Agentic AI for Developers & Architects
How Enterprise AI Agents Actually Work
Move beyond AI-agent buzzwords and understand how real Agentic AI systems are designed—from RAG and tools to reasoning, state, human approval and evaluation.
You may know the pieces. How do they fit together?
LLMs, RAG, tools, agents, LangChain and LangGraph are easier to learn individually than to combine into the right enterprise architecture.
- Do I actually need an AI Agent?
- Should this be RAG, a workflow or an Agentic system?
- Where do tools, state and memory fit?
- Which decisions should the LLM make?
- Which actions require human approval?
- What changes in production?
What you will be able to do
Choose the right pattern
Distinguish chatbots, RAG, deterministic workflows, agents and multi-agent systems.
Design the architecture
Place RAG, tools, state, orchestration and human approval in the correct roles.
Think like an architect
Use the least autonomous architecture that reliably solves the business problem.
See agent behavior
Walk through a live enterprise incident investigation using Agent → Tool → Observation steps.
Apply the framework
Complete an enterprise architecture challenge using the BEETS Architecture Canvas.
Recognize production gaps
Understand security, HITL, evaluation, observability, cost, latency and failure handling.
Built for experienced technology professionals
- Software Developers
- Senior Developers
- Technical Leads
- Solution Architects
- AI / ML Practitioners
- Engineering Managers
A mass-market beginner bootcamp
- A beginner introduction to ChatGPT
- A “100 AI tools” session
- Prompt-engineering-only training
- A coding bootcamp
- Placement-oriented training
- Framework syntax without architecture understanding
2.5 hours of architecture, demonstration and application
When does AI become Agentic?
Chatbot → RAG → Workflow → Agent → Agentic System.Understand what changes as autonomy increases.
Anatomy of an Enterprise Agent
Goal, context, RAG, tools, state, orchestration,human-in-the-loop, guardrails and evaluation.
Architecture Decision Framework
Decide when to use RAG, a deterministic workflow,an AI Agent or potentially a multi-agent system.
Live IncidentOps Demonstration
Watch an enterprise incident agent investigate using toolsand runbooks, update its understanding, and stop for humanapproval before a sensitive production change.
Participant Architecture Challenge
Design an enterprise support agent using RAG, tools,state, deterministic policy controls and human escalation.
What Changes in Production?
Security, authorization, prompt injection, tool safety,evaluation, observability, latency, cost and failure handling.
Consolidation & Practical Takeaways
Bring the concepts together into a reusable architecturedecision model for future Agentic AI solutions.
See agent behavior, not just a diagram
Bhartendu Sharma
Founder, BEETS Academy
Enterprise AI Trainer & GenAI Practitioner
17+ years of experience in enterprise technology, with hands-on specialization in AI/ML, Generative AI, RAG, AI Agents and Agentic AI. The approach is architecture-first: understand the business problem, choose the right AI pattern, design the architecture, understand implementation, evaluate trade-offs, and consider production realities.
Before you register
Do I need to know Python?
No participant coding is required. The workshop focuses on architecture, reasoning, demonstrations and design.
Will you teach LangChain or LangGraph?
We explain where orchestration frameworks fit, but this is not a framework-syntax tutorial.
Is this suitable for complete beginners?
It is best for technology professionals who already understand basic Generative AI or ChatGPT concepts.
Can my company arrange a private version?
Yes. Enterprise versions can include team baseline assessment, company-relevant scenarios, customized exercises and a post-training capability report.
Join the founding workshop
• IST • Maximum 20 participants
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