Brolly Academy | Agentic AI
Agentic AI Syllabus
A Complete Module-by-Module Course Outline With Projects (2026)
A complete, module-by-module agentic AI syllabus for 2026: what to know before you start, the 10 modules every good course should cover, the tools and projects to expect, how you should be assessed, how long it takes, and a checklist to judge any course syllabus before you pay.
Agentic AI syllabus for 2026: prerequisites, 10 modules from LLMs to tool calling, RAG, multi-agent systems and deployment, plus projects and a checklist.
By Brolly Academy Team · Brolly Academy · Updated 23 October 2026
Table of Contents
1. What an agentic AI syllabus should cover in 2026
2. Prerequisites before you start
3. The complete agentic AI syllabus, module by module
5. Tools you should see in a modern syllabus
6. Projects every agentic AI course should include
8. How long it takes to finish the syllabus
9. How to judge an agentic AI course syllabus: a checklist
Quick answer
A good agentic AI syllabus in 2026 takes you from Python and LLM basics to building, testing and deploying AI agents that plan, use tools, remember context and work together safely. It should cover prerequisites, LLMs and prompting, tool calling, RAG and memory, agent design patterns, at least one current framework, multi-agent orchestration, protocols such as MCP, evaluation and guardrails, deployment and cost, and real projects. Most learners need about 3 to 6 months of steady study to finish it.
- Foundations: Python, APIs, JSON and how large language models work
- Core agent skills: tool calling, structured output, RAG, memory and planning
- Building: one current framework, multi-agent patterns, MCP and A2A
- Production: evaluation, guardrails, human review, observability, cost and deployment
- Proof: 3 to 6 portfolio projects and a capstone you can explain in interviews
Key takeaways
- An agentic AI syllabus is not a generative AI syllabus with a new name. It must teach agents that act through tools, not just chatbots that write text.
- Tool calling, RAG, memory and evaluation are the four topics that turn a chatbot course into an agent course. Check for them first.
- A 2026 syllabus should teach current frameworks. Microsoft AutoGen is archived, and Microsoft Agent Framework is its successor (Microsoft Learn, October 2026).
- MCP is now a core topic. Anthropic donated it to the Agentic AI Foundation under the Linux Foundation in December 2025 (Anthropic, December 2025).
- Judge a syllabus by its outcomes and projects, not by the number of tool logos on the brochure.
1. What an agentic AI syllabus should cover in 2026
Short answer: It should cover how to build systems where a large language model (LLM) decides what to do next, calls tools to act, checks its own work and stops when the goal is met, plus the testing and safety work needed to run those systems for real users.
First, a few terms you will see throughout this guide:
- LLM (large language model): a model such as GPT, Claude or Gemini that reads and writes text. It is the "brain" of an agent.
- Agent: a system that uses an LLM to work towards a goal by deciding steps and calling tools, instead of answering once and stopping.
- Tool calling: the LLM asks your code to run a function (search the web, query a database, send an email) and then uses the result.
- RAG (retrieval-augmented generation): the agent looks up relevant documents first and answers from them, so it is grounded in your data.
- MCP (Model Context Protocol): an open standard that lets agents connect to tools and data sources in one common way.
- Orchestration: the logic that controls which agent or step runs when, how they pass work along, and when a human steps in.
OpenAI's guide puts it simply: "Agents are systems that independently accomplish tasks on your behalf" (OpenAI, A practical guide to building agents). The same guide breaks every agent into three parts: a model, tools and instructions. A good syllabus teaches all three in depth.
Anthropic's engineering team separates workflows, where code follows a fixed path, from agents, where the LLM directs its own process and tool use, and advises starting with the simplest design that works (Anthropic, December 2024). So a good syllabus builds up from a single LLM call, to a workflow, to one agent, and only then to many agents.
There is no single official agentic AI syllabus or "syllabus PDF" that everyone follows. Course providers design their own, so this guide gives you a complete reference syllabus plus a checklist to compare any course against.
2. Prerequisites before you start
Short answer: You need basic Python, comfort with APIs and JSON, simple command line and Git skills, and a clear idea of what an LLM can and cannot do. You do not need a maths degree or prior machine learning experience to start.
Here is a fair entry-level prerequisite list. If a course asks for nothing at all, check whether it includes a Python bridge module.
Maths. You do not need calculus or linear algebra to build agents with existing models. You will use ideas like similarity scores in RAG, but you learn them as you go. Maths matters more if you later move into training models.
Non-coders. You can still learn agentic AI. Start with a low-code builder to understand the ideas, then learn enough Python to move to code. A good beginner course includes a short Python bridge module for this reason. If you are completely new, our agentic AI roadmap shows the full path stage by stage.
3. The complete agentic AI syllabus, module by module
Short answer: A complete 2026 syllabus has 10 modules: Python and APIs, LLMs and prompting, tool calling, RAG and memory, agent design patterns, frameworks, multi-agent systems and protocols, evaluation and guardrails, deployment and cost, and a capstone project.
Each module lists topics, an outcome (what you can do at the end) and a hands-on task. If a course cannot show how its students reach each outcome, it is not complete.
Module 1: Python and API foundations for agents
Topics:
- Python refresher: functions, classes, type hints, error handling and async basics
- Virtual environments, pip and keeping API keys in environment variables
- Calling REST APIs, handling JSON, timeouts and retries
- Data validation with Pydantic, plus Git and GitHub
Outcome: You can write a small Python program that calls an external API, validates the response and handles failures without crashing.
Hands-on: Build a script that fetches weather data for a city and returns a clean, validated summary.
Module 2: LLM fundamentals and prompt engineering
Topics:
- What an LLM is, tokens, context windows and why models hallucinate
- Choosing a model: quality, speed, cost and open versus closed models
- Calling a model through an API: system messages, user messages and parameters such as temperature
- Prompt engineering: clear instructions, examples (few-shot prompting), step-by-step reasoning and role prompts
- Structured output: asking the model for JSON that matches a schema
Outcome: You can call two different LLM providers, write prompts that give consistent results and get reliable structured output.
Hands-on: Build a resume parser that turns plain text into a validated JSON profile.
Module 3: Tool calling and function calling
This is the module that turns a chatbot into an agent. Do not accept a course that rushes it.
Topics:
- How tool calling works: you describe a tool, the model asks to call it, your code runs it and returns the result
- Writing good tool descriptions and input schemas
- The agent loop: think, act, observe, repeat until done
- The ReAct pattern (reasoning plus acting), where the model alternates between thinking about the next step and taking it
- Handling tool errors, timeouts and bad arguments from the model
- Limiting what tools can do: read-only versus write actions, and approval steps
Outcome: You can build a single agent that picks the right tool from several options, recovers from a failed call and stops when the task is done.
Hands-on: Build a research agent with a web search tool, a calculator tool and a note-saving tool.
Module 4: RAG, knowledge and memory
Topics:
- Embeddings, vector databases and similarity search
- Chunking documents, metadata and filtering
- Basic RAG versus agentic RAG, where the agent decides when and what to retrieve
- Short-term memory (the current conversation) and long-term memory (facts saved across sessions)
- Measuring retrieval quality and reducing wrong answers
Outcome: You can build an agent that answers questions from your own documents, shows where each answer came from and remembers user preferences across sessions.
Hands-on: Build a policy Q&A agent over a set of HR or college PDFs.
Module 5: Agent design patterns and planning
Topics:
- Workflows versus agents, and when a plain function is the better answer
- Anthropic's five common patterns: prompt chaining, routing, parallelization, orchestrator-workers and evaluator-optimizer (Anthropic, December 2024)
- Planning: plan-and-execute, breaking goals into sub-tasks and re-planning
- Reflection: the agent reviews and improves its own output
- Human-in-the-loop: where a person approves, edits or rejects a step
Outcome: You can look at a business problem and pick the right pattern, and explain why a more complex design is not needed.
Hands-on: Build the same task twice, once as a fixed workflow and once as an agent, and compare reliability and cost.
Module 6: Agent frameworks
A framework gives you ready-made pieces for the agent loop, state, memory and tool handling. A strong syllabus teaches one deeply and shows how others compare.
Topics:
- Why use a framework, and when plain code is enough
- One framework in depth, for example LangGraph, CrewAI, OpenAI Agents SDK, Google ADK, Pydantic AI or Microsoft Agent Framework
- State, checkpoints and resuming long-running tasks
- Streaming, tracing and debugging inside the framework
- Moving the same project between two frameworks to see the trade-offs
A note on outdated content. Microsoft now presents Microsoft Agent Framework as the direct successor to both Semantic Kernel and AutoGen, with Python and .NET support and a 1.0 production release (Microsoft Learn, October 2026). AutoGen's own repository is archived. A course in 2026 can mention AutoGen as history, but it should not teach it as the framework for new projects.
Outcome: You can build, debug and explain a stateful agent in one current framework.
Hands-on: Rebuild your Module 4 agent in your chosen framework, with saved state and a trace of every step.
For owners, licences and a side-by-side comparison, see our guide to agentic AI frameworks.
Module 7: Multi-agent systems, MCP and A2A
Topics:
- When one agent is enough and when to split work across several
- Multi-agent designs: a manager agent that hands work to specialists, and decentralised handoffs between peers (both described in OpenAI's guide)
- Passing context between agents without losing or leaking information
- MCP: building an MCP server for your own tool and connecting an agent to it
- A2A (Agent2Agent): an open protocol that lets agents built by different teams or vendors discover each other and work together
- Debugging multi-agent conversations and avoiding endless loops
MCP deserves real time in any 2026 syllabus. Anthropic introduced it in November 2024, and in December 2025 it donated MCP to the Agentic AI Foundation, a fund under the Linux Foundation co-founded by Anthropic, Block and OpenAI. At that point Anthropic reported more than 10,000 active public MCP servers (Anthropic, December 2025). Knowing how to build and secure an MCP server is now a practical job skill.
Outcome: You can design a small team of agents, connect them to tools through MCP and explain how they hand work to each other.
Hands-on: Build a sales research crew: one agent finds company news, one summarises it and one drafts an outreach email for human review.
Module 8: Evaluation, guardrails and observability
Topics:
- Why agents fail: wrong tool, wrong argument, made-up facts, loops and runaway costs
- Building an evaluation set: test questions, expected outcomes and pass criteria
- Automatic evaluation, including using an LLM as a judge, and when you still need human review
- Guardrails: relevance checks, safety checks, filtering personal data (PII), moderation, tool safeguards and output validation, as listed in OpenAI's guide
- Human approval for high-risk actions, such as payments or deleting records
- Observability: logging every step, tracing, and tracking latency, token use and cost
- Security basics: prompt injection, least-privilege tools and secret handling
Outcome: You can prove, with numbers from your own test set, that your agent works, and you can show what it did at every step.
Hands-on: Write 30 test cases for your Module 7 project, run them, fix the worst failures and run them again.
Module 9: Deployment, cost and LLMOps
Topics:
- Wrapping an agent in an API with FastAPI
- Containers with Docker and deploying to a cloud platform
- Managed agent platforms on AWS, Azure and Google Cloud, at a high level
- Controlling cost: smaller models for simple steps, caching, limits on steps and tokens
- Rate limits, retries and fallbacks between models
- Versioning prompts, safe rollouts and production monitoring
Outcome: You can deploy an agent that other people can use through a URL, with logs, limits and a cost estimate per task.
Hands-on: Deploy your best project and publish a one-page note on its cost per 100 tasks, based on your own logs.
Module 10: Capstone project and career preparation
Topics:
- Choosing a real problem with a clear user and a measurable result
- A short design document: goal, tools, pattern, risks and evaluation plan
- Building, testing, deploying and demoing the capstone
- GitHub portfolio, resume bullets and interview practice
Outcome: You have one strong capstone and two to five smaller projects that you can demo and defend in an interview.
Optional advanced topics. Some syllabi add reinforcement learning (how agents learn from rewards), fine-tuning small models, or domain modules such as agents for security or cloud automation. These are useful extras. They should not replace Modules 3, 4 and 8.
4. The syllabus at a glance
Short answer: Here is the full syllabus in one table, with suggested weeks for someone studying about 10 to 12 hours a week. Treat the weeks as a guide, not a rule.
That adds up to roughly 16 to 23 weeks, or about 4 to 6 months, part-time. Copy this table into your notes and use it as your own syllabus PDF or checklist.
5. Tools you should see in a modern syllabus
Short answer: Expect a model provider or two, one main framework, a vector database, a protocol layer (MCP), an evaluation and tracing tool, and a deployment stack. A low-code builder is a useful extra for beginners.
Two warnings. First, a long list of logos does not mean deep teaching, so ask which tools you will actually build projects with. Second, check for archived tools. AutoGen is archived, as noted above, so it should not be the main framework in a new syllabus. Open each tool's official repository or docs page and check that it is still actively maintained before you commit months to learning it.
6. Projects every agentic AI course should include
Short answer: At least three to six projects that grow in difficulty, from a single tool-using agent to a deployed multi-agent system, each with tests and a short write-up.
A good progression looks like this:
- Single tool agent: a research or weather agent that calls APIs and handles errors.
- Document Q&A agent: RAG over real PDFs, with source citations in every answer.
- Workflow automation agent: reads emails or tickets, classifies them and drafts replies for human approval.
- Data analysis agent: answers questions about a CSV or database by writing and running queries.
- Multi-agent system: a small crew with a manager agent and specialists, connected to tools through MCP.
- Capstone: a deployed, evaluated agent for a real user group, with a cost and risk note.
Each project needs a GitHub repository, a README, a test set and a short demo. For more ideas, see our list of AI agent projects.
7. How you should be assessed
Short answer: Through hands-on work, not just multiple-choice quizzes: weekly coding tasks, project reviews, an evaluation report on your own agent, a capstone demo and a mock technical interview.
Quizzes test memory, not building. Look for this mix:
Some learners also take a vendor exam after the course. Certificates from a course and vendor certifications are different things. We compare them in our guide to agentic AI certification.
8. How long it takes to finish the syllabus
Short answer: About 3 to 6 months part-time for most learners. Experienced Python developers can move faster, and complete beginners should plan for the longer end, plus extra time for Python.
The time depends on where you start. These are our planning estimates for 10 to 12 hours of study a week, not guarantees:
Shorter options exist. For example, DeepLearning.AI's "Agentic AI" course by Andrew Ng is an intermediate course of about 9 hours 55 minutes. It focuses on four patterns: reflection, tool use, planning and multi-agent work. You can audit it for free, and the certificate needs a Pro plan at $25 per month billed annually (DeepLearning.AI, October 2026). A short course like this is a great start, but on its own it does not cover the full syllabus above, especially deployment, security and a portfolio.
If you want a week-by-week learning plan rather than a course outline, use the agentic AI roadmap linked in the prerequisites section above.
9. How to judge an agentic AI course syllabus: a checklist
Short answer: Check that the syllabus covers tool calling, RAG, memory, evaluation and deployment in depth, uses current frameworks, includes graded projects and a capstone, and lists clear outcomes for each module.
Before you join any course, go through this checklist with the syllabus in front of you:
- Prerequisites are clear. It says what you need before day one and offers a bridge if you lack it.
- Every module has outcomes. "Learn LangGraph" is a topic. "Build a stateful agent that resumes after a failure" is an outcome.
- Tool calling gets real time. At least a full module, with error handling and approvals.
- RAG and memory are both covered. Not just "upload a PDF to a chatbot".
- Design patterns are taught. Workflows versus agents, planning, reflection and human-in-the-loop.
- Frameworks are current. Microsoft Agent Framework rather than AutoGen for new work, and no archived tools as the main stack.
- MCP is included. You build an MCP server, not just hear the name.
- Evaluation and guardrails are graded. You write test cases and prove your agent works.
- Deployment and cost are covered. You put at least one agent online and measure its cost.
- Projects grow in difficulty. Three to six projects plus a capstone, all on your GitHub.
- Trainer has built real systems. Ask to see their projects or past student work.
- It is recent and hands-on. Look for a 2026 update date and ask what share of class time is coding versus slides.
Red flags in an agentic AI syllabus
- It is mostly prompt engineering and image generation with "agentic" added to the title.
- It lists 20 or more tools but only one or two projects.
- There is no evaluation, testing or security topic anywhere.
- It teaches archived tools as the main framework.
- It promises a fixed salary or a guaranteed job without written terms.
- Module names are vague, such as "Advanced AI concepts", with no topic list.
If price is your main question, our guide to agentic AI course fees explains what drives the cost and how to judge value.
10. Agentic AI syllabus vs generative AI syllabus
Short answer: A generative AI syllabus focuses on creating content with models: prompts, text, images and chat apps. An agentic AI syllabus builds on that and adds tool calling, planning, memory, orchestration, evaluation and safe action in real systems.
The risk topics change too. A generative AI course worries about hallucination and content safety. An agentic AI course adds wrong actions, prompt injection, cost loops and human approval. If a course you are considering has a "generative AI and agentic AI" title, check that at least half the syllabus covers the agentic column. Otherwise you are paying for a generative AI course.
11. How Brolly Academy helps
Short answer: Brolly Academy's 3-month Agentic AI Course in Hyderabad follows a 9-module syllabus with six capstone projects, live trainer support, and classroom, online and recorded options.
Brolly Academy's Agentic AI Course in Hyderabad runs for 3 months: Month 1 covers foundations, Month 2 covers tools and workflows, and Month 3 covers projects and deployment, plus career preparation. The course page lists these 9 modules:
- Introduction to Agentic AI
- Foundations of AI Agents
- Multi-Agent Systems (MAS)
- Reinforcement Learning for Agentic AI
- Large Language Models (LLMs) and Agentic AI
- Tools and Frameworks for Building AI Agents
- Agentic AI for Security and Cloud Automation
- Ethics and Risks of Agentic AI
- Hands-on Projects and Case Studies
The roadmap on the page also covers tool calling, MCP basics, agentic RAG, human-in-the-loop and evaluation, and shows OpenAI Agents SDK, Google ADK and Foundry Agent Service. Its tool list includes LangGraph, CrewAI, LlamaIndex and AutoGen; since AutoGen is now archived, treat it as background and Microsoft Agent Framework as its successor.
The six capstone projects are a customer support agent, a document Q&A and RAG agent, a workflow automation agent, an email and sales research agent, a resume screening and HR agent, and a data analysis agent.
- Trainer: Mr. Dinesh, an Agentic AI, Generative AI Specialist and Cloud Architect with 12+ years in AI, ML, cloud computing and generative AI
- Eligibility: no AI experience needed, Python is optional, age 18+
- Modes: classroom and online, with weekend batches and flexible schedules; the next batch starts on Monday, 2 November 2026
- Fees (course page, October 2026): Video Recording ₹9,999, Online Course ₹24,999, Classroom Training ₹29,999; easy EMI is available
- Certificate: a Brolly Academy Agentic AI course completion certificate, which is a course certificate, not a vendor certification
- Career support: placement assistance, resume and LinkedIn help, mock interviews and internship opportunities
- Try first: a free demo class
See the full Agentic AI syllabus in a free demo class
Want to know how the 9 modules and six capstone projects map to the syllabus in this guide? Book a free demo class with Brolly Academy, meet the trainer, and ask exactly how tool calling, RAG, MCP and evaluation are taught before you decide. Classroom batches run near JNTU Metro Station in Hyderabad, and online and recorded options are available.
Frequently asked questions
What is an agentic AI syllabus?
An agentic AI syllabus is the list of modules, topics, tools and projects a course uses to teach you how to build AI agents. A good 2026 syllabus starts with Python and LLM basics, then covers tool calling, RAG, memory, design patterns, a current framework, multi-agent systems, MCP, evaluation, guardrails and deployment. It ends with a capstone project you can show employers. There is no single official version, so use a reference syllabus to compare courses.
What topics are included in an agentic AI course syllabus?
An agentic AI course syllabus should include Python and APIs, LLM fundamentals and prompting, tool calling, RAG and memory, agent design patterns such as planning and reflection, at least one current framework, multi-agent orchestration, MCP and A2A, evaluation and guardrails, and deployment with cost control. It should finish with three to six projects and a capstone. Optional topics include reinforcement learning, fine-tuning and domain modules such as security automation.
What are the prerequisites for learning agentic AI?
The main prerequisites are basic Python, comfort calling APIs and reading JSON, simple command line and Git skills, and some experience using tools like ChatGPT, Claude or Gemini. You do not need a maths degree or prior machine learning experience to start building agents with existing models. If you lack Python, choose a course with a short Python bridge module or learn the basics first.
Do I need Python to learn agentic AI?
Python is strongly recommended because almost every agent framework is Python first. You can begin with low-code builders such as n8n, Dify or Langflow to understand how agents work, but serious agent building, testing and deployment need code. Learn functions, classes, lists, dictionaries, pip, virtual environments and API calls first, because every later module builds on them.
Can non-coders learn agentic AI?
Yes, non-coders can learn agentic AI, but they should plan extra time. Start with a low-code builder to learn ideas such as tools, memory and workflows visually. Then learn enough Python to build simple agents in code. A complete beginner may need about 5 to 7 months part-time to finish a full syllabus, compared with 3 to 4 months for a working developer.
How long does it take to complete an agentic AI syllabus?
Most learners need about 3 to 6 months part-time, studying 10 to 12 hours a week. A full 10-module syllabus adds up to roughly 16 to 23 weeks. Working developers can finish in about 3 to 4 months, data scientists in 2 to 3 months, and complete beginners should plan for 5 to 7 months including time to learn Python.
Is maths required for an agentic AI course?
Heavy maths is not required to build agents with existing models. You will meet ideas like similarity scores when you learn embeddings and RAG, but you can learn them as you go. Calculus and linear algebra matter more if you later move into training or fine-tuning models. For most agent engineering roles, Python, system design and testing skills matter more than advanced maths.
Which frameworks should an agentic AI syllabus teach?
A good syllabus teaches one current framework in depth and shows how others compare. Common choices are LangGraph, CrewAI, OpenAI Agents SDK, Google ADK, Pydantic AI and Microsoft Agent Framework. The exact framework matters less than learning state, tool handling, tracing and debugging well. A good course also has you move one project to a second framework so you understand the trade-offs.
Should an agentic AI course still teach AutoGen?
AutoGen should only be taught as background. Its repository is archived, and Microsoft presents Microsoft Agent Framework as the direct successor to both AutoGen and Semantic Kernel, with Python and .NET support and a 1.0 production release. A 2026 course can mention AutoGen as history, but new projects should use Microsoft Agent Framework or another actively maintained framework.
What is MCP and why is it in the agentic AI syllabus?
MCP, the Model Context Protocol, is an open standard that lets agents connect to tools and data sources in one common way. Anthropic introduced it in November 2024 and donated it to the Agentic AI Foundation under the Linux Foundation in December 2025, when it reported more than 10,000 active public MCP servers. Building and securing an MCP server is now a practical job skill.
What is RAG in an agentic AI syllabus?
RAG, or retrieval-augmented generation, means the agent looks up relevant documents before answering, so its answers are grounded in your data. In an agentic AI syllabus you learn embeddings, vector databases, chunking and similarity search, then agentic RAG, where the agent itself decides when and what to retrieve. A good module also teaches you to cite sources and measure retrieval quality.
What is tool calling in agentic AI?
Tool calling is how an LLM takes action. You describe a tool such as a web search or database query, the model asks to call it with certain inputs, your code runs it, and the result goes back to the model. This loop of thinking, acting and observing is what turns a chatbot into an agent, so a good syllabus gives it a full module.
What projects should an agentic AI course include?
A strong course includes three to six projects that grow in difficulty. Good examples are a single tool-using research agent, a document Q&A agent with RAG, a workflow automation agent with human approval, a data analysis agent, a multi-agent system connected through MCP, and a deployed capstone. Each project should have a GitHub repository, a README, a test set and a short demo.
How are students assessed in an agentic AI course?
Good courses assess you through hands-on work rather than only quizzes. Expect weekly labs with code review, module projects with design feedback, an evaluation report showing your agent's test results before and after fixes, a live capstone demo with questions on trade-offs, and a mock technical interview on topics such as tool calling, RAG, MCP and how agents fail.
Is there an agentic AI syllabus PDF?
There is no single official agentic AI syllabus PDF that every course follows. Each course provider designs its own syllabus. The best approach is to use a complete reference syllabus, like the 10-module table in this guide, as your checklist. Copy it into your notes and compare any course PDF or brochure against it, module by module.
What is the difference between a generative AI and an agentic AI syllabus?
A generative AI syllabus focuses on creating content: prompts, text, images and chat apps. An agentic AI syllabus builds on that and adds tool calling, planning, memory, orchestration, multi-agent systems, evaluation and safe action. If a course is titled "generative AI and agentic AI", check that at least half of its modules cover the agentic topics.
Is the DeepLearning.AI Agentic AI course enough on its own?
It is a strong start but not a full syllabus. The course by Andrew Ng runs about 9 hours 55 minutes and covers reflection, tool use, planning and multi-agent patterns. You can audit it free, and the certificate needs a Pro plan at $25 per month billed annually. It does not cover deployment, security and portfolio building in depth, so add those separately.
How do I judge whether a course syllabus is good?
Check that every module has clear outcomes, that tool calling, RAG, memory, evaluation and deployment each get real time, and that the frameworks are current. Look for MCP, graded projects that grow in difficulty, a capstone, a trainer with real project experience and a 2026 update date. Also ask how much class time is hands-on coding versus slides.
What are the red flags in an agentic AI syllabus?
Watch for a syllabus that is mostly prompt engineering with "agentic" added to the title, a long tool list with only one or two projects, no testing or security topics, archived tools taught as the main framework, vague module names, and promises of a fixed salary or guaranteed job without written terms. Any of these suggests the course will not make you job ready.
Does an agentic AI syllabus cover multi-agent systems?
A complete syllabus should cover multi-agent systems after you can build a reliable single agent. You learn when to split work across agents, manager and handoff designs, passing context safely, connecting agents to tools through MCP, how A2A lets agents from different teams work together, and how to debug multi-agent conversations and avoid endless loops.
Why should evaluation and guardrails be in the syllabus?
Agents can pick the wrong tool, invent facts, loop forever or run up costs, so you need to prove they work before real users rely on them. Evaluation teaches you to build test sets and measure results. Guardrails cover relevance and safety checks, filtering personal data, tool safeguards, output validation and human approval for high-risk actions such as payments.
Is reinforcement learning part of an agentic AI syllabus?
Reinforcement learning is usually an optional or advanced topic. It explains how agents can learn from rewards, which is useful background. However, most agent engineering work in 2026 uses existing LLMs with tool calling, RAG and orchestration rather than training agents with rewards. Reinforcement learning should not replace core modules on tool calling, memory and evaluation.
What does the Brolly Academy agentic AI syllabus cover?
Brolly Academy's 3-month Agentic AI Course in Hyderabad has 9 modules, from Introduction to Agentic AI and Foundations of AI Agents to Multi-Agent Systems, LLMs, Tools and Frameworks, Security and Cloud Automation, Ethics and Risks, and Hands-on Projects. Its roadmap also covers tool calling, MCP basics, agentic RAG, human-in-the-loop and evaluation, with six capstone projects.
Do I need AI experience to join Brolly Academy's agentic AI course?
No, Brolly Academy's course page says no AI experience is needed, Python is optional and learners must be 18 or older. The course runs for 3 months in classroom, online and recorded formats, with weekend batches and a free demo class. Learners get a Brolly Academy course completion certificate, which is a course certificate rather than a vendor certification.
What should I learn after finishing an agentic AI syllabus?
After the syllabus, deepen one area and keep building. Good next steps are learning a second framework, building and securing MCP servers, improving your evaluation skills, deploying on one cloud platform in depth, or preparing for a vendor certification. Keep adding projects to your GitHub, because a working, tested and deployed agent is the strongest proof of your skills.
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