Client: one of the world's largest electronics manufacturers (name withheld under confidentiality). Location: on-site, Noida / Delhi NCR, India. Format: 4-day hands-on intensive, condensed from a 13-day / 39-hour curriculum, 80–90% hands-on. Audience: Python, .NET, and Java developers, QA / test engineers, DevOps & SRE, data & BI analysts, solution architects, and product engineers / tech leads.
What Was Covered
Four modules — from product thinking to a board-ready multi-agent demo
1 · Product Thinking & Agent Foundations
Identifying agent-worthy problems, decomposing workflows and bottlenecks, MVP framing, agent cognition (observe–think–decide–act), role-based agent separation (planner, executor, retriever, validator, escalation), and production prompt contracts, tool strategy, and governance.
2 · Single-Agent Engineering
First end-to-end agent pipeline; session and persistent memory, context pruning and token optimisation; domain-specific customisation with tool wiring, edge-case validation, and confidence scoring.
3 · RAG & Intelligence Engineering
Document intelligence and RAG (ingestion, chunking, vector indexing, evidence-backed answers); a CI/CD interpretation agent; a root-cause-analysis agent; and an enterprise knowledge assistant over runbooks and incident history.
4 · Multi-Agent Productization
Multi-agent orchestration (planner, executors, validator, arbitration); reliability, observability, cost tracking, and human-in-the-loop governance; product hardening and an executive-ready demo with an ROI narrative.
Stack & Engineering Environment
Azure OpenAI / OpenAI (GPT-4 family, function calling) · LangGraph (primary orchestration) · Microsoft Autogen · VS Code / Cursor · Python 3.10+ · GitHub (one repo per participant) · Postman · FAISS / Chroma vector stores · CI/CD and ops data (GitHub Actions, Jenkins logs, incident runbooks). .NET engineers focused on agent design, prompts, and orchestration — using Python only as an agent runtime and integrating via REST APIs, JSON schemas, and Web API endpoints into ASP.NET / Blazor.
Outcome
Participants left able to independently conceptualise, architect, build, test, iterate, and present enterprise-grade agentic AI solutions — each owning a complete product prototype plus reusable artefacts: a Problem Discovery Canvas, Agent Role Matrix, Decision Architecture Blueprint, a versioned prompt repository with output-validation templates, and a packaged capstone with architecture diagrams and an ROI story.
Related
← All Past Trainings
The full anonymized track record of delivered corporate AI trainings.
Agentic AI Training →
Book this as a 1:1 or team program — LangGraph, tools, memory, guardrails, evals.
LangGraph Training →
Stateful, graph-based agents in depth.
RAG Training →
Production retrieval-augmented generation.