Fireblaze Self-Paced Course
Agentic AI: Build Autonomous AI Agents With Memory, Planning and Multi-Agent Systems
Fifteen lessons on the architecture behind agents that actually finish a task: tool calling, memory that persists, planning, self-correction, and multiple agents working under a supervisor. You finish with a graded multi-agent system, not a demo that works once.
- 15 lessons, from tool calling to deep research architecture
- 1 graded capstone: a working multi-agent research system
- Doubt-solving chat support for one month from first login, replies within 1 working day
- Covers the Model Context Protocol (MCP) and agentic RAG
- Certificate of completion, and access for as long as the platform runs
Self-paced and fully online. No batch timings, no scheduled classes.
Agentic AI is the practice of building systems where a language model plans, uses tools, remembers across steps and corrects itself, rather than answering a single prompt. This self-paced course teaches that architecture in 15 lessons: tool calling, short and long-term memory, planning and task decomposition, reflection and self-evaluation, self-healing RAG, multi-agent collaboration, supervisor architecture, the Model Context Protocol, autonomous retrieval and research agents. It is built for developers who have already used an LLM API and want systems that complete work unattended. The fee is ₹1,999 inclusive of 18% GST, and it includes one graded project, one month of doubt-solving chat support from first login with replies within 1 working day, a certificate of completion, and access for as long as the platform runs.
The Capstone
The Hard Part Is Not Making an Agent. It Is Making It Finish.
Almost any tutorial can get an agent to call one tool once. The failures start when a step returns something unexpected, the agent loops, or two agents disagree. That is what the project is about.
Graded capstone project
Build a multi-agent research assistant that plans, retrieves, checks its own work and reports
You build a system where several agents work under a supervisor to answer a research question that cannot be answered in one call. It has to handle a step going wrong without falling over.
What you build
- A supervisor agent that decomposes a goal into tasks
- Worker agents with defined tools and clear boundaries
- Short-term and long-term memory across the run
- Retrieval over your own documents, plus web search
- A reflection step where the system evaluates its own draft
- Failure handling for when a tool returns nothing useful
- A final report with the sources the system actually used
How it is graded
- Task completion: does it finish without human rescue
- Architecture: are agent roles and tool boundaries sensible
- Failure handling: what happens when a step fails or loops
- Reasoning: a short report on where it broke and what you changed
You receive written feedback on the submission. The failure handling carries real weight, because an agent that works only on a clean run is not a system anyone can deploy.
Curriculum
All 15 Lessons
Ordered the way agent systems are built: make one agent work, make it reliable, then let several of them cooperate.
Agent foundations
- Introduction to agentic AI: from AI assistants to autonomous agents
- Understanding AI agents and tool calling
- AI agents with memory: short-term and long-term memory
- Planning agents: task decomposition and goal-oriented reasoning
Making agents reliable
- Reflection agents: self-evaluation and iterative improvement
- Self-healing RAG systems
- Multi-agent AI systems: collaboration between agents
- Supervisor agent architecture
Connecting agents to the world
- AI agents vs traditional workflows
- Model Context Protocol (MCP) and its role in agentic AI
- Autonomous retrieval systems
- Hybrid web search and RAG systems
Research systems
- AI research agents: automating information gathering and analysis
- Deep research architecture
- The future of agentic AI: autonomous and collaborative intelligence
Honestly
Why Pay When Agent Demos Are Everywhere?
Agent content is abundant. Agent content that survives a failing tool call is not.
| Free demos and threads | This course | |
|---|---|---|
| Scope | One agent, one tool, one happy path | Multi-agent systems with a supervisor |
| Memory | Rarely beyond the current call | Short-term and long-term, across a run |
| When a step fails | Not covered | Reflection, self-healing and failure handling |
| Architecture | Implicit in the code | Taught explicitly, and graded |
| When you get stuck | Comments section, maybe | Doubt chat for a month, answered in 1 working day |
| Proof you did it | Nothing verifiable | Graded project plus a certificate |
The gap between an agent demo and an agent system is almost entirely about what happens on the unhappy path. That is the part free content tends to skip, and the part this course spends its time on.
Fit
Who This Is For
A good fit if you
- Write Python comfortably and have called an LLM API before
- Have built something with prompts or retrieval and hit its limits
- Want systems that complete multi-step work rather than answer single questions
- Are a developer or AI engineer moving toward applied AI roles
Not the right starting point if you
- Have not used an LLM API yet, where prompt engineering is the better first step
- Want a no-code agent builder rather than code you control
- Are looking for model training or deep learning theory
- Want live scheduled classes, which our career programs provide instead
Familiarity with retrieval-augmented generation helps, because self-healing RAG, autonomous retrieval and hybrid search all build on it. Our RAG course is the natural prerequisite if you have not worked with retrieval before. Lesson 10 covers the Model Context Protocol, which we also wrote about in this MCP tutorial.
Enrol
One Price, Everything Included
No subscription, no upsell at checkout, and no separate charge for the project review or the support window. The number below is the number you pay.
Payment is processed securely through Razorpay. You get access immediately after payment, and you can start the first lesson the same day.
Course Fee
₹1,999
Inclusive of 18% GST. No hidden charges.
- All 15 lessons, self-paced
- Graded capstone project with written feedback
- Doubt-solving chat support for one month from first login, replies within 1 working day
- Certificate of completion
- Access for as long as the platform runs
Questions first? Ask on WhatsApp
FAQ
Frequently Asked Questions
What is Agentic AI?
Agentic AI describes systems where a language model plans a goal, uses tools, keeps memory across steps and evaluates its own output, instead of returning one answer to one prompt. The model becomes part of a loop that can complete multi-step work with little supervision.
What is the difference between an AI agent and a normal LLM workflow?
A workflow runs fixed steps you defined in advance. An agent decides what to do next based on what it finds, which makes it more capable and less predictable. There is a dedicated lesson on when each is the right choice, because agents are often used where a workflow would be safer and cheaper.
What will I be able to build after this course?
A multi-agent system where a supervisor decomposes a goal, worker agents use tools and retrieval to carry out tasks, memory persists across the run, and a reflection step checks the output before it is returned. The capstone is exactly this, built over a research question.
Do I need to know RAG before taking this course?
It helps considerably. Self-healing RAG, autonomous retrieval and hybrid web search all assume you understand retrieval. If you have not worked with RAG, take our RAG course first; if you already build retrieval systems at work, you can start here.
What are the prerequisites?
Comfortable Python and at least one prior experience calling an LLM API. You do not need machine learning theory, model training experience or a GPU. This is an applied engineering course, not a research course.
Does the course cover MCP?
Yes, lesson 10 covers the Model Context Protocol and its role in agentic AI. MCP is becoming the standard way to connect models to external systems and tools, which makes it directly relevant to how agents reach the world.
What is a supervisor agent architecture?
A pattern where one agent coordinates several specialised worker agents rather than a single agent trying to do everything. The supervisor decomposes the goal, assigns tasks and assembles results. It is covered in lesson 8 and is a required part of the capstone project.
How much does the course cost?
Rs 1,999, inclusive of 18% GST. That covers all 15 lessons, the graded capstone project with written feedback, one month of doubt-solving chat support, a certificate of completion, and continued access. Nothing is added at checkout.
Do I get a certificate?
Yes. You receive a certificate of completion from Fireblaze AI School once you finish the course and submit the graded capstone. The multi-agent system you build is the more useful artifact in an interview, but the certificate is included.
How long do I have access?
There is no expiry date. You keep access for as long as we run the learning platform, so you can return to lessons later when you are building something similar at work.
How does the doubt support work?
You get a doubt-solving chat window for one month, counted from your first login rather than the date of purchase. Questions are answered within 1 working day. It covers course and project questions.
Is this course live or self-paced?
Fully self-paced and online. There are no batch timings and no scheduled classes. You start when you buy and work at your own speed.
Who is this course not suitable for?
People who have never called an LLM API, people wanting a no-code agent builder, and people looking for deep learning theory or model training. It also is not the right choice if you specifically want live instructor-led classes.
How do I pay, and when do I get access?
Payment is processed securely through Razorpay. Access is granted immediately after successful payment, so you can start the first lesson the same day.
Can I ask questions before buying?
Yes. Message us on WhatsApp using the link on this page and we will answer questions about prerequisites, the capstone or the support window before you enrol.