Agentic AI

build autonomous ai systems with python, openai & langchain the ai revolution you're missing you've heard about chatgpt you've played with gpt 4 maybe you've even integrated openai's api into a project but here's what you haven't done built ai systems that actually do things autonomously agentic ai isn't just another chatbot it's ai that can plan multi step tasks without human guidance use tools and apis to accomplish goals reason through complex problems collaborate with other ai agents learn and adapt from experience make decisions in dynamic environments while you're still building chatbots, companies are deploying ai agents that handle customer service end to end, automate entire workflows, write and debug code autonomously, and make business decisions in real time the gap is widening fast the agentic ai market explosion numbers that demand attention the shift to agentic ai isn't hype—it's happening now, backed by explosive market data market growth (the big picture) the global agentic ai market stood at $5 2 7 5 billion in 2024 and is projected to reach $93 199 billion by 2032 2034, growing at a staggering 43 8 45 8% cagr to put this in perspective this market is growing 40+ times in less than a decade this isn't incremental growth—this is a fundamental shift in how businesses operate the enterprise agentic ai market specifically is expected to grow from $2 58 billion in 2024 to $24 5 billion by 2030, with businesses racing to deploy autonomous systems that can handle complex operations without human intervention enterprise adoption (who's investing) as of 2025, 45% of fortune 500 companies are actively piloting agentic systems, signaling massive enterprise trust in autonomous ai this isn't experimental anymore—it's strategic 79% of employees report their companies are already using ai agents, and 51% of organizations are exploring integration the wave isn't coming—it's already here microsoft's autogen framework is being used by 40% of fortune 100 firms to automate tasks in it, compliance, and operations if you're not building with these frameworks, you're falling behind the industry standard investment momentum (follow the money) over $9 7 billion has been poured into agentic ai startups since 2023 venture capital is flooding into this space because the roi is proven openai's revenue is on a trajectory from $12 7 billion in 2025 toward $125 billion by 2029—a 10x growth in 4 years, driven largely by agentic ai applications productivity gains (why companies are adopting) agentic ai systems can complete up to 12 times more complex tasks compared to traditional llms, thanks to dynamic feedback loops and autonomous decision making companies using ai agents see a 61% boost in employee efficiency, and 66% of senior executives report measurable productivity or business value from their agentic ai initiatives in software engineering, agentic ai enables 4x faster code debugging, fundamentally changing devops workflows siemens achieved 90% touchless processing across industrial workflows, realizing €5 million (usd 5 65 million) in annual savings by deploying multi agent systems the opportunity gap here's the critical insight despite strong intent, only 2% of organizations had deployed agentic ai at scale by 2025, while 61% were still in exploration phases translation the market is massive, adoption is accelerating, but skilled practitioners are scarce this is your window regional growth hotspots asia pacific is the fastest growing region for agentic ai, with india's ministry of electronics announcing a $1 2 billion ai mission focused on foundational models and enterprise ai integration chinese banks and insurers are deploying llm powered agents for automated claims and fraud detection, with major institutions like icbc and ping an piloting multi agent systems for indian developers this isn't just a global trend—your government is betting billions on it the jobs, the projects, and the opportunities are coming to india in a big way what this means for your career the agentic ai market isn't theoretical it's massive ($93 199b by 2032 2034) fast growing (43 45% cagr) enterprise validated (45% of fortune 500 piloting) productivity proven (12x task complexity, 61% efficiency boost) investment backed ($9 7b+ in funding) underserved (only 2% deployed at scale—huge skill gap) the developers who learn to build agentic ai systems now will be the architects and leads when this market hits $100b+ in the next 5 7 years what this 20 hour crash course covers this isn't a theoretical survey course this is intensive, hands on training designed to take you from "i've used chatgpt" to "i can build multi agent autonomous systems" in 20 hours course structure 8 modules + capstone project every module includes live instruction with expert explanations jupyter notebooks with step by step code hands on tasks you complete during the session real world examples and use cases module 1 introduction to agentic ai (2 hours) what you'll learn what agentic ai actually is (vs chatbots, vs traditional automation) the core concepts autonomy, reasoning, tool use, memory setting up your development environment (python, openai, langchain) making your first openai api call understanding agent architecture patterns hands on environment setup (python, jupyter, api keys) first openai api integration building a simple conversational agent outcome you'll understand the agentic ai landscape and have a working development environment ready to build module 2 llm fundamentals & prompt engineering (2 hours) what you'll learn how llms process prompts and generate responses prompt templates and variable injection few shot learning (teaching through examples) structured outputs (json, xml) for downstream processing system prompts vs user prompts temperature, tokens, and parameter tuning hands on writing effective prompts for different tasks creating reusable prompt templates implementing few shot learning patterns extracting structured data from unstructured text outcome you'll master prompt engineering—the foundation of all llm work you'll know how to get consistent, high quality outputs from language models module 3 building simple agents (2 hours) what you'll learn the agent loop observe → think → act langchain basics and core abstractions chains vs agents (when to use which) agent memory (keeping context across interactions) basic decision making logic hands on building your first langchain agent implementing the agent loop adding short term memory creating decision trees for task routing outcome you'll build working agents that can maintain context and make simple decisions autonomously module 4 tools, memory & reasoning (3 hours) what you'll learn what are tools in agentic ai (apis, functions, databases) giving agents access to tools memory stores short term vs long term react pattern (reasoning + acting) chain of thought prompting tool selection and execution hands on implementing tool calling agents building a calculator tool (structured function calling) adding persistent memory (conversation history) react pattern implementation creating agents that reason through multi step problems outcome your agents will be able to use tools, remember past interactions, and reason through complex tasks step by step module 5 multi agent systems (2 hours) what you'll learn why multiple agents? (specialization, collaboration, scale) agent collaboration patterns hierarchical vs peer to peer agent architectures inter agent communication protocols conflict resolution and consensus task delegation and coordination hands on building a two agent system (researcher + writer) implementing agent communication coordinating tasks across multiple agents handling agent failures gracefully outcome you'll understand how to architect systems where multiple specialized agents work together to solve complex problems module 6 integrating apis & external tools (3 hours) what you'll learn connecting agents to real world apis (rest, graphql) file handling (reading, writing, processing) database integration (sql, vector databases) web scraping for data collection api authentication and error handling rate limiting and retry logic hands on building agents that call external apis creating file processing agents implementing web scraping workflows connecting to databases for persistent storage building api wrappers as agent tools outcome your agents will be able to interact with real systems—apis, files, databases, and websites—making them production ready module 7 custom agent workflows & planning (3 hours) what you'll learn task decomposition (breaking complex goals into steps) planning algorithms for agents vector stores and semantic search rag (retrieval augmented generation) for knowledge grounding workflow orchestration error recovery and fallback strategies hands on implementing task decomposition building a planning agent (goal → subgoals → actions) setting up vector stores (pinecone, chroma, faiss) creating rag powered agents orchestrating complex multi step workflows outcome you'll build agents that can plan, break down complex tasks, and use external knowledge bases to ground their responses in facts module 8 capstone – multi agent chatroom (3 hours) the final project build a complete multi agent system where specialized agents collaborate in a chatroom environment what you'll build 1\ console based multi agent chatroom multiple specialized agents (researcher, writer, critic, coordinator) real time collaboration between agents shared memory and context task delegation and consensus 2\ streamlit web ui beautiful web interface for your multi agent system real time visualization of agent interactions user controls for system behavior production ready deployment features agent specialization (each agent has specific skills) inter agent communication task routing and delegation memory management across agents user interaction and control error handling and recovery outcome you'll have a complete, working multi agent system that you can demo, deploy, and expand this becomes your portfolio piece what you get complete learning package included in the course all jupyter notebooks 01 intro agentic ai ipynb 02 prompt engineering ipynb 03 simple agents ipynb 04 tools memory reasoning ipynb 05 multi agent systems ipynb 06 api integration ipynb 07 custom agent workflows ipynb 08 capstone multiagent chatroom console ipynb 08 capstone multiagent chatroom streamlit ipynb complete code all working examples from every module capstone project source code (console + streamlit) requirements txt with all dependencies setup guides and documentation step by step instructions every notebook has detailed explanations comments in code explaining the "why" behind decisions best practices and common pitfalls highlighted who should join this cohort you're a perfect fit if you the python developer ready to specialize you know python fundamentals you've used apis and libraries you want to specialize in the hottest ai domain you're targeting ai engineer, ml engineer, or ai product developer roles the data scientist/ml engineer pivoting to agentic ai you understand machine learning concepts you've worked with traditional ml models you want to move into the autonomous ai space you see the shift from predictive models to agentic systems the software engineer adding ai skills you're a solid developer (any language) you want to add cutting edge ai capabilities to your skill set you're comfortable learning a new domain quickly you want to be the "ai person" on your team the startup builder/entrepreneur you want to build ai powered products you need to understand what's possible with current ai you want to prototype agentic ai features fast you're betting on ai as a competitive advantage the career switcher betting on ai you're transitioning into tech from another field you want to enter at the cutting edge (not legacy technologies) you're willing to invest intensive effort for rapid skill acquisition you understand that ai is the future and want to be part of it you're not ready if ❌ you don't know python basics (variables, functions, loops) ❌ you've never used apis or libraries ❌ you want passive video watching (this requires hands on coding) ❌ you can't commit to intensive 20 hour learning ❌ you're looking for theoretical ai research (this is applied engineering) why this crash course format works traditional courses 40 60 hours spread over months, most students never finish this crash course 20 hours of intensive, focused learning where you build real systems from day one the science deep, immersive learning over a short period creates stronger neural pathways than scattered learning over months you'll retain more by doing 20 hours intensively than 60 hours casually the practical advantage in one intensive session, you go from zero to having a working multi agent system you're immediately productive, not waiting months to "someday finish the course " technology stack you'll master core technologies python (the language of ai) openai api (gpt 4, gpt 4o) langchain (the leading agentic ai framework) tools & libraries jupyter notebooks (interactive development) streamlit (web ui for demos) vector stores (pinecone, chroma, faiss) api integration (rest, authentication, error handling) concepts prompt engineering agent architectures multi agent systems rag (retrieval augmented generation) react pattern tool calling and function execution career outcomes what this opens up roles you'll be qualified for ai engineer / ai application developer salary range ₹15 35 lakhs (india), $100k 180k (us) build production ai applications integrate llms into products ml engineer (agentic ai specialist) salary range ₹20 40 lakhs (india), $120k 200k (us) design autonomous ai systems optimize agent performance ai product manager (technical) salary range ₹25 50 lakhs (india), $130k 220k (us) define ai product features bridge business and engineering founding engineer at ai startups equity + competitive salary build the product from scratch high risk, high reward companies hiring for these skills big tech google, microsoft, amazon, meta (all building agentic ai) ai first companies openai, anthropic, cohere, hugging face enterprise ai salesforce, servicenow, uipath startups hundreds of ai startups raising millions for agentic systems consulting deloitte, accenture (helping enterprises deploy ai agents) the opportunity window remember only 2% of organizations have deployed agentic ai at scale, but 45% of fortune 500 companies are actively piloting this is the moment when early adopters become the experts the developers learning agentic ai in 2025 will be the senior engineers, team leads, and architects when this market is 10 20x larger in 2030 setup & prerequisites before the course, you'll need technical requirements python 3 8+ installed openai api key (you'll get setup instructions) jupyter notebook or jupyterlab code editor (vs code recommended) knowledge prerequisites python basics variables, functions, loops, conditionals api concepts what rest apis are, how to make http requests command line familiarity running scripts, pip install don't have openai credits? we'll provide guidance on getting started with minimal cost most of the course can be completed with $10 20 in api credits what happens after you enroll immediately access to pre course materials and setup instructions openai api setup guide environment configuration checklist before the course complete the environment setup review python fundamentals (if needed) get your openai api key ready during the course (20 hours) follow along with live instruction complete hands on coding in each module build projects from scratch ask questions in real time collaborate with other learners after the course lifetime access to all notebooks and code complete capstone project to showcase portfolio piece for job applications understanding of agentic ai architecture frequently asked questions "i know python but haven't worked with ai can i take this?" yes! the course starts with fundamentals and builds up if you're comfortable with python (functions, classes, apis), you're ready we teach the ai concepts from scratch "will 20 hours really be enough to learn this?" yes, because this is intensive, focused learning you're not learning "everything about ai"—you're learning one specific, high value skill building agentic ai systems by keeping the scope focused and the pace intensive, 20 hours is highly effective "do i need a powerful gpu or expensive hardware?" no we're using openai's apis, which means the compute happens on their servers you just need a laptop that can run python and jupyter notebooks "how much will openai api costs be?" for this course, expect $10 20 in api costs we'll show you how to set spending limits and work cost effectively "is this course focused on theory or practical building?" 100% practical every module has hands on coding you'll build 8+ projects including a complete multi agent system theory is taught only as needed to understand what you're building "what if i can't keep up during the live sessions?" every session is recorded if you miss something, you can rewatch plus, all code is provided in notebooks, so you can review at your own pace afterward "will i get a certificate?" yes, you'll receive a certificate of completion but more importantly, you'll have a working multi agent system in your github portfolio—that's worth more than any certificate "can i use this to build my startup idea?" absolutely many students take this course specifically to prototype ai powered products the capstone project is designed to be extensible—you can adapt it for your use case the bottom line the agentic ai market is growing at 43 45% cagr fortune 500 companies are piloting systems billions are being invested productivity gains are proven but only 2% of organizations have deployed at scale that's your opportunity this 20 hour crash course takes you from "i've used chatgpt" to "i can build autonomous multi agent systems" faster than any other program you'll emerge with working knowledge of langchain and openai a complete multi agent system in your portfolio understanding of production agentic ai architecture the skills companies are desperately hiring for the developers who build agentic ai systems in 2025 will be the leads and architects when this market hits $100b+ in 2030 are you in? ready to build the future? questions before enrolling? email us at questpond\@questpond com we'll help you determine if this crash course is right for your current skill level and career goals the next session starts soon limited seats available