01
Students After 12th
Students from any stream can begin the programme with the Python fundamentals module and gradually progress toward AI application development.
Start right after school
Best After 12th 6-Month Agentic AI Program in Mohali
Build practical AI agents from the ground up with a six-month, project-driven programme designed for students after 12th. Start with Python, APIs and LLM fundamentals, then progress into RAG, memory, agent frameworks, evaluation, security, multi-agent architectures, browser automation, deployment and AI infrastructure.
By the end of the programme, you will have worked on practical AI systems, evaluated their performance, secured them against common attacks and deployed production-oriented projects that can strengthen your technical portfolio.
0113
01Where this starts
Course Overview
This six-month programme follows a structured path from programming fundamentals to advanced agent engineering.
During the first three months, you establish the core knowledge needed to work with Python, APIs, language models, prompting, tool calling, retrieval systems, memory, agent frameworks and evaluation.
The second half focuses on professional engineering practices. You work with asynchronous Python, model routing, prompt optimisation, production MCP, advanced retrieval, knowledge graphs, durable workflows, multi-agent systems, browser automation, coding agents, AI security and cloud-native deployment.
Rather than treating every topic as isolated theory, the programme connects each concept with a practical implementation. You progressively build systems, test them, analyse their behaviour and improve them.
Where it takes you
- AI Engineer / LLM Application Engineer
- Agent Platform Engineer
- AI Automation / Integration Engineer
- MLOps / AI Reliability Engineer
- Generative AI Developer
02Skills you collect
What You'll Learn
Four engineering outcomes the six months are built around — reliability, security, durability and cost.
Build Reliable AI Agents
Move beyond simple chatbot demonstrations and understand how reliable AI systems are engineered using concurrency controls, retries, circuit breakers, checkpoints and failure-handling strategies.
Test and Secure AI Systems
Learn how to evaluate AI applications and identify vulnerabilities such as prompt injection, unsafe tool usage and data exposure through structured testing and red-team exercises.
Design Durable AI Workflows
Create workflows that can maintain state, recover from interruptions and continue long-running tasks without losing important progress.
Engineer for Cost and Performance
Learn how model selection, routing, caching, context management and smaller models can influence latency, quality and operating costs.
03The route
Course Curriculum
Six months in two halves — Python, prompting, retrieval, memory, frameworks and evaluation across months one to three, then production engineering, advanced retrieval, multi-agent systems, security and deployment across months four to six.
- Begin with Python and gradually connect programming concepts to modern AI application development.
- Python fundamentals including syntax, variables, data structures, loops, functions and classes.
- Type hints, asynchronous programming and virtual environments using modern Python tooling.
- Command-line fundamentals and development workflows.
- Git and GitHub including repositories, commits, branches, pull requests and .gitignore.
- HTTP, REST APIs, JSON, API keys and bearer-token authentication.
- Introduction to SQL and PostgreSQL.
- Docker fundamentals and application containerisation.
- Understanding language models through tokens, context windows, temperature and conversational roles.
- Core characteristics of AI agents: goals, tools, memory and autonomy.
- Prompt design including few-shot prompting and structured responses.
- JSON Schema and Pydantic validation.
- Tool calling and parameter design.
- Building a ReAct-style agent loop without depending entirely on a framework.
- Model Context Protocol (MCP), including servers, clients, resources and transports.
- Connecting external REST APIs with AI tools.
- Tool Stack: Python · Git · GitHub · FastAPI · PostgreSQL · Docker · Claude API · OpenAI API · Pydantic · MCP SDK
- Deliverable: A containerised API service, model comparison exercise and an MCP server containing multiple usable tools.
A Deliverable Every Month
Each of the six months closes on its own shipped artefact — an MCP server, a cited knowledge assistant, a deployed agent, a model router, a durable multi-agent application and the final capstone.
Outcome
By completing the curriculum, students will have built, evaluated, secured, deployed and costed AI agents across the full engineering stack, closing on a documented capstone with a technical case study.
04Your toolkit
Technologies You Will Practise
The programme uses a broad development stack so that you understand not only how AI models work, but also how AI applications are developed around them.
PythonGit & GitHubFastAPIPostgreSQLDockerOpenAI APIClaude APILangChainLangGraphLlamaIndexQdrantChromaFAISSRedisRAGASLangSmithLangfuseMCPPythonGit & GitHubFastAPIPostgreSQLDockerOpenAI APIClaude APILangChainLangGraphLlamaIndexQdrantChromaFAISSRedisRAGASLangSmithLangfuseMCPPythonGit & GitHubFastAPIPostgreSQLDockerOpenAI APIClaude APILangChainLangGraphLlamaIndexQdrantChromaFAISSRedisRAGASLangSmithLangfuseMCP
FastMCPLiteLLMDSPyvLLMOllamaHugging FaceNeo4jGraphRAGTemporalCrewAIAutoGenPlaywrightKubernetesTerraformHelmPrometheusGrafanaFastMCPLiteLLMDSPyvLLMOllamaHugging FaceNeo4jGraphRAGTemporalCrewAIAutoGenPlaywrightKubernetesTerraformHelmPrometheusGrafanaFastMCPLiteLLMDSPyvLLMOllamaHugging FaceNeo4jGraphRAGTemporalCrewAIAutoGenPlaywrightKubernetesTerraformHelmPrometheusGrafana
- Python
- The language everything in the programme is built in.
- Git & GitHub
- Repositories, branches and the development workflow.
- FastAPI
- Serve agents and pipelines behind an HTTP API.
- PostgreSQL
- Relational storage for application and agent data.
- Docker
- Containerise the agent for deployment.
- OpenAI API
- One of the model providers behind the agents.
- Claude API
- A second provider, for comparison and structured work.
- LangChain
- Chains, tools and the glue around model calls.
- LangGraph
- Nodes, edges, routing, subgraphs and durable state.
- LlamaIndex
- Document indexing and retrieval pipelines.
- Qdrant
- A vector database for semantic search.
- Chroma
- A lightweight vector store for local RAG work.
- FAISS
- Similarity search over embeddings at speed.
- Redis
- Short-term memory and fast shared state.
- RAGAS
- Measure retrieval and answer quality.
- LangSmith
- Trace runs and debug agent trajectories.
- Langfuse
- Logging, monitoring and cost tracking.
- MCP
- Servers, clients, resources and transports.
- FastMCP
- Build production MCP servers quickly.
- LiteLLM
- One interface across several model providers.
- DSPy
- Signatures and metric-based prompt optimisation.
- vLLM
- Self-hosted model serving at throughput.
- Ollama
- Run local models on your own machine.
- Hugging Face
- Transformers, tokenizers and pretrained models.
- Neo4j
- The graph database behind GraphRAG.
- GraphRAG
- Knowledge-graph retrieval for multi-hop questions.
- Temporal
- Durable execution for long-running workflows.
- CrewAI
- Multi-agent orchestration and task delegation.
- AutoGen
- A second multi-agent framework to compare.
- Playwright
- Browser automation for web agents.
- Kubernetes
- Scale stateful AI services in production.
- Terraform
- Infrastructure as Code for the deployment.
- Helm
- Package and version the Kubernetes deployment.
- Prometheus
- Collect service and application metrics.
- Grafana
- Dashboards for reliability and cost.
05Is this you?
Who Can Join the Programme?
02
College Students
Students pursuing BCA, B.Sc, B.Tech or other technology-related degree programmes can use the course to add practical AI engineering skills alongside their academic studies.
03
Developers & Technical Learners
Learners with existing programming knowledge can strengthen their capabilities in LLM applications, agent architecture, evaluation, security and deployment.
04
Career Changers
If you are moving toward the AI technology field, the structured six-month progression provides a practical route from programming fundamentals to project development.
05
Freelancers & Entrepreneurs
Understand how AI agents can be designed around business workflows, automation requirements, information retrieval and operational processes.
06
Self-Learners
If you have explored AI independently but need a structured curriculum and project-based learning environment, this programme provides a guided progression.
06Why this one
Why This Programme Is Worth Your Six Months
01
Build More Than Prompt Skills
Agentic AI requires much more than writing effective prompts. You need to understand APIs, state, tools, retrieval, evaluation, security, deployment and system reliability.
02
Reliability Engineering
Learn concurrency management, retries, checkpoints, circuit breakers and failure recovery so your applications can handle problems instead of simply working during a demonstration.
03
Cost-Aware AI Development
Understand model selection, routing, caching and context management to design AI applications with performance and operational costs in mind.
04
Security-First Development
Learn how AI applications can be attacked and how architecture, permissions, guardrails, sandboxing and monitoring can reduce risk.
05
Practical Multi-Agent Engineering
Build multi-agent systems and compare them against simpler architectures instead of assuming that adding more agents automatically improves the result.
06
Production-Oriented Deployment
Gain exposure to containers, cloud deployment, Kubernetes, monitoring, SLOs, infrastructure automation and operational workflows.
07Why now
Build AI Systems That Can Actually Do Work
AI is moving beyond question-and-answer interfaces toward systems that can use tools, retrieve information, interact with applications and complete multi-step workflows.
That creates demand for people who understand the engineering layer surrounding AI models.
This programme is structured around those practical engineering questions.
A strong AI engineer needs to answer questions such as
- How reliable is the agent?Month 4 — retries, backoff, jitter and circuit breakers.
- What happens when a tool fails?Month 5 — durable execution and rollback strategies.
- How is sensitive information protected?Months 3 and 6 — PII filtering, guardrails and least privilege.
- How much does each successful task cost?Month 6 — AI unit economics and cost per successful task.
- How do you evaluate an AI workflow?Months 3 and 6 — datasets, trajectory evals and LLM-as-judge.
- Can the system recover after an interruption?Months 2 and 5 — checkpointing and resumable execution.
- How do multiple agents communicate?Month 5 — shared state versus message passing.
- How can the application be monitored after deployment?Month 6 — tracing, SLOs, Prometheus and Grafana.
Talk to a Course Advisor
Ten minutes with the techcadd team settles eligibility, session timings, fees and where this leads — before you commit six months to it.
08Certification
Complete the Agentic AI Programme
After completing the required programme components, learners may receive course completion documentation according to the current certification terms.
Course Completion CertificateA certificate documenting successful completion of the Agentic AI training programme, subject to programme requirements.
Project PortfolioYour practical work can become part of a technical portfolio demonstrating your development experience.
Project DocumentationLearn to document architecture, implementation decisions, testing results and deployment details.
Career PreparationGet support with resume development, project presentation, interview preparation and career guidance where included in the current programme.
Capstone Defense
Fullscreen
Project Completion Certificate
ISO 9001:2015 Accredited
Fullscreen
Course Completion Certificate
Two documents on completion — the course certificate, and a separate certificate for the capstone you build and defend. Click any card to inspect in full screen.
09Where it leads
Where Can Agentic AI Skills Take You?
The programme develops skills relevant to several emerging technology roles.
Roles this programme prepares you for
Work on applications that integrate language models with APIs, databases, retrieval systems and external tools.
10What you build
Hands-On Projects You Will Build
01
Async LLM Client Library
Create a production-oriented Python client with asynchronous execution, retries, rate limiting, caching and automated tests. Technologies: asyncio · httpx · Tenacity · pytest
02
Multi-Provider Model Router
Build a routing layer that can select different models according to task requirements, performance and cost. Technologies: LiteLLM · vLLM · OpenRouter
03
GraphRAG Retrieval Service
Combine semantic retrieval, reranking and graph-based knowledge retrieval to answer complex multi-step questions. Technologies: Neo4j · GraphRAG · ColBERT
04
Durable Workflow Agent
Create a long-running agent workflow capable of maintaining state and recovering after an interrupted execution. Technologies: Temporal · LangGraph · Redis
05
Browser & Coding Agent
Develop an automated browser workflow and explore coding-agent techniques for repository analysis, testing and code changes. Technologies: Playwright · Browser Use · Claude Code
06
AI Red-Team Security Report
Design an automated security-testing workflow that explores prompt injection and other common attack scenarios and documents mitigation results. Technologies: Garak · PyRIT · Lakera Guard
07
Kubernetes AI Deployment
Deploy an AI service using container orchestration and infrastructure automation while monitoring application performance and reliability. Technologies: Kubernetes · Helm · Terraform · ArgoCD
11How it works
Learn It. Build It. Explain It.
Every major project follows a practical cycle designed to turn concepts into demonstrable skills.
Understand
Study the requirement, identify the technical problem and choose an appropriate architecture.
Async LLM Client LibraryBuild
Implement the system with practical guidance, testing and iterative improvements.
Multi-Provider Model RouterPresent
Explain your architecture, technical decisions, challenges and results so the project becomes something you can confidently discuss in interviews.
GraphRAG Retrieval Service12Why techcadd
A Structured Path From Beginner to Advanced AI Engineering
Progressive topics, working applications at every stage, and a stack that runs all the way to deployment.
01
Skill-Based Progression
Topics are connected progressively, allowing learners to move from Python fundamentals toward complex AI systems without jumping immediately into advanced infrastructure.
02
Practical Project Development
The curriculum focuses on building working applications alongside the concepts being taught.
03
Modern AI Technology Stack
Learners are introduced to current tools and frameworks used across LLM application development, retrieval, orchestration, evaluation and deployment.
04
Deployment-Focused Learning
The programme extends beyond local development into containers, deployment, monitoring and cloud-native infrastructure.
05
Career-Oriented Preparation
Project documentation, GitHub work, portfolio development, resume preparation and interview practice can help learners present their technical skills more effectively.
13Who has walked it
Students who started where you are
Alumni of this route, on what changed once they were sitting in interviews.
4.8
425 reviews
586%
49%
33%
21%
11%
AutoCAD and SolidWorks were taught with actual production drawings, not textbook exercises. My employer noticed that my drawing sets followed proper standards from day one.
I joined the 6-month MERN track straight after B.Tech with almost no practical experience. The live project work is what changed things — I had real code to talk about in interviews instead of just a syllabus.
The trainers actually work in the field, so every session had context from real projects. The Power BI and SQL modules were exactly what my interview rounds tested.
The Generative AI course was current in a way online tutorials are not. Building a full RAG pipeline and deploying it gave me something genuinely impressive for my portfolio.
14Before you go
Frequently Asked Questions
Still unsure? A ten-minute call with a counsellor usually settles it faster than any brochure.
The programme runs for six months. The curriculum is divided into foundational AI development during the first half and advanced engineering, security, deployment and capstone work during the later months.
15Final stage
Ask About the Agentic AI Certificate Program
Want to understand whether Agentic AI is suitable for your career plans? Speak with a course counsellor about current session timings, course fees, available payment or EMI options, eligibility, course structure, project work, certification, career support and the admission process.
Location: Mohali, Punjab. Counselling hours: Monday – Saturday, 9:00 AM – 7:00 PM.
+91 98881 22442Call the admissions desk+91 98881 22442WhatsApp for a quick replyinfo@techcadd.comEmail us your questions
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Still deciding
Not Sure If Agentic AI Is Right for You?
A counselling session can help you understand the curriculum, practical projects, eligibility and learning path before you enrol.
Start with a free demo or speak with the Mohali course team to understand how the six-month Agentic AI programme can fit into your education and career plans.
Get Started Today
- 6 months, 12th pass, any stream
- Starts at Python — no prior programming required
- RAG, memory, LangGraph, MCP and multi-agent systems
- Evaluation, guardrails and AI security throughout
- 7 practical projects and a deployed capstone
- Kubernetes, Terraform and production deployment