Systems ship. Demos don't.
Build one production-style Agentic AI system end to end — with reliable orchestration, tool and MCP governance, production RAG, persistent memory, asynchronous execution, evaluation, observability, security and cloud deployment. Across the 6-week core program you engineer the system layer by layer; the 2-week Advanced Production Mastery Bonus helps you harden it, review the architecture, and explain it like a senior engineer.
This is not an introduction to Agentic AI. Completion of the Agentic AI Developer Bootcamp — or equivalent hands-on Agentic AI experience — is recommended.
Next cohort's 1st class starts Sunday, 20 September 2026. Live sessions run Saturdays & Sundays · 8:30 PM – 11:30 PM IST. Onboarding details are emailed after you enroll.
Join at $649 — standard price $699.
Your access includes the upcoming live cohort, recordings, and a premium included benefit: attend any 2 future cohorts free — start early, or revise after your first production build.
✓ Live sessions Saturdays & Sundays · 8:30 PM – 11:30 PM IST · Next Cohort · 1st class Sun 20 Sep 2026 · Current cohort pricing. Future cohorts may be revised as the program expands.
This is not a bundle of disconnected weekly builds. You build one production-style Agentic AI system layer by layer across the 6-week core program, then harden, review, and explain it like a senior engineer across the 2-week Advanced Production Mastery Bonus. Next cohort's 1st class starts Sunday, 20 September 2026. Onboarding details are emailed after you enroll.
3 minutes from Nachiketh on what this program is, who it's for, and why it works.
The gap between building a demo and owning production isn't skill — it's architecture thinking.
Jupyter notebooks that break at scale. Tutorials that stop at "hello world". No understanding of what happens when real users hit your agent at 3 AM.
Latency spikes, runaway token costs, hallucinations in production — without the right observability and guardrails, you can't debug what you can't see.
Everyone can follow a tutorial. Senior engineers know why one design outperforms another under real constraints — and can articulate those tradeoffs clearly to the people around them.
Developer Bootcamp teaches you how the pieces work. Enterprise Mastery teaches you how to make the pieces work together reliably under real production constraints.
Learn the ecosystem and build the essential components. Develop practical fluency across LLM APIs, structured outputs, tools, MCP, RAG, LangGraph, multi-agent frameworks, cloud AI and evaluation fundamentals — with weekly hands-on projects and a final portfolio project.
Best suited for: Developers and working professionals building their Agentic AI foundation.
Explore the Developer Bootcamp →Integrate the components and engineer the complete system for production. Build one flagship Agentic AI system with controlled orchestration, reliable tools, production grounding, persistent memory, asynchronous execution, evaluation, observability, security, deployment and architecture documentation.
Best suited for: Engineers who already understand Agentic AI fundamentals and want production-system depth.
See the Production Curriculum ↓This is not an introduction to Agentic AI. This program is for learners who already understand the foundations of building AI agents and now want to engineer, deploy and operate a complete production-style system.
Completion of the Agentic AI Developer Bootcamp is the recommended preparation for this program — but it is not mandatory. You may join directly if you have equivalent hands-on experience and can already:
You do not need an advanced machine-learning background. However, you should not be encountering LLM APIs, tools, RAG or LangGraph for the first time inside this program.
Start with the Agentic AI Developer Bootcamp. It gives you structured ecosystem fluency across Python, LLM APIs, tool calling, MCP, RAG, LangGraph, multi-agent frameworks, cloud AI and evaluation fundamentals — along with weekly hands-on projects.
Continue with Agentic AI Enterprise Mastery to integrate those capabilities into one reliable, evaluated, secure and deployable production-style system.
The core bootcamp helps you build the system. The bonus weeks help you harden it, review it, and explain it like a senior engineer.
Integrate the components you already know into one flagship Agentic AI system, engineered layer by layer for production: controlled orchestration, governed tools, production grounding, asynchronous execution, evaluation, security, and deployment.
The bonus weeks are for learners who want senior-level depth: enterprise orchestration patterns, architecture review under real constraints, capstone hardening, and portfolio/interview explanation.
Woven into the core weeks — Advanced System Architect Modules that teach you to reason about design decisions the way senior engineers do. Not what to build. How to think about what to build, and why one approach holds under real constraints while another breaks.
Advanced System Architect Modules for Trade-Off Discussions. Production code is table stakes. What distinguishes senior engineers is how they reason through design decisions under real constraints — and communicate those decisions clearly to the people around them. This module teaches that reasoning explicitly.
How to evaluate latency vs cost vs accuracy vs maintainability systematically. Not rules of thumb — structured decision frameworks that production teams use to make and defend architectural choices at scale.
How to architect under real limits: token budget ceilings, team size, data ownership requirements, SLA boundaries, compliance constraints. Real systems are designed inside constraints. This teaches you to think inside them from the start.
How to explain system choices clearly — to engineers, to technical leads, to stakeholders with different mental models. The ability to walk through a design decision with reasoning, not opinion, is what distinguishes senior contributors.
Architecture Decision Records (ADRs) and the documentation patterns that production teams actually use. Leave a paper trail that survives team changes, onboarding cycles, and the questions that come six months after a decision was made.
These modules are woven into the production weeks — not separate theory sessions. Every architect exercise is grounded in code you've already written. This is about how you think on the job, every day.
Instead of disconnected weekly projects, you build one flagship production-style Agentic AI system. Every week adds a serious production layer.
This is not "What is an agent?" — you already know that. Convert a business use case into system requirements, define latency, reliability, cost and safety expectations, decide what belongs inside the graph versus deterministic code, and design the architecture before writing code. Then build a production LangGraph runtime with typed state contracts, checkpointing, loop prevention and failure recovery.
The Developer Bootcamp teaches tool calling and MCP concepts. This week teaches how to operate tools safely and reliably in production: reliable tool contracts, execution policies, idempotency, retries, timeouts, authorization — and an MCP gateway architecture that protects tools from arbitrary model execution.
This is not "upload a PDF and ask questions" RAG. Build a production ingestion pipeline with structure-aware chunking and metadata design, hybrid retrieval with reranking, citation generation, grounding verification and abstention — plus controlled memory with Redis and PostgreSQL persistence, summarisation, TTL policies, and pollution prevention.
One of the clearest differentiators from the Developer Bootcamp. Production agent workflows run for minutes, not seconds — so the flagship system becomes a FastAPI service with Celery workers, queue design, job-state tracking, cancellation, resumption, progress streaming, and graceful degradation.
Far beyond "introducing LangSmith or Langfuse." Build a golden evaluation dataset with output-quality, retrieval, tool-selection and tool-trajectory evaluation, LLM-as-judge design, regression testing and CI evaluation gates. Add end-to-end traces, correlation IDs, latency and cost visibility — and defend the system against prompt injection with validation, PII handling, and human approval for high-risk actions.
The final week ends with a deployed system — not "deployment awareness." Containerise the API, workers and supporting services, wire a CI/CD pipeline with automated tests and evaluation gates before release, and ship a reference AWS deployment (with Azure/GCP portability explained) complete with monitoring, rollback strategy, and an operational runbook.
No introductory MCP or ordinary multi-agent walkthroughs — this week is about orchestration patterns used in serious enterprise systems: approval workflows, escalation and exception handling, multi-agent coordination under explicit constraints, and knowing when not to use multi-agent systems at all.
Review the flagship system the way a senior engineering panel would: threat model, evaluation coverage, observability gaps, cost and latency, scalability bottlenecks, and ADRs — then turn it into a portfolio case study and practise defending your design trade-offs in a senior technical interview setting.
Every tool is introduced through the system we are building. The goal is not tool overload — it is knowing where each piece fits in a production Agentic AI architecture.
From our cohort feedback and public LinkedIn reflections — senior engineers, in their own words.
"My goal for joining this bootcamp wasn't to learn what Agentic AI is — it was to learn how to build AI systems that are scalable, reliable, observable, and truly production-ready. One of the key takeaways has been the importance of viewing Agentic AI as a complete system rather than just an LLM or an agent — success depends on the architecture around it: retrieval, memory, orchestration, evaluation, monitoring, and human oversight."
Read the full post on LinkedIn →"The weekend bootcamps are well-structured, combining concepts with hands-on experiential learning. The discussions with Nachiketh were particularly enriching — a balanced perspective on both the opportunities and challenges of building AI agents, including common pitfalls, reliability considerations, evaluation strategies, and best practices for developing robust agentic applications. A strong blend of theory, practical implementation, and real-world lessons."
Read the full post on LinkedIn →"There are many courses out there around AI concepts and theory. I wanted a curated course on how we actually build solutions and make them production-level rather than only building POCs. Enterprise RAG, adding observability into AI applications, prompt versioning — these are a few of the many things I can explain better now."
"An amazing program where I experienced significant learning and became ready for production-ready projects. It covered every aspect of a production project — from requirements to testing to final deployment."
"This program changed my thinking about how we should implement enterprise-level RAG and build production-ready agents. With no prior exposure to AI, I got a good solid foundation and a clear direction."
"Before this course, I struggled with GenAI concepts. It helped me bridge the gap between a Data Scientist role and a GenAI role. I would highly recommend it to anyone who wants to transition into GenAI with a strong foundation."
"I couldn't connect the dots between LLMs, agents, RAG, guardrails and testing methodology. Now I'm confident developing an end-to-end Agentic AI application with production-grade quality — and I can explain why I'm choosing each architectural decision."
"With 16 years in software engineering, the AI agent and RAG concepts were what I struggled with. Now I can apply these concepts to solving real GenAI use cases — this is one of the few programs in the market covering enterprise AI agents at this depth."
The core bootcamp helps you build the system. The 2-week bonus helps you harden it, review it, and explain it like a senior engineer.
Join at $649 — standard price $699.
Your access includes the upcoming live cohort, recordings, and a premium included benefit: attend any 2 future cohorts free — start early, or revise after your first production build.
Current cohort pricing. Future cohorts may be revised as the program expands.
✓ Secure checkout · EMI available at checkout — from ₹2,002/month (India) · ~$37/month (international)
Not ready to enroll yet? Register for the free demo session first →
6-week core live bootcamp plus a 2-week Advanced Production Mastery Bonus — together, the complete 8-week live experience. Next cohort's 1st class starts Sunday, 20 September 2026. Onboarding details are emailed after you enroll.
They are two stages of one clear progression. The Developer Bootcamp teaches the ecosystem — LLM APIs, tool calling, MCP, RAG, LangGraph, multi-agent frameworks, cloud AI and evaluation fundamentals — through individual hands-on projects. Enterprise Mastery does not repeat those foundations: you integrate the components into one production-style system and progressively add architecture, reliability, asynchronous execution, evaluation, security, observability and deployment. In short: the Developer Bootcamp teaches you how the pieces work; Enterprise Mastery teaches you how to make the pieces work together reliably under real production constraints.
It is the recommended preparation, but not mandatory. You may join directly if you have equivalent hands-on experience: building Python applications that call LLM APIs, structured outputs, basic tool calling, a basic RAG workflow, LangGraph state/nodes/edges/conditional routing, the foundational purpose of MCP, and debugging a small Agentic AI application. If you'd be encountering LLM APIs, tools, RAG or LangGraph for the first time, start with the Developer Bootcamp instead.
The core bootcamp runs for 6 weeks. New registrations also get a 2-week Advanced Production Mastery Bonus, making the complete live experience 8 weeks.
The bootcamp is a premium 6-week core build plus a 2-week Advanced Production Mastery Bonus. The standard cohort price is $699. As a limited-period early learner, your price is $649 — a genuine discount for committing early.
The current cohort is available at the limited-period early learner price of $649 (standard $699). After this cohort closes, the next cohort is planned at $849. Future cohorts may be revised further as the program expands.
Yes — at a production level, not a basics level. Week 3 covers the production grounding layer: ingestion pipelines, structure-aware chunking, hybrid retrieval, reranking, citations, grounding verification, retrieval evaluation and access control. This is enough production RAG to make the integrated system credible. The deeper RAG specialization is included through the self-paced Advanced RAG Implementation Vault so the live cohort stays focused on the system as a whole.
New learners get a focused portal experience: their enrolled cohort, one previous reference cohort, and selected bonus vaults. This avoids confusion and helps learners follow one clear path. You should always know what to watch next.
Yes — it's a premium benefit included with your seat. Every enrolled learner can attend any 2 future cohorts free. Use one to start the material early or catch up if life happens, and use the other to revise topics later once your production Agentic AI work has questions our first round didn't. Plus lifetime portal access to recordings, code, materials, and future content updates.
The next cohort's first class is Sunday, 20 September 2026. Live sessions run Saturdays & Sundays · 8:30 PM – 11:30 PM IST. Onboarding details are emailed after you enroll.
The 2-week bonus is included for new registrations. It goes deeper into senior-level production patterns: Week 7 covers enterprise orchestration — human-in-the-loop approval workflows, escalation handling, constrained multi-agent coordination, A2A patterns, event-driven workflows, and when not to use multi-agent systems. Week 8 is a full architecture review — threat model, evaluation coverage, observability gaps, cost and scalability — plus your portfolio case study and senior-engineer system walkthrough.
3 hours of live instruction on Saturdays and Sundays (6 hours total per weekend), 8:30 PM – 11:30 PM IST. Expect 4–6 additional hours for the weekly build layer and any debugging. Engineers who put in 10–12 hours per week consistently get the most out of the cohort.
No advanced ML background is required — but Agentic AI development foundations are required. This is not an introduction to Agentic AI. You should already be able to build a Python application that calls an LLM API, implement basic tool calling, build a basic RAG workflow, and understand LangGraph state, nodes, edges and conditional routing. Completing the Agentic AI Developer Bootcamp is the recommended preparation, but equivalent hands-on experience works too. Backend engineers, full-stack developers, devops engineers, and data engineers with those foundations all thrive here.
All sessions are recorded and available within 24 hours. You can catch up asynchronously through your lifetime portal access. Live sessions are where the real depth comes from — the Q&A, the debugging, the "why did this fail?" discussions — so we recommend attending live whenever possible. Learners can also attend any 2 future cohorts free — use one to start early or catch up if you miss sessions, and use the other to revise later once your production Agentic AI work has questions the first round didn't cover.
These are structured exercises woven into the core production weeks. They teach you to reason about architecture decisions the way senior engineers do — trade-off frameworks, constraint-driven design, and how to document and communicate decisions clearly. They're not separate theory sessions; they're built into the layers you're already shipping, making the reasoning explicit. This is about how you think on the job, not a separate skill you practice in isolation.
It's designed to give you genuine capability and a portfolio that demonstrates it. Engineers who complete the capstone walk away with a real deployed system and documented architecture decisions — the kind of work you can discuss in depth with any senior technical stakeholder. We don't promise job placement, but we give you something more durable: demonstrated engineering judgment built through real production work.
Yes. EMI options are available at checkout through our payment processor — from approximately ₹2,002/month for Indian learners (on ₹34,999 + GST) and around $37/month for international learners (on $649). You'll see the exact installment options when you proceed to payment. International learners can also use PayPal if your card doesn't go through at checkout.
Because cohort access starts when your seat is confirmed, refunds are limited once you're enrolled. If you can't join this cohort, we can usually move your seat to a future one at no extra cost. Questions before you commit? Email support@manifoldailearning.in. See our Refund Policy.
No. Manifold AI Learning does not offer or imply any job, placement, hiring, salary, or income guarantee — for this Bootcamp or any other program. The Bootcamp is designed to give you genuine engineering capability and a portfolio of deployed work that demonstrates it — production-style builds, documented architecture decisions, and the depth to discuss them with any senior technical stakeholder. Outcomes from there depend entirely on your own effort, applications, and performance.