Agentic AI Mastery.
From Software Engineer to AI Architect.
You already use AI to write software. This course teaches you to write software that is AI.
A live weekend cohort for engineers already shipping with Cursor and Claude Code, going from users of AI tools to builders of the agentic and GenAI systems behind them.
Filtering top-rated ANC earbuds in your budget.
“Sony WF-C700N — best ANC, longest battery.”
Found unguarded token access on line 42.
“Patched. Tests green. Ready to commit.”
Pulling latest benchmarks from primary sources.
“Claude 4.7 leads GPT-5 by 1.8 points.”
Looking up your last 30 days of orders.
“Same as usual? Sending to Blue Tokai.”
Twenty modules. Every one exists for a reason.
Grouped into five levels of increasing AI depth. Each module lists what you’ll actually master and what you’ll build.
Foundation
A Brief History of the Evolution of Intelligence
Before we touch a single API, we walk carefully through the eighty-year arc that brought us here, from the first mathematical model of a neuron to the ChatGPT moment. Every design decision inside a modern LLM is a deliberate answer to a question raised decades ago.
A Perceptron from scratch in NumPy, train it on a linearly-separable problem (Rosenblatt), watch it fail on XOR (Minsky & Papert), then add a hidden layer with backpropagation and watch it succeed.
LLM APIs & Prompt Engineering
The craft of shipping reliable output from LLMs in production. API mechanics across providers, prompt engineering that holds up under load, structured outputs for real data extraction, and the cost patterns you need from day one.
Repo Sage, a CLI that summarises any GitHub repo and answers questions about its architecture.
From Tokens to Generation: How LLMs Actually Work
The inside of an LLM, piece by piece. Tokenisation, embeddings, and attention, built from scratch so nothing remains a black box. By the end of this module, no LLM behaviour will surprise you.
Multi-head causal attention in PyTorch from scratch, then nanoGPT, a decoder-only Transformer, trained on Tiny Shakespeare.
Retrieval-Augmented Generation (RAG)
Give an LLM access to your data without fine-tuning. The full production RAG pipeline, from embeddings to retrieval to evaluation, grounded in the tradeoffs real engineering teams make.
Personal Wiki AI, your own second brain. Markdown notes → hybrid search + AI Q&A with citations. Deployable as a Slack bot or Raycast extension.
Agent Builder
Hacking Claude Code & Cursor as Power Platforms
The extension layer most developers never touch. Build custom skills, hooks, subagents, and workflows on top of the tools you already use daily. By mastering their extension points, you learn agent design by example.
A custom Claude Code workflow that ships a PR from a Linear ticket, using skills + hooks + subagents.
Tool Use & Agent Patterns
The difference between a chatbot and an agent, tool use, reasoning loops, and the pattern decision tree that separates good agents from broken ones. Implemented from scratch so you understand them without framework magic.
ReAct and CodeAct agents from scratch, compared on a real multi-step research task.
Memory Systems for Agents
Give your agents the ability to remember, across turns, across sessions, across users. Build a standalone memory service that any agent can plug into.
Personal Memory Layer, a Mem0-style memory-as-a-service with REST API, multi-tenant storage, and decay logic.
Agentic RAG
Beyond naive retrieve-then-generate. Multi-step retrieval, query decomposition, reranking, and routers across multiple data sources, the RAG patterns real production systems use.
Mini Claude Code Clone, a terminal coding agent (Aider-style) that reads files, edits code, runs tests, iterates on errors, and plugs into the memory layer you built in this level.
Production Engineer
Model Context Protocol (MCP)
The “USB-C for AI” — the protocol every major AI tool now speaks. Build servers, clients, and deploy them to production with the same patterns Anthropic uses internally.
A production MCP server wrapping a real API (Linear / GitHub / Postgres) with auth, logging, and health checks, published to a marketplace.
Multi-Agent Systems
Orchestration patterns for agents working together. Supervisor, debate, consensus, and the framework tradeoffs that separate production-ready from research toys.
A multi-agent code review system (reviewer + fixer + tester) orchestrated with LangGraph.
Knowledge Graphs & GraphRAG
When vector retrieval isn't enough. Extract entities and relationships with LLMs, build your own graph context layer, and combine graph traversal with semantic search.
GraphRAG over your domain, pick a real dataset (legal / medical / codebase / product docs) and build a knowledge graph with LLM Q&A.
Sandboxing, Browser Agents & Secure Execution
LLM-generated code cannot run untethered. The full isolation toolkit (gVisor, Daytona, E2B) and browser agents that run safely inside them.
A browser agent running inside a gVisor/Daytona sandbox that books a flight or scrapes a real site end-to-end.
Evaluating AI Systems & Agents
You cannot improve what you cannot measure. The complete evaluation engineering stack: from designing benchmarks and golden datasets to running automated eval pipelines, LLM-as-judge frameworks, and the continuous eval harness that catches regressions before they reach production.
An end-to-end eval harness for the coding agent you built in Level 2 — golden dataset construction, LLM-as-judge scoring pipeline, CI gate that blocks regressions, and a Braintrust dashboard tracking quality across prompt and model versions.
Production Deployment, Observability & LLM Security
Ship, watch, defend. Agents in production need tracing, guardrails, and red-teaming against the attacks specific to LLM systems, all the failure modes that never show up in development.
Production Coding Assistant (MCP + gVisor + HITL + observability), or Deep Research Clone (long-running citation-backed research agent).
Systems Specialist
Voice & Multimodal Agents
Agents that see, hear, and speak. Build real-time voice agents at sub-200ms latency and computer-use agents that control browsers and desktops.
Voice Tutor Agent, a real-time voice agent for interview prep on a LiveKit + Cartesia stack, at sub-200ms latency.
Training & Fine-Tuning Models
Go inside the model. Understand how models are pre-trained and aligned, then fine-tune open-source models for your own use cases with the techniques production teams actually ship.
LoRA fine-tune of Llama 3 8B on a custom instruction dataset, published to Hugging Face.
Inference Optimisation
Serving is where models go to die (or scale). KV cache, quantisation, Flash Attention, vLLM, the mechanics of fast, cheap inference at production scale.
nanochat from Scratch: Karpathy-inspired full stack (pretrain → SFT → RLHF → serve), end to end. Or Voice + Browser Agent combining Modules 12 & 15.
AI Architect
Reasoning Models & Advanced Architectures
The o1/o3 era. Chain-of-thought, tree-of-thought, test-time compute, and the architectures (MoE, Mamba) that are beginning to replace the classic Transformer.
Side-by-side comparison of CoT vs ToT vs self-consistency on a reasoning benchmark, with cost / quality analysis.
Agent Platforms at Scale
The leap from shipping one agent to running a platform. Orchestration engines, governance, compliance, and team structure at organisational scale.
Personal AI Clone, an agent trained on your writing and voice that answers “as you” (LoRA fine-tune + voice clone + memory layer).
Strategy, Career & Frontier
How AI architects think. Patterns for leading AI strategy at your company, navigating the AI job market, and staying ahead of the frontier as it accelerates.
AI Strategy Pitch + Working Demo, a full AI initiative pitched as if to a CTO, with a working demo of one component.
Aseem Rastogi.
Software & AI Architect · Co-Founder & CTO, Agentcord.ai
Ex-Architect, iXiGo · Ex-Staff Engineer, Synaptic · B.Tech CSE, NIT Hamirpur (Gold Medalist)
With 12+ years of experience shipping production software, Aseem has architected and led the systems behind some of India's highest-traffic consumer platforms, and over the last 3 years has brought that same depth to building production-grade agentic AI for real businesses.
Today, Aseem is building Agentcord.ai on a deliberate two-layer agentic architecture: a deterministic orchestration plane in LangGraph that handles goal setting, context assembly, and agent routing, paired with a Temporal-based autonomous execution engine that runs long-horizon agent workflows surviving failures, retries, and multi-day cycles. Every agent executes inside a gVisor-sandboxed runtime, communicates with the outside world over an MCP-mediated tool plane, composes its behaviour from a custom Skills layer, and is observed continuously by an Agent Evals harness, with the entire platform deployed on EKS.
Before Agentcord, Aseem was the Architect who led the entire Trains & Bus backend at iXiGo for five years, owning its team and its systems end to end. From that period came CrowdSource Running Status, the flywheel that drives organic customer acquisition at iXiGo to this day, built directly with Prashant Ghidiyal, then VP Tech at iXiGo and today the GenAI and Cybersecurity head at Delhivery (and ex-CEO of Devtron Labs). The system ingests tens of millions of GPS and cell-tower events daily through a real-time streaming pipeline on Apache Flink fused with carrier APIs, and powers a real-time delay-prediction model built on ARIMA that set a new bar for running-status accuracy across the industry. It is also the period in which he led the rearchitecture of the entire trains backend, graduating it from a classic microservices setup into a mature, reactive, strongly-consistent service mesh on Kubernetes and Istio. That programme retired static HAProxy edge routing in favour of dynamic, service-aware routing that treated canary and blue-green rollouts as first-class, replaced the previous Kibana-centric logging with an end-to-end NewRelic + Grafana + Loki observability stack, hardened failure isolation through a significantly improved Hystrix-based compartmentalisation layer, and introduced a Redis + ScyllaDB + Aerospike operational data layer tuned for the new latency and throughput profile. It was designed and shipped directly with iXiGo's CTO Rajnish Kumar and Ram Singla, today CEO of Temple at Eternal (the parent company of Zomato). Out of that same rearchitecture came an availability-prediction model that forecasts train-seat availability across routes and classes, shipping today inside the iXiGo train-search experience. From the same years came iXiGo's Travel Graph, a multi-modal A2B search graph on JanusGraph, ScyllaDB, and ElasticSearch that plans a single journey across bus, train, and flight in one query, and a real-time CDC pipeline on Kafka Connect and Debezium that propagated trip and transaction state across the platform.
He then took the entire Core Data Engineering function at Synaptic under his leadership, a 13-person team working directly with Anurag (CTO of the company), where he architected a workflow orchestrator on EKS, Apache Pulsar, JanusGraph, Apache Spark, and Apache Hudi, with Dask handling out-of-core parallel compute, that today runs 10,000+ concurrent pipelines over 10+ TB of data daily, and built out Synaptic's semantic-search and vector-embedding stack on ElasticSearch and ClickhouseDB alongside the Research team.
He recently served as architectural advisor on Squizify (Australia)'s full-stack modernisation and GenAI adoption, helping the world's first AI-powered Food Security Compliance platform evolve its core compliance workflow from a reactive system into a proactive, anticipatory agentic one. Separately, he continues to provide ongoing technical assistance to the Government of Himachal Pradesh's Hydrology Department, where his real-time sensor pipelines and flood-prediction models now run state-wide.
One thread runs through all of it: production depth, shipped at scale, alongside the engineering leaders who set that bar. This is not a course taught from tutorials. It is taught by the architect who built the systems.
Live on weekends. Supported every day.
Live weekend cohort
Saturday and Sunday sessions, with recordings of every class.
Hands-on
Weekly labs and five portfolio-grade capstones across the five levels.
Support
Weekly office hours and capstone reviews per level.
Private Discord community
Dedicated channels per module and topic. Ask anything, any time. Every question and answer lives permanently, becoming a growing knowledge base for the cohort.
The full modern agentic and GenAI toolkit.
Grouped by the problem it solves. Every one of these appears in at least one module or lab.
- A pair of premium running shoes
- Last weekend's bar tab in any metro
- A single IPL playoff ticket
- Your monthly Swiggy bill in a heavy WFH month
- 5 portfolio-grade builds shipped to your GitHub
- The depth most AI engineers in the market don't have
- A cohort and Discord community for life
- The vocabulary to lead AI work at your company
The world is in the middle of something rare. AI is reshaping software faster than any shift since the internet. The engineers who go deep right now will shape what gets built over the next decade.
Doors open for the next cohort soon.
Questions worth asking.
No. The course is built for software engineers who haven't yet touched ML seriously. We start from the Perceptron and backpropagation, then build up through the modern stack. If you can write production code in any language, you can take this.
Get your Discord invite.
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