. However, Level 1 Agents typically still wait for human input on major decisions. They’re not deciding goals by themselves. Think of them as highly sophisticated helpers: they can often out-ask you “anything else?” and carry some baggage (remembering your name), but they expect you to steer the ship. Level 2: Agentic AI Now we reach Agentic AI proper fully autonomous problem-solvers. Ok so here's, the human typically provides a goal or objective, and the AI system figures out the how itself, coordinating potentially multiple agents. It “puts the ‘agency’ in agentic AI.” These 10
- 11. AGENTIC AI Aaryan systems can break a complex goal into parts and execute them with little oversight . Key characteristics: • Goal-Driven Autonomy: You say “Solve problem X,” and the system autonomously plans steps, executes them (often in sequence or parallel), and adapts as needed. The worker bees (sub-agents) do the dirty work with minimal prompting. • Multi-Agent Coordination: Often, multiple specialized agents work together. For example, in a smart-home scenario, one agent manages lighting, another optimizes heating, and a manager agent juggles them to maximize comfort and savings . • Evolving Strategy: Agentic AI can adjust mid- course. If an option gets blocked (say an API is down), the agent figures an alternate route or asks questions. It’s “proactive” rather than reactive . • Memory and Learning: Some agentic systems incorporate feedback or past experiences to improve over time (reinforcement learning or simple logging). They might refine their plan the next day based on what worked yesterday. 11
- 12. AGENTIC AI Aaryan Examples: • Autonomous Software Engineer: Deloitte cites Cognition Software’s “Devin,” an AI that can take a high-level project idea and then autonomously design, code, test, and deploy a software application with minimal human supervision . This goes far beyond ChatGPT’s code suggestions; Devin is a whole pipeline engine. 12
- 13. AGENTIC AI Aaryan • Enterprise Workflows: Imagine telling an AI, “Set up my new hire’s workstation.” An agentic system could order hardware, install required software by itself, schedule onboarding sessions, and update HR systems – all without a human manually doing each step. • Research assistants: For example, instructing “Find the latest competitors in electric vehicles,” and the agentic AI scours news, compiles profiles, summarizes findings, and may even schedule meetings with sales reps. Why it matters: Agentic AI can cross-app boundaries. It’s not just a chatbot – it’s a diligent intern/assistant that breaks tasks into steps. Moveworks points out these agents “reason, plan, execute, and adapt,” enabling workflows traditional tools couldn’t handle . That means tasks involving judgment, multi-step logic, or human-like initiative become automatable. Deloitte predicts agentic systems will significantly boost productivity and even begin rolling out in businesses by 2025 . Put bluntly: if Generative AI is GPT writing a draft email for you, Agentic AI is GPT that writes it, sends it, follows up on replies, and updates your CRM without asking you every minute. 13
- 14. AGENTIC AI Aaryan How Is This Even Built? You’re thinking: “Cool story, but how do I actually make one of these?” Thankfully, there’s an emerging ecosystem of frameworks that glue together LLMs, tools, and orchestration. Here are some key players: • LangChain (Agents & Tools): Think of LangChain as the Swiss Army knife for LLM apps. It’s an open-source framework that connects language models with tools, APIs, and data sources . LangChain lets you define “chains” where the LLM can call external functions (like search_web()) and feed the results back. Its Agent module lets developers create loops of observation-action-thought until a goal is met . In short, LangChain simplifies building multi-step LLM workflows with features like memory buffers, toolkits (search, database lookup, calculators, custom code), and more. • LangGraph: A spin-off by the LangChain folks, LangGraph treats your multi-step process as a directed graph. You define nodes (agents, tools, LLM calls) and edges (flow control). LangGraph adds strong state management, debugging, and visual editing (with LangGraph Studio). It’s designed for “when you need control” over the exact flow . For example, if your application has 14
- 15. AGENTIC AI Aaryan branches (“if user answers A, do this; if B, do that”), LangGraph’s graph structure keeps it organized. According to LangChain docs: “LangGraph is a framework… to manage the control flow of applications that integrate an LLM.” • AutoGen (Microsoft): An open-source framework specifically for agentic AI, developed by Microsoft Research . AutoGen excels at multi- agent systems where agents can produce, fix, and run code (it even runs agents in Docker containers). It features asynchronous messaging, observability, and is language- agnostic (Python, .NET). The team says AutoGen “provides an easy-to-use and flexible framework for accelerating development… on agentic AI” . It includes built-in patterns like Manager/Worker agents and tools for debugging interaction flows. If your problem is complex and code-heavy, AutoGen is tailored for LLMs-as-developers scenarios. • CrewAI: A community-driven Python framework for multi-agent workflows. CrewAI bills itself as a “lean, lightning-fast Python framework built entirely from scratch” (no LangChain underpinnings) . It emphasizes simplicity: you assign roles to agents (like “researcher,” “writer,” “checker”) and define how they collaborate. CrewAI was even the basis for an Andrew Ng 15
- 16. AGENTIC AI Aaryan course on multi-agent systems. It offers high- level abstractions (good for novices) and still lets you tune low-level behavior. Its GitHub intro touts “high-level simplicity and precise low-level control,” ideal for autonomous agents . In practice, CrewAI is great for building pipelines where each agent has a clear task (collect data, analyze, summarize, etc.). • OpenAI Swarm (experimental): OpenAI’s new kid on the block. It’s an ultra-lightweight agent framework for prototyping. Currently marked “experimental,” it’s simpler than the above but shows the trend that even OpenAI sees the need for agent scaffolding. (It’s suitable for quick tests, but not yet production-ready according to its maintainers.) • Other Tools: Depending on your use case, you might also use: LLamaIndex for knowledge retrieval, Haystack for search, custom APIs, Docker for isolation, database backends for memory, etc. The common thread is: you’re orchestrating LLM calls and external tools in code, and these frameworks make it manageable. In practice: You’d pick a framework and write something like: define an overall objective, then spin up agents (or define steps) that use LLM prompts plus tool calls. For example, with LangChain you might 16
- 17. AGENTIC AI Aaryan create an agent that, given “Find sales leads,” repeatedly queries a search API and stores results. With AutoGen, you might define a manager agent that assigns “researcher” and “outreacher” roles. Each toolset has tutorials many promise “hello world” multi- agent demos but the concept is consistent: code the plan, let the agents chatter through LLMs, and supervise through observability logs. Why Should You Care? Real- World Use Cases If you’ve stuck with me this far, you’re probably wondering: “Okay smarty-pants, why does this matter for my startup, my projects, my team?” Fair question. Here are some real-world scenarios where agentic AI makes a difference: • Startup Operations (Small Biz Automation): Launching a startup means juggling everything from data analysis to marketing and ops. An agentic system can take mundane tasks off your plate. For example, an agent could scan meeting notes and automatically update your roadmap in JIRA, or analyze weekly sales figures and produce a concise report. The big gain is speed: instead of spending hours on grunt work (email follow-ups, spreadsheets, content publishing), you delegate to agents. In fact, one founder 17
- 18. AGENTIC AI Aaryan demonstrated building a basic app prototype in minutes using AI-driven tools . This shows how agentic tools can accelerate prototyping and iteration. (Caveat: “MVP” vs “prototype” aside, it highlights the trend: AI can bootstrap your MVP faster than coding from scratch .) • Sales & Outreach: Imagine you have a list of leads or a target profile. An agentic AI could: research each lead on LinkedIn, draft personalized intro emails, track replies, and schedule calls – all on its own. This is more than an email template; it’s a personalized campaign manager. As one example illustrates, AI agents can monitor customer engagement, segment users, score leads, and even route them to sales reps . The result is tailored follow-ups at scale. In B2B or e-commerce, such agents can automatically qualify leads based on behavior patterns and push high-priority prospects to humans. Basically, no more copy-pasting outreach or manually CRM-updates – the agent handles it continuously. • Customer Support: Chatbots today handle scripted Q&A, but agentic AI can manage end-to- end support flows. For instance, a customer reports a problem; the agent retrieves the account data, tries basic troubleshooting steps, escalates to human engineers if needed, and then follows up with the customer after a fix. 18
- 19. AGENTIC AI Aaryan Moveworks and others note that agentic systems can “predictively assess a situation” and resolve issues with minimal human touch . Already, companies use agents to handle password resets, order tracking, or simple tech support. The key is 24/7 consistency and freeing human agents for truly complex issues. As Codewave explains, AI agents now give “personalized and instant support” around the clock, boosting satisfaction by reducing wait times . • Market Research: Traditionally a slow, grunt- data process; agentic AI revamps it. An AI agent can ingest social media, news, and industry reports to spot trends or emerging competitors. Relevance AI describes these agents as having “a team of superhuman analysts working 24/7” – crunching terabytes in minutes and finding hidden patterns humans might miss . For a startup or product team, this could mean automatically updated dashboards of market sentiment, or daily summaries of competitor moves. Importantly, it democratizes insight: even small teams can harness agentic “market analysts” that only big firms used to afford . The agents handle data cleaning and initial insight, leaving strategists to make big calls. • MVP/Product Building: Beyond prototype- building, agentic AI can co-pilot feature development. Suppose you have a product idea: 19
- 20. AGENTIC AI Aaryan “build me a landing page and blog about eco- friendly gadgets.” An agent could outline content, generate copy, even create basic layouts via website-builder APIs. The LinkedIn example showed AI taking a step-by-step description and spinning out a demo website in minutes . It’s not perfect (e.g. “real” engineers still handle security/ auth), but as a rapid turnaround tool, it’s invaluable. You can iterate business ideas faster: test concepts with sketch apps or chat-driven prototypes. The bonus is that agents can learn from feedback – you ask “No, use green and blue theme,” and it iterates the design on the fly. • Administrative & Workflow Automation: Agentic AI shines on cross-app workflows. Need to onboard a new employee? An agent could email HR forms, set up accounts, enroll them in training, and even order hardware. For product management, an agent might triage incoming bug reports, prioritize them, and assign to devs. Essentially, any multistep policy-driven process benefits. Moveworks emphasizes that agentic AI can autonomously manage approval flows or IT tickets across systems . This is more powerful than Zapier-type automation because the agent actually decides which actions to take next, rather than following a rigid script. These use cases share a theme: scale and autonomy. Agentic AI converts a one-person 20
- 21. AGENTIC AI Aaryan brainstorm into a multi-agent execution. It doesn’t tire or forget, so you effectively get a “team” of digital workers. As one survey noted, executives expect agentic AI to deliver faster ROI than pure GenAI, with many anticipating over 100% returns . For computer science students and developers, it means new opportunities: building and deploying these systems can become a competitive edge or even a startup idea in itself. Conclusion 21
- 22. AGENTIC AI Aaryan The AI world is evolving fast. Just when you think you’ve mastered prompt engineering, the industry shifts to orchestration and autonomy. Agentic AI represents that leap: it’s generative AI plus self-direction. In it, LLMs are no longer static text generators but active problem-solvers. For developers, this means learning to think not just in terms of model outputs, but workflows and goals. The good news: the building blocks are here (LLMs, APIs, frameworks) and maturing. The challenge: designing agents that are reliable, safe, and actually helpful (remember, agents can still hallucinate or go astray if not properly constrained). In practical terms, start small. Tinker with an LLM agent: give it a goal, hook up a simple tool (web search, your calendar), and see how it reasons through steps. Frameworks like LangChain or AutoGen will handle the plumbing. From there, expand: add memory, add more agents. Use-cases are limited only by imagination from drafting business plans to playing dungeon master in a text-based game. Above all, stay pragmatic. Agentic AI is powerful, but it’s not magic sauce. Keep humans in the loop for final decisions, audit logs regularly, and iteratively refine. With the right approach, though, you might find yourself at the helm of the very AI army you once imagined agents humbly serving your every calculated whim. 22
- 23. AGENTIC AI Aaryan Sources: Authoritative industry analyses and documentation (IBM , Deloitte , AWS , etc.), plus expert-written guides and examples . These provide the backbone for our definitions and use-case insights. (Citations are embedded inline.) 23