Natural Language Processing (NLP)
NLP covers the full arc of language processing: from tokenisation and classical statistical methods through word embeddings to transformer-based models.
2 months Classroom & Online
View syllabus 6 modules
AI & Advanced Technologies
Agentic AI
Systems that plan, act and check their own work.
Agentic AI is the current frontier of applied LLM engineering: systems that decompose a goal, choose tools, act on the world, observe the result and correct course — without a human driving every step.
6 weeks Classroom & Online Advanced 6 modules
Agentic AI Systems that plan, act and check their own work.
Overview
What this course is, plainly
Agentic AI is the current frontier of applied LLM engineering: systems that decompose a goal, choose tools, act on the world, observe the result and correct course — without a human driving every step.
This track is heavily practical. You build single agents, then tool-using agents, then multi-agent systems, and you spend real time on the hard part: making them reliable, observable, bounded and safe enough to run unattended.
Why choose this course
- Tool use, planning and self-correction loops
- Multi-agent orchestration patterns
- Model Context Protocol (MCP) integration
- Observability, tracing and cost control
- Guardrails, sandboxing and human-in-the-loop design
Curriculum
Module-by-module syllabus
The same structure on every Xypron course page, so you can compare tracks like for like. Each module ends in working code, not a quiz.
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Module 1: Agent Foundations
What actually makes a system agentic rather than just a prompt chain.
- Agent loops: reason, act, observe
- The ReAct pattern
- Planning vs. reactive architectures
- Task decomposition strategies
- When an agent is the wrong answer
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Module 2: Tools & Function Calling
Giving a model hands.
- Designing tool schemas an LLM can use reliably
- Parallel and sequential tool execution
- Error handling & tool retry logic
- Model Context Protocol (MCP) servers
- Connecting to APIs, databases & file systems
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Module 3: Memory & Context
What the agent remembers between steps and sessions.
- Short-term working memory
- Long-term memory with vector stores
- Context window management & compaction
- State persistence across sessions
- Retrieval inside an agent loop
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Module 4: Multi-Agent Systems
Several agents, one goal, without chaos.
- Orchestrator / worker patterns
- Specialist agent design
- Inter-agent communication & handoff
- LangGraph & CrewAI frameworks
- Cost and latency of fan-out
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Module 5: Reliability & Observability
The engineering that makes an agent trustworthy.
- Tracing every step of an agent run
- Evaluating agent trajectories, not just outputs
- Loop detection & step budgets
- Failure recovery & graceful degradation
- Token and cost instrumentation
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Module 6: Safety, Deployment & Capstone
Running an autonomous system without regretting it.
- Permission boundaries & sandboxing
- Human-in-the-loop approval gates
- Prompt injection in agentic contexts
- Deploying an agent as a service
- End-to-end capstone project
Projects you'll build
- Research agent with web and document tools
- Multi-agent support triage system
- MCP-connected internal operations assistant
Roles this prepares you for
- AI Agent Engineer
- Applied AI Engineer
- LLM Systems Developer
- AI Solutions Architect (entry)
Tools you'll master
Python LangGraph CrewAI LangChain MCP Vector databases OpenAI & Anthropic APIs Outcomes
What happens after the last module
The course does not end at the capstone. Placement support runs from your first mock interview until you have an offer — and it is support, not a guarantee.
Resume rewrite
Rebuilt around the projects you actually shipped here, targeted at the roles you are applying for.
Mock interviews
Technical and HR rounds with written feedback after each one, repeated until the gaps close.
Profile sharing
Your profile goes to our hiring partners with a note from the trainer who taught you.
Interview scheduling
We book the interviews and follow through until there is a decision either way.
Next step
Ask us anything about Agentic AI
Batch timings, the fee, whether your background fits, what the trainer's day job is — all fair questions. A counsellor will call you back within two business hours.
Python fluency and hands-on experience with LLM APIs. Complete the LLM track first unless you already build with them at work.
The course runs for 6 weeks in classroom & online mode. Weekday, weekend and evening batches are scheduled continuously — call +91 73493 01293 for the next available start date.
Research agent with web and document tools; Multi-agent support triage system; MCP-connected internal operations assistant — plus an end-to-end capstone project you deploy yourself and can walk an interviewer through line by line.
Yes. Sit a full session with the trainer who would actually teach your batch before you decide anything. Ask for one on WhatsApp or through the enquiry form on this page.
Enquiry
Enquire about Agentic AI
Choose WhatsApp or email below. We'll open it with your details already filled in for Agentic AI — you just hit send.
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Also in AI & Advanced Technologies
Compare with the neighbouring tracks
Large Language Models (LLM)
This track treats large language models as engineering material rather than magic. You learn how they are trained, why they fail the way they do, and how to build reliable products on top of something fundamentally probabilistic.
2 months Classroom & Online
View syllabus 6 modules
Retrieval Augmented Generation (RAG)
RAG is the pattern behind almost every enterprise AI deployment: retrieve the right context from your own data, then let the model answer from it. It is also the pattern most teams implement badly, and retrieval quality is almost always the reason.
6 weeks Classroom & Online
View syllabus 6 modules
Next step
Ready to start Agentic AI?
Sit a free demo class before you commit. Ask about batch timings, the trainer, and exactly what the placement support covers.