Learn how AI systems move beyond answering prompts to planning steps, using tools and completing goal-based tasks. Brolly AI's three-month Agentic AI training combines guided learning with real-time live projects, flexible training modes, certification and structured career support.
The PDF contains a proposed syllabus update. Confirm the final batch curriculum and course arrangements with the team.
| Course detail | Information |
|---|---|
| Course | Agentic AI |
| Course duration | 3 months |
| Training modes | Classroom, live online and hybrid |
| Practical learning | Real-time live projects |
| Learner support | Certification, placement assistance, internship support and interview preparation |
| Batch schedule | Weekday and weekend batches |
| Trainer | Shaheda Tabassum |
Review how the proposed agent-first curriculum turns concepts into code, integrates real tools and builds a portfolio you can explain.
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This proposed learning outline follows an agent from its first model call through tools, memory, evaluation and deployment. Review the final module coverage with the trainer during your free demo.
Request the current fee for classroom or live online training. The team can confirm upcoming batch dates, weekday or weekend timings, and payment terms for your preferred option.
Discuss a programme built around your team’s workflows, with on-site or remote delivery and pricing based on the agreed scope.
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Agentic AI training teaches learners how goal-driven AI systems work. Instead of responding to one instruction and stopping, an AI agent can understand an objective, plan a series of steps, use approved tools or services, review the result and decide what to do next.
The purpose is not simply to show learners how to use an AI chatbot. It is to help them understand the thinking, workflow and control required to build or work with AI agents responsibly.
Receive a goal
Understand the available context
Divide the goal into smaller tasks
Select an appropriate tool or action
Complete the action
Check the result
Continue, retry or ask for human input
An agent can query a billing API, filter overdue invoices, check received payments and draft reminder emails. It then pauses for human approval before sending. The tools, retained context and approval points turn a model response into a controlled workflow.
Generative AI mainly creates an output from an instruction. Agentic AI uses AI models as part of a wider system that works toward a goal. Reliable systems need clear permissions, validation, monitoring and human review for important actions.
| Area | Generative AI | Agentic AI |
|---|---|---|
| Main purpose | Creates text, images, code or other content | Works toward a defined goal |
| Typical interaction | Responds to a prompt | Plans and completes multiple steps |
| Tool use | May use a tool when instructed | Can select and use approved tools as part of a workflow |
| Decision process | Usually focused on producing an answer | Decides what action should happen next |
| Human involvement | A user commonly guides each interaction | A human sets the goal, permissions and review points |
| Feature | Traditional AI / ML | Agentic AI |
|---|---|---|
| Core job | Predict or classify from data | Decide and act toward a goal |
| Output | A score, label or forecast | A completed task, plus a record of the steps |
| Flexibility | Fixed to the task it was trained for | Adapts its plan when conditions change |
| Tools | Rarely calls external systems | Designed around calling external systems |
| Human role | Human interprets the output and acts | Human sets the goal and approves key actions |
| Failure mode | Inaccurate prediction | Wrong action taken — which is why evaluation and guardrails matter |
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Agents can use context to adapt a workflow when an input changes, with clear limits and error handling.
Routing, approvals, escalations and exception handling can be expressed as agent workflows rather than hard-coded branches.
Chat interfaces are the easy part. The valuable work is connecting a model to your data, systems and permissions.
Research, data collection, report drafting and ticket triage are being handed to agents that run for minutes, not milliseconds.
Explore agent workflows for finance, HR, support and operations tasks.
An assistant that can read your CRM and file a ticket is a different product from one that only talks.
Divide a larger task among specialised researcher, writer and reviewer agents.
Practise designing tool schemas, managing state and debugging agent loops.
Understand how modern AI systems are designed and gain practical project exposure before applying for internships or entry-level opportunities.
Explore how AI agents can support research, operations, software workflows, data tasks and business automation.
Move beyond basic model calls and understand how goal-based AI applications connect reasoning, tools, data and workflow logic.
Extend existing AI/ML knowledge toward agent architecture, orchestration, evaluation and real-world application design.
Follow a structured learning path, complete guided projects and receive support for presenting new skills during interviews.
Use the free demo and course counselling session to understand the learning path and check whether the program matches your current knowledge and goal.
Move from analysis that informs a decision to agents that carry the decision out, including retrieval, tool calls and scheduled runs.
Your pipelines become the tools agents call. Learn how retrieval, vector stores and state fit alongside the systems you already run.
See how you will learn.
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If Python is new to you, ask the course team which foundation topics to complete before your batch starts.
Agentic AI is not one model or one software tool. It is a way of designing AI systems that can work toward a goal through a series of controlled actions.
An AI agent receives a goal and decides what should happen next. Unlike a basic chatbot response, the work may continue across several connected steps.
Planning allows an agent to divide a broad objective into smaller tasks and decide the order in which they should be completed.
Tools allow an agent to do more than generate text. Depending on its permissions, an agent may search approved information, call an application interface or interact with another software service.
Context gives an agent the information needed for its current task. Memory can help it retain relevant information across steps or sessions. Both must be controlled so the system uses the right information.
A single agent may manage a complete workflow. A multi-agent system divides work among specialised agents that coordinate their tasks.
Evaluation checks whether an agent completed the intended task, used the correct information and followed its limits. Human review remains important for sensitive information, customers, operations or high-impact decisions.
See how you will learn.
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These tools appear in Brolly AI’s published AI syllabus. Discuss which you will use in your Agentic AI batch and how they connect to the project work.
Python functions, data structures and integrations for AI agents.
Chains, agents, memory, output parsers and tool wiring.
Graph-based control flow, state, conditional logic, human-in-the-loop.
Model calls, function calling and structured output.
Open models, pipelines and evaluation.
Vector stores behind retrieval and agentic RAG.
MCP architecture, servers and clients for tool access.
Packaging an agent application for deployment.
Run everything without a heavy local setup.
These are proposed extensions. Confirm their inclusion and depth before enrolling.
Alternative model provider, tool use and long-context work.
Second provider for comparison and fallback design.
Role-based multi-agent teams.
Conversational multi-agent orchestration.
Managed vector database for larger retrieval sets.
Serving agents behind a real HTTP API.
Version control and a portfolio interviewers can read.
Hosting the finished application; discuss the platform with the course team.
An AI agent may appear to work correctly during a short demonstration but fail when information is missing, a tool returns an error or the task changes midway. Project work helps learners see these problems and understand how to handle them.
Brolly AI includes real-time live projects so learners can apply course concepts through practical work. Project-based learning gives learners something concrete to practise, review and discuss instead of relying only on definitions or demonstrations.
Explore these proposed builds in the demo and confirm the final projects for your batch. Each connects a practical problem with a workflow, tools and a reviewable result.
See how you will learn.
Meet your trainer and find a batch that fits your schedule.
Illustrative industry examples show how agents can support different workflows. These are examples of applications, rather than claims about Brolly AI client projects.
| Area | What the agent does | Example |
|---|---|---|
| Customer support | Reads the ticket, retrieves policy, drafts a reply, escalates when unsure | A refund query answered with the correct policy attached |
| Sales automation | Researches a lead, enriches the CRM record, drafts personalised outreach | A prepared call brief waiting before every meeting |
| Research | Plans sub-questions, gathers sources, compares and summarises | A competitor landscape brief with citations |
| Document processing | Extracts fields, validates them, flags exceptions for review | Invoice data pulled into a system with mismatches flagged |
| IT support | Triages a request, runs diagnostics, applies a known fix | Access requests handled end to end within policy |
| Marketing | Researches a topic, drafts variants, checks against brand rules | Campaign copy drafted and pre-screened before review |
| Knowledge management | Answers from internal documents and keeps the index current | New joiners self-serve instead of interrupting the team |
| Data analysis | Writes and runs queries, checks results, explains what it found | A weekly metrics summary with anomalies called out |
| Software development | Reads an issue, proposes a change, runs tests, opens a pull request | Small, well-scoped fixes prepared for human review |
| Business processes | Coordinates steps across several systems with approval gates | Onboarding tasks sequenced and tracked to completion |
The Agentic AI course runs for three months. This gives learners time to understand the concepts, practise them with guidance and apply them through live project work.
| Training mode | How you attend | Suitable for |
|---|---|---|
| Classroom training | Attend sessions in Hyderabad | Learners who prefer direct, in-person interaction |
| Live online training | Join instructor-led sessions remotely | Learners who need location flexibility |
| Hybrid training | Combine online and classroom learning | Learners who want both flexibility and in-person support |
See how you will learn.
Meet your trainer and find a batch that fits your schedule.
| Batch | Days | Learning mode | Next session |
|---|---|---|---|
| Weekday batch | Weekdays | Classroom or live online | Confirm the next start date and class time |
| Weekend batch | Weekends | Classroom or live online | Confirm the next start date and class time |
Choose a weekday or weekend batch. Contact the team for the next start date, exact timings and available seats before enrolling.
Generative AI Specialist & LLM Architect
9+ years in Artificial Intelligence, Machine Learning and Generative AI
Core expertise: Large Language Models, Prompt Engineering, Retrieval-Augmented Generation, AI agents, NLP systems, Deep Learning, MLOps and cloud deployment on AWS.
Industry projects: LLM-based AI assistants, RAG-powered knowledge platforms, intelligent chatbots, fraud detection systems and predictive AI solutions for healthcare and manufacturing.
Teaching style: explains transformers, embeddings and AI agents in simple language, with hands-on coding and job-focused training.
Book a free demoSee how you will learn.
Meet your trainer and find a batch that fits your schedule.
Certification is available as part of the Brolly AI training offer. It gives learners a record connected to course participation. Practical project work remains important because it helps a learner explain what they worked on and how they applied the subject.
Brolly AI provides placement assistance, internship support and interview preparation. This support helps learners present their skills and approach suitable opportunities with clearer evidence of their work. It is support, not a job guarantee.
Ask which of these support activities are included in your Agentic AI batch and how they fit your experience and goals.
An ATS-friendly resume that leads with agent projects rather than a list of tools.
GitHub-ready agent projects with a README that explains the design decisions.
Practice on the questions agent roles actually ask: tool design, failure handling, cost, guardrails.
Technical mocks with feedback from the trainer.
Making your profile findable for AI and agent role searches.
Support with applications and interview scheduling.
These roles use agent and AI application skills at different levels of experience. Opportunities depend on your background, portfolio and employer requirements.
| Role | Responsibilities |
|---|---|
| AI Agent Developer | Designing agent loops, tool schemas and memory; making agents behave predictably in production |
| AI Engineer | Building applications around models — retrieval, integrations, evaluation and deployment |
| Generative AI Developer | Shipping LLM-powered product features and the services behind them |
| LLM Application Developer | Backend work connecting models to company data, permissions and internal systems |
| AI Automation Engineer | Replacing brittle manual processes with monitored agent workflows |
| AI Solutions Developer | Translating a business problem into a workable agent design, then building it |
| AI/ML Engineer | Model work plus the orchestration and serving layer around it |
| AI Architect | System-level decisions: model choice, guardrails, cost, security, scale |
See how you will learn.
Meet your trainer and find a batch that fits your schedule.
Use these capabilities to discuss your learning goals and the final syllabus with the trainer.
Understand the teaching approach, training mode and expected learning commitment.
Tell the counsellor about your education, work experience, coding exposure and reason for learning Agentic AI.
Review the syllabus, prerequisites, batch schedule, fee, certification details and support terms.
Choose classroom, live online or hybrid learning based on your schedule and access needs.
Complete enrolment only after the course structure and learner expectations are clear.
See how you will learn.
Meet your trainer and find a batch that fits your schedule.
Agentic AI training helps learners understand AI systems that can work toward a goal, plan multiple steps, use approved tools, check results and continue within defined limits. It goes beyond learning how to ask a chatbot questions.
An AI agent is a software system that uses artificial intelligence to pursue a goal and complete tasks for a user. Depending on its design, it may reason about the next step, use tools, retrieve information and request human approval.
Generative AI mainly creates content from an instruction. Agentic AI focuses on achieving a goal through connected actions, with workflow logic, tools, limits and evaluation.
No. RAG brings relevant information into an AI response. Agentic AI manages actions toward a goal; an agent can use RAG to find context, but retrieval is only one possible part of an agentic system.
The course is intended for students, freshers, working professionals, developers, AI/ML professionals, career switchers and learners interested in Agentic AI. Course counselling can help each learner understand an appropriate starting point.
Brolly AI's Agentic AI course runs for three months. Learners can choose classroom training in Hyderabad, live online training or a hybrid format.
Yes. Real-time live projects are included so learners can apply course concepts through practical work. Confirm exact project titles, tools and expected outputs with the course team before enrolment.
Yes. Certification is available with the program. Confirm the certificate title, issuer, assessment method and completion requirements during the free demo or course counselling.
Brolly AI provides placement assistance, internship support and interview preparation. These services support a learner's job search but do not guarantee employment; final selection remains with the recruiting organisation.
Human oversight helps prevent an AI agent from acting on incorrect information, using the wrong tool or going beyond its allowed task. Approval points, access controls and output checks are especially important when customer data or business operations are involved.
Freshers can begin with basic Python and build confidence through coding practice. Discuss your starting point and the preparation you need during the free demo.
Basic Python, including variables, loops, functions, lists and dictionaries, is a useful starting point. REST APIs, JSON, Git and prior LLM exposure help. Ask the team whether you need a Python foundation module first.
Ask how Python, LangChain, LangGraph, the OpenAI API, Hugging Face, FAISS, Chroma, MCP, Docker and Google Colab fit the batch syllabus, and which tools you will use directly in project work.
The learning outline uses Python for agent code and tool integrations. You need the basics to follow the practical work; discuss any preparation you need with the course team.
Request current fee details for your preferred training mode through the demo form, by phone or on WhatsApp. Confirm the full fee and any payment terms before enrolling.
Ask the course advisor for the current weekday and weekend schedule. Batch dates, timings and availability are confirmed during enquiry.
Relevant roles include AI Agent Developer, AI Engineer, LLM Application Developer and AI Automation Engineer. Suitability depends on your background, practical portfolio and the requirements of each employer.
Use the demo form on this page, call the team or send a WhatsApp message. The team will confirm the next available slot and discuss your preferred learning mode.
No. Many LLM agents use prompts, tools and orchestration without training a new policy. The reinforcement-learning chapter is a separate conceptual and simulated learning exercise, included to explain decision-making and reward design.
Yes. The proposed chapters cover MCP architecture and controlled integration, short-term and persistent state, memory provenance, coordination patterns and evaluation. Each is connected to a lab and a reviewable output.
The proposal begins with a small Python tool loop, then uses LangGraph for stateful orchestration. Other frameworks are compared by their capabilities. Final tools, versions and account access remain subject to delivery planning.
The proposed exercises use synthetic data and sandbox or mock services. They teach action previews, permission boundaries, dry runs and recovery. Production access is not required to demonstrate these skills.
Check task completion, tool selection and arguments, evidence support, stopping behaviour, recovery and resource use. Compare it with a simpler baseline and test interruptions and difficult inputs as well as successful runs.
Yes. Select Download syllabus (PDF) at the top of this page or after the curriculum. The ungated PDF includes all proposed modules, readiness guidance, four project briefs and assessment criteria. Business details marked in the PDF still need confirmation.
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