AGENTIC AI
ENGINEERING

From prompt user to agent builder. In four weeks you will design, build, and deploy autonomous AI agents that solve real business problems — not just use AI tools, but orchestrate them.
4 WEEKS - INTENSIVE
120 HOURS - LIVE INSTRUCTION 
5 DAYS/WEEK - 6 HOURS/DAY
COHORT - 20 LEARNERS MAX
Build multi-agent systems with LangChain, CrewAI, AutoGen
Master RAG, vector databases and memory architectures
Graduate with a live, deployed AI agent as your portfolio
Deploy production agents with monitoring and guardrails
Implement MCP (Model Context Protocol) in real systems
Live Radar profile visible to 100+ hiring companies

₹49,999

+ 18% GST · Online, live
City center: ₹74,999 · Launch batch: ₹34,999 Sold out
Online - Live
Anywhere in India
₹49,999
Offline - Center
Bangalore and Hyderabad
₹74,999
SELECT A COHORT
5th May 2026
Mon–Fri · 9am–3pm IST
5 Seats Left
2nd June 2026
Mon–Fri · 6pm–9pm IST
10 Seats Left
7th July 2026
Mon–Fri · 9am–3pm IST
Open
ENROLL AND PAY NOW

Built for engineers
who want to lead.

Four weeks.
Zero fluff.

Foundations of Agentic AI
Understand what agents actually are — not the marketing version, the engineering version. Build your first agent by end of Day 2.
01
What agents are — and aren't
Chatbots vs assistants vs agents · The perception-reasoning-action-reflection loop · Real-world agent examples dissected
02
LLM fundamentals for engineers
Tokens, context windows, temperature · OpenAI, Anthropic, Gemini APIs · Structured outputs and JSON mode
03
Prompt engineering for agentic tasks
System prompts · Chain-of-thought · Few-shot prompting · ReAct pattern · Prompt injection defence
04
Tool use and function calling
OpenAI function calling · Anthropic tool use · Building custom tools · Tool result handling
05
MCP — Model Context Protocol
MCP architecture · Building MCP servers · Connecting tools to agents · The future of agent interoperability
Agent Architecture & Memory
Agents without memory are useless. This week you build agents that remember — across sessions, across users, across knowledge bases.
01
Agent memory architectures
In-context memory · External memory · Episodic memory · Semantic memory · Memory retrieval patterns
02
RAG — retrieval augmented generation
Chunking strategies · Embedding models · Similarity search · Hybrid retrieval · Re-ranking
03
Vector databases in production
Pinecone · Weaviate · Chroma · pgvector · Index design · Namespace management · Cost vs performance
04
Planning agents
ReAct · Chain-of-Thought · Tree of Thought · Plan-and-Execute · LangGraph state machines
05
Agent frameworks deep dive
LangChain · LangGraph · CrewAI · OpenAI Agents SDK · When to use which — and when to build from scratch
Multi-Agent Systems
One agent is a tool. Multiple agents working together is a system. This week you design and orchestrate agent teams that tackle complex, multi-step problems.
01
Orchestrator–subagent architecture
Hierarchical agent design · Delegation patterns · Task decomposition · Result aggregation
02
Agent communication protocols
Message passing · Shared state · Handoff design · Conflict resolution between agents
03
Parallelisation and efficiency
Parallel task execution · Async agent patterns · Token budget management · Cost-aware orchestration
04
Human-in-the-loop design
Interrupt patterns · Approval gates · Escalation logic · Keeping humans in control without killing autonomy
05
Agent evaluation and red-teaming
Evals for agents · Adversarial testing · Failure mode mapping · LLM-as-judge patterns
Deploy, Monitor & Scale
Building agents in a notebook is easy. Keeping them alive in production is the hard part. This week you learn what enterprise AI engineering actually looks like.
01
Production deployment patterns
FastAPI · Docker · Cloud deployment (AWS/GCP/Azure) · Serverless agent patterns · Edge deployment
02
Observability for AI systems
LangSmith · Langfuse · Tracing · Logging · Latency dashboards · Token spend monitoring
03
Security and guardrails
Prompt injection defence · Output validation · Content filtering · PII detection · Safe tool execution sandboxing
04
Cost and latency optimisation
Model selection by task · Caching strategies · Prompt compression · Batch processing · Inference routing
05
Capstone presentations
Cohort demo day · Live Radar profile activation · RED certification assessment · Career strategy session

Companies see you before you
graduate.

Every learner enrolled in an RED track has a Live Radar profile. As you progress through the curriculum — completing projects, scoring assessments, building your portfolio — your profile updates in real time. Companies registered on RED's Live Radar feed can see anonymised learner signals: skill tags, project scores, portfolio links, and track progress. When they see someone they want, they reach out. Before you finish. Before you apply anywhere.This isn't a job board. There are no applications. Companies come to you — based on what you're actually building, not what your CV says.

Live Radar · Agentic AI Engineering · Batch May 2026
AK
Learner CH-2-001
Multi-agent · RAG · LangGraph
Progress
WK4
RJ
Learner CH-2-005
MCP · Tool use · Deployment
Progress
WK4
MK
Learner CH-2-003
RAG · Vector DBs · FastAPI
Progress
WK4
TN
Learner CH-2-007
LangChain · CrewAI · Agents SDK
Progress
WK4
TS
Learner CH-2-006
Multi-agent · RAG · LangGraph
Progress
WK4

Here's What our Students Have to Say!

Read All the Stories
RED Save my Job!

I'd been a backend developer for nine years — Java, Spring Boot, enterprise APIs. Last year I started seeing job descriptions ask for AI engineering skills I didn't have. I enrolled in the Agentic AI Engineering track half-convinced it was too late. Four weeks later I had a deployed multi-agent system in my portfolio. Within three weeks of graduating, a GCC in Hyderabad reached out through Live Radar. I joined at a 40% salary jump. RED didn't just save my job — it upgraded it.

Ravi Narayan
AI Engineer, Global Capability Centre · Hyderabad
I Have a Stable Job Now!

I finished my B.Tech in 2024 and spent eight months applying to jobs with nothing to show for it. My degree had a two-line mention of machine learning — nothing applied, nothing current. A friend told me about RED's launch batch pricing. I enrolled in AI Ops Engineering. The four weeks were the hardest I've worked in my life. But I graduated with three live projects and a Live Radar profile. A startup in Bengaluru offered me a role before my batch even ended. First salary: ₹11 LPA. I'd been applying for ₹4 LPA roles before.

Anjali Krishnamurthy
MLOps Engineer, AI Startup · Bengaluru
I Understand AI Now!

I'm a VP at a mid-size manufacturing company. For two years I've been sitting in board meetings nodding at AI presentations I didn't fully understand — approving budgets I couldn't evaluate. My team knew it. My vendors definitely knew it. I did the AI for Business Leaders track on evenings, without taking a day off work. By Week 2 I was already asking better questions in vendor calls. My capstone AI strategy document is now our actual company roadmap for FY27. I don't nod anymore. I lead the conversation.

Suresh Malhotra
VP Operations, Manufacturing Group · Delhi
I Have Got New AI Business Ideas! 

I run a chain of diagnostic labs across Telangana — 14 centres, 200 staff. I did RED's AI for Professionals track because I wanted to use AI in our workflows, not just hear about it at conferences. What I didn't expect was that by Week 3 I'd have three completely new business ideas I'd never considered. AI-assisted radiology report triaging. A WhatsApp-based patient follow-up agent. An internal knowledge system for our lab technicians. I'm building one of them right now with a developer I found through the RED alumni network.

Padmaja Reddy
Founder, Diagnostic Lab Network · Hyderabad