AceDevHub · Agentic AI
Agentic AI Engineer Program
1 joined
AceDevHub · Agentic AI Engineering
One JavaScript and TypeScript path from model choice and tool calling to RAG, LangGraph orchestration, MCP integrations, evaluation, and production agent systems you can actually operate.
1 developers joined
One connected engineering path
0+
Developers joined
1 developers joined62+
Agent engineering topics
10
Curriculum phases
1
Connected path
Value
Agent content is usually purchased as disconnected demos. This program is designed as one production engineering path.
Bootcamp Route
Typical fragmented AI bootcamp route
Illustrative India bootcamp price bands only — not quotes from any one provider. Voice-agent platforms and ML research tracks are separate products and are intentionally left out.
AceDevHub route
One connected engineering program
Frontend → Backend → Auth → Data → Distributed → Testing → Production
This comparison is about fragmentation cost — not a claim that AceDevHub matches any number above.
Marketplace Route
Piecing it together on Udemy-style catalogs
Typical cart range for similar breadth
₹15,000–₹25,000+
Not one checkout — the sum of sale courses you keep adding.
Plus the hidden cost
The price is only the receipt. This is what you actually pay in time and confusion.
01
Different instructors
02
Repeated introductions
03
Demo-only projects
04
Missing evaluation habits
05
Weak production wiring
The biggest saving is not only money. It is context switching.
Why this course
The problem is not a lack of AI content. It is the lack of one coherent path from demo to operable product.
Impressive agent videos hide brittle failure modes — you never build the habit of catching them before users do.
LangChain, RAG, and MCP arrive as separate trends instead of layers in one shippable agent system.
Techniques float free of Fastify APIs, auth, operator controls, and the JS/TS codebase the agent must live inside.
Observability, cost tracking, and safe rollout show up last — after the architecture should already be operable.
The AceDevHub path
One progression. Each layer unlocks the next.
Course overview
A production-first JavaScript and TypeScript curriculum — not a playlist of disconnected AI demos. Each phase connects to the next so you understand where models, tools, RAG, orchestration, and observability belong in a real product.
Curriculum overview
Ten deliberately ordered phases — frame the problem, call models safely, ground with RAG and tools, orchestrate with LangGraph, evaluate before trust, then ship and operate inside real JavaScript and TypeScript products.
One primary technology per layer — alternatives appear for comparison, not logo tourism.
Curriculum explorer
From problem framing and model choice to RAG, LangGraph, MCP, evaluation, and production observability — one connected path, not a scattered demo playlist.
Phase 01
Frame when an agent belongs in the product and set the JavaScript/TypeScript engineering context agents ship inside.
When agents are the right shape
Problem framing before model or framework choice
Agents vs workflows vs search
Choosing the simplest product shape that works
TypeScript & Node.js runtime
The default AceDevHub agent engineering stack
Zod & schema contracts
Validation shared across tools, APIs, and model output
Environment & config validation
Safe runtime configuration for AI services
Fastify / Next.js product context
Where agent capabilities live in real web systems
Phase 01
Frame when an agent belongs in the product and set the JavaScript/TypeScript engineering context agents ship inside.
When agents are the right shape
Problem framing before model or framework choice
Agents vs workflows vs search
Choosing the simplest product shape that works
TypeScript & Node.js runtime
The default AceDevHub agent engineering stack
Zod & schema contracts
Validation shared across tools, APIs, and model output
Environment & config validation
Safe runtime configuration for AI services
Fastify / Next.js product context
Where agent capabilities live in real web systems
Architecture
Every layer exists because a production agent eventually develops a problem that requires it — UI surfaces, runtime control, grounding, safety, and operability.
Audience
You build web products and want agent capabilities — RAG, tool calling, LangGraph, MCP — wired into Fastify or Next.js systems, not isolated notebooks.
You can ship features end to end and want a coherent path through orchestration, evaluation, operator controls, and production observability.
You are tired of collecting agent demos. You want one curriculum that connects models, grounding, frameworks, eval, and deploy.
Probably not for you if you only want prompt tricks, need a Python-first or ML research track, or want a voice-agent-only product course with no backend engineering depth.
Outcomes
These outcomes describe the capabilities the Agentic AI program is built to develop.
Decide when an agent belongs in the product and when a simpler workflow is enough.
Connect models to real actions with clear contracts instead of free-form guessing.
Ingest documents, retrieve context, rerank results, and keep answers tied to sources.
Design control loops, memory, and recovery so longer tasks do not collapse on the first failure.
Use evaluation habits that catch brittle behavior before users do.
Expose APIs, UI, tracing, and cost controls that engineering teams can maintain after launch.
Differentiation
Typical fragmented AI learning
AceDevHub Agentic AI path
Prompt tips first
Problem framing → models → tools → orchestration → eval
Framework hype cycle
LangGraph spine with deliberate comparison modules
RAG sold as a standalone course
RAG, search, and reranking inside the agent architecture
MCP / browser agents as Twitter threads
MCP, Composio, and Stagehand as integration layers
Demo notebooks and chat UIs
Product APIs, streaming AI UI, auth, and operator controls
Evaluation is optional content
Promptfoo, tracing, and guardrails before production trust
Deploy taught as a final bonus video
OpenTelemetry, cost tracking, Docker, and CI/CD gates
Different course conventions every week
One JS/TS progression from foundations to production
Instructor
Software Engineer & Educator
9+ years of engineering experience
Software engineer and educator focused on practical JavaScript/TypeScript systems — including how agent capabilities fit into real products.
This program focuses on the gaps between AI demos and production systems: tooling contracts, RAG, orchestration, evaluation, product surfaces, and safe iteration.
The goal is not maximum logo coverage. The goal is understanding where each piece belongs in a shippable agent architecture.
Previously
FAQ
One connected agent path
Join the waitlist for a production-first Agentic AI curriculum — models, RAG, LangGraph, MCP, evaluation, and shipping patterns in one JavaScript and TypeScript engineering path.
Join 1 developers already waiting for launch.