Agentic AI Course: Build Production-Ready AI Agents

An Agentic AI Course teaches developers and engineers how to build, deploy, and scale autonomous AI systems. Unlike static prompt engineering, it focuses on constructing AI agents capable of reasoning, planning, dynamic tool execution, memory retrieval, and multi-agent collaboration.

It teaches you to design, build, evaluate and deploy GenAI applications and AI agents. You progress from LLM foundations and structured outputs to RAG, single and multi-agent orchestration, MCP, evaluation, guardrails and deployment, with a build in every module and a production capstone at the end.

Generative AI mainly creates text, images or code from an instruction. Agentic AI places a generative model inside a controlled system that can plan and act — retrieving evidence, calling an API, inspecting the result and continuing until a goal or stopping condition is reached, with permissions and human approvals limiting autonomy.

The syllabus covers LangChain, LangGraph, CrewAI, AutoGen, MCP, n8n, Pydantic, RAGAS, LangSmith or Langfuse, FastAPI, Docker, Pytest and GitHub Actions. Tools are taught as parts of a system, so you learn when each helps and when plain Python is the safer choice.

MCP is an open protocol that gives AI applications a consistent way to discover and use tools and contextual resources. Teams can expose approved capabilities through a standard client-server interface instead of one-off integrations. Module 6 covers its architecture, permissions, testing and audit-friendly tool access.

Yes. The final module covers serving an agent with FastAPI, containerizing it with Docker, managing secrets, streaming responses, monitoring cost and latency, and deploying to AWS. You also add evaluation checks and a runbook to the capstone.

Basic Python is expected; machine-learning experience is not. You should be able to write functions, use lists and dictionaries, read JSON, install packages and work from the command line. Prior work with APIs, Git or SQL helps but is not required — REST APIs and LLM concepts are taught in Module 1.

Yes, if you have working Python basics and can commit consistent practice time. Freshers may need extra preparation in APIs, JSON, Git and debugging. Ask an advisor whether the self-paced Python preparation track is right for your starting point.

Technical product managers and non-IT professionals can join if they are ready to learn basic Python and work through hands-on labs. The course is build-oriented, so an advisor conversation about your starting point is recommended before you enroll.

The course runs for 7 months with 100+ live hours. Plan for 8–10 hours per week across live sessions, revision and project work.

Classes are live online. Every session is recorded, and you get lifetime access to the recordings for catch-up and revision. Recordings help with revision, but completing the builds still requires active coding and asking questions when an implementation fails.

Each module includes a hands-on build. For the certificate you need at least 80% live-session attendance and a submitted capstone. The capstone should include a repository with a README and architecture diagram, a deployed service with monitoring, and evaluation evidence with known failure modes.

Doubt support is provided through dedicated doubt-clearing sessions with mentors and a mentor-moderated learner community, where most questions are answered within 24 hours. Share the error, the expected result and what you have already tried to get the fastest help.

The standard fee is ₹70,000, inclusive of GST. A promotional fee of ₹59,500 (15% off) applies to the first 15 candidates. No-cost EMI is available from ₹5,000/month, with 0% interest on credit cards — confirm tenure, eligible banks and any processing charges with an advisor before enrolling.

Possibly. Model APIs, capstone hosting and optional observability tools may create usage charges beyond tuition. You can keep them low with free tiers, smaller models, caching and strict budgets.

Refund requests must be emailed within seven days of the batch start and before the third class; the window runs from the batch start even if you miss early classes, and no refunds are given after it. Discounted or promotional enrollments are non-refundable unless stated otherwise at purchase. Batch switching is available for a genuine medical emergency, subject to approval, seat availability and supporting documents, and an approved switch does not restart the refund window. Eligibility is confirmed in 3–5 business days and processing takes 7–10 business days after approval.

No. Career support improves your preparation, but roles and salaries depend on your prior experience, project quality, interview performance and the job market. Referrals are offered where applicable. This page does not quote salary figures, because no outcome data specific to this course has been published.

Learners who attend at least 80% of live sessions and submit the capstone receive the LogicMojo Agentic AI Course Certificate. Pair it with working repositories and a deployed demo — that is what hiring teams check.

Agentic AI Engineers are among the most sought-after tech professionals in 2026. Organizations are actively hiring specialists who can build autonomous workflows to replace traditional linear automations.

The skills map to roles such as AI Engineer, GenAI or LLM Engineer, Agentic AI Engineer and AI Automation or LLMOps Engineer. A course does not guarantee a role; prior experience, project quality and interview performance still shape outcomes.

Career support includes resume and GitHub portfolio reviews, role-specific mock interviews, capstone and project presentation preparation, interview-question practice covering LLMs, RAG, agents and system design, one-to-one career guidance, and referral support where applicable. Support can improve preparation but is not a placement guarantee.