AI Stack · Python → GenAI → Agentic → Coding Agent · Class 014 Enrolling Now

AI Engineer + Coding Agent

Master the frontier — from Large Language Models to autonomous AI agents. A focused AI Engineer programme covering Python, FastAPI, PostgreSQL, frontier models, prompt and context engineering, RAG and vector databases, agentic frameworks and the Model Context Protocol — culminating in a deployed AI Coding Agent project.

modules

4.7/5

class rating

100k+

enrolled

1,000+

hiring partners

32+

GenAI & agentic tools

Where our AI Engineer alumni work

Microsoft

Amazon

SalesforceAI Engineer

Deloitte

Infosys

Accenture

TCS

Wipro

Capgemini

Cognizant

HCL

Microsoft

Amazon

SalesforceAI Engineer

Deloitte

Infosys

Accenture

TCS

Wipro

Capgemini

Cognizant

HCL

Direct answer

What is an AI Engineer — and why build the whole stack?

An AI Engineer designs, builds and operates software systems around frontier models — production APIs, retrieval pipelines and autonomous agents that plan, use tools and act. It sits on top of solid engineering: Python and SQL for data, FastAPI for services, then LLMs, RAG and agentic frameworks on top. This programme builds the stack in that order, so the Coding Agent you ship at the end is the sum of what you learned, not a demo bolted on.

The four layers of the AI Engineer stackEach layer builds on the last — you learn them in order.

01ENGINEERING

•Python 3.12+, async and type hints
•PostgreSQL 16+, advanced SQL, PL/pgSQL
•NumPy, pandas and API clients
•Testing, packaging and project structure

02SERVICES

•FastAPI with Pydantic validation
•SQLAlchemy ORM and Alembic migrations
•JWT authentication and OAuth2 flows
•Role-based access control

03GENERATIVE

•Transformer internals and frontier models
•Prompt and context engineering
•LLM APIs in production
•Embeddings, vector databases and RAG

04AGENTIC

•LangGraph, Claude Agent SDK, CrewAI, Pydantic AI
•Model Context Protocol and A2A
•Agent design patterns and observability
•Final project: the AI Coding Agent

Agentic AI landscape · 2026

Models learned to act. The jobs followed.

Agent frameworks became the defaultLangGraph · CrewAI

LangGraph 1.0 is the production default for stateful agents, Claude Agent SDK carries the deepest MCP integration, and CrewAI runs multi-agent crews. Engineers who can pick and wire them are hired first.

MCP is the integration standard200+ servers

The Model Context Protocol — now Linux Foundation-stewarded — exposes code repositories, test suites and execution environments to agents through one open standard.

RAG matured into a production disciplineHybrid search

Chunking, embeddings, vector databases, hybrid search with re-ranking and agentic RAG with self-improving retrieval replaced single-shot prompting.

Enterprise agent platforms went liveAgentforce · ServiceNow

Salesforce Agentforce, ServiceNow and enterprise HCM AI put agents inside the systems businesses already run, creating demand for engineers who can extend them.

Observability and evals are table stakesLangSmith · Arize

Production agents ship with tracing, evaluation harnesses, guardrails and cost controls. Reliability engineering for agents is a role in its own right.

Coding agents changed the SDLCAI-native teams

Agents now plan, write, test and refactor code. Teams want engineers who build those agents, not only engineers who use them.

Who should join

Built for engineers who want to ship AI systems, not call APIs.

Software engineers moving into AIBackend & full-stack developersData analysts & data engineersFresh graduates with basic programmingQA & automation engineersProduct & solution engineers adding agents

Prior experience: basic programming logic. Section 1 rebuilds the application lifecycle, computing and data fundamentals; Python and SQL are taught from first principles before any model work begins.

What you will be able to do

Take an idea from prompt to a deployed, observable agent.

Engineer production PythonAsync, type hints, OOP, packaging and tested code with NumPy, pandas and API clients.

Model and serve dataPostgreSQL schemas, advanced SQL, PL/pgSQL and FastAPI services with JWT auth and SQLAlchemy.

Work with frontier modelsGPT, Claude, Gemini, Llama and DeepSeek — prompt and context engineering with LLM APIs in production.

Build production RAGEmbeddings, vector databases, hybrid search with re-ranking and agentic RAG with self-improving retrieval.

Design multi-agent systemsLangGraph state machines, Claude Agent SDK, CrewAI crews and Pydantic AI, wired through MCP and A2A.

Operate agents in productionLangSmith and Arize observability, evaluation harnesses, guardrails, deployment and a public verification URL.

Course curriculum

Eight sections. 53 modules. Python → FastAPI → GenAI → Agentic AI.

Fundamentals of IT & AI

Foundations5 modules

Foundational track building the conceptual bedrock every AI engineer needs — application lifecycle, Agile/Scrum, computing infrastructure, and AI/ML/Generative/Agentic AI fundamentals.

Modules & topics

•1.1 Application Lifecycle ManagementTHEORY

Application fundamentals — types, web architecture · Web Technologies — Frontend (HTML, CSS, JavaScript, React) and Backend (Python, Java, Node.js) · Database Systems — SQL (PostgreSQL, MySQL) and NoSQL (MongoDB) · The seven SDLC phases — Planning, Analysis, Design, Implementation, Testing, Deployment, Maintenance · AI Engineers sit at the seam of code, data, and AI — knowing how applications work makes you a better AI system architect

•1.2 Agile & Scrum FrameworkTHEORY

Methodology Evolution — Waterfall vs Agile · Scrum Roles, Events, Artifacts, User Stories · Estimating with story points · Backlog management with Azure Boards, JIRA

•1.3 Computing & DataTHEORY

CPU vs GPU — when each matters for AI workloads · Cloud Service Models — IaaS, PaaS, SaaS · Public, private, hybrid cloud · GPU economics for AI engineering — when to use cloud GPU, when to call APIs

•1.4 Introduction to AI, Generative AI & Agentic AIAI

AI is reshaping software engineering — from AI-generated code to autonomous agents to production AI services · AI Fundamentals — what AI is, how AI systems work · Machine Learning — algorithms that improve through experience · Deep Learning — neural networks for complex pattern recognition · Generative AI — systems that create text, images, code based on learned patterns · Large Language Models — process and generate human-like text · Image Generation — AI models creating original images from text · Agentic AI — Plan / Reason / Act / Learn loops, tool use

•1.5 Real-World ApplicationsTHEORY

AI in production — chatbots, code assistants, content generation · AI agents in modern workflows · Industry applications — fintech, healthcare, retail, education · Domain depth multiplies your AI Engineer salary

Format

5 modules · 4 theory · 0 hands-on · 1 AI deep dive. Live mentor-led sessions with guided practice.

Section 1 of 8

Prepares you for section 2: Python for AI & Data.

You ship

SDLC mapAgile boardAI landscape brief

Python for AI & Data

Foundations10 modules

The dominant language for AI engineering. Ten modules from environment setup through advanced OOP — the language fluency that powers every AI engineering job.

Modules & topics

•2.1 Python FundamentalsHANDS-ON

Python interpreter installation for Windows and Mac · Visual Studio Code IDE configuration for AI engineering · Python's 35 essential keywords · Variables and memory management · Data types, operators, type conversion · Control flow — if/elif/else, while, for, match-case, break/continue/pass

•2.2 String ManipulationHANDS-ON

String fundamentals — indexing, slicing, concatenation · f-strings and .format() for AI prompt construction · String methods — case, search, trimming, replacement · .split() and .join() for text data preprocessing · Critical for parsing LLM responses and constructing prompts

•2.3 Data Structures — Lists & TuplesHANDS-ON

Lists — creation, indexing, slicing, modification · List comprehensions for elegant data transformation · Sorting, reversing, copying patterns · Tuples — immutable sequences with packing and unpacking · Performance advantages over lists · Use cases for immutability in AI applications

•2.4 Data Structures — Dictionaries & SetsHANDS-ON

Dictionaries — creation, access, operations · Dictionary comprehensions · Nested dictionaries for structured data · Essential for representing LLM API requests/responses and agent state · Sets and the UUU properties (Unique, Unordered, Unindexed) · Mathematical operations — union, intersection, difference · Applications in deduplication and set algebra

•2.5 Advanced Collections & IteratorsHANDS-ON

Collections module — namedtuple, Counter, defaultdict, deque · Iteration protocol enabling custom iterators · Generators using yield for memory-efficient streaming — essential for processing large LLM responses · Generator expressions and pipelines · Lambda functions for anonymous functions · Higher-order functions — map(), filter(), reduce()

•2.6 FunctionsHANDS-ON

Function definition, parameters, return values · Default arguments, *args, **kwargs · Variable scope (LEGB rule) · First-class functions and higher-order patterns · Recursion and recursive design patterns · Type hints (Python 3.5+) — essential for self-documenting AI code · Documenting functions with docstrings (PEP 257)

•2.7 Modules & PackagesHANDS-ON

Built-in modules, user-defined modules, packages · pip for package management · requirements.txt for reproducible builds · Virtual environments for isolated AI projects · Reproducibility is critical when AI dependencies change weekly

•2.8 Working with Data FormatsHANDS-ON

CRUD operations with open() · File modes and pathlib · Python's csv module — reader, writer, DictReader, DictWriter · JSON operations — dump(), dumps(), load(), loads() · JSON is the language of AI APIs — master it cold

•2.9 Advanced Python ConceptsHANDS-ON

Exception Handling — robust error handling for unreliable LLM APIs, retry patterns · Decorators — for logging API calls, timing, caching, rate limiting AI calls · Generators deep dive — memory efficiency for streaming LLM responses · Context Managers — proper resource management for DB connections, file handles, async clients · Four patterns that separate scripting Python from production AI engineering

•2.10 Object-Oriented ProgrammingHANDS-ON

Classes & Objects, Methods, Special Methods · Instance vs class variables · Encapsulation — access modifiers control data visibility · Inheritance — single, multi-level, and multiple inheritance with super() and method overriding · Abstraction — abstract classes and methods (from abc module) · Polymorphism — method overriding and duck typing · Pydantic BaseModel, LangChain BaseModel — OOP underpins every modern Python AI library

Format

10 modules · 0 theory · 10 hands-on. Live mentor-led sessions with guided practice.

Section 2 of 8

Prepares you for section 3: SQL for AI & Data.

You ship

Python 3.12 codebaseData structuresOOP modules

SQL for AI & Data

Data layer5 modules

The data backbone of AI applications. Five modules covering PostgreSQL from foundations through programming with PL/pgSQL — the data layer that powers your AI services and your pgvector RAG implementations.

Modules & topics

•3.1 Foundations of Databases & PostgreSQLTHEORY

Databases, DBMS, RDBMS — concepts and terminology · ACID properties — Atomicity, Consistency, Isolation, Durability · PostgreSQL setup, psql, pgAdmin 4, DBeaver · Data types — numeric, character, date/time, boolean, JSON, arrays · Constraints — PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, CHECK, DEFAULT

•3.2 QueryingHANDS-ON

SELECT statements and column projection · WHERE clauses with operators and conditions · Built-in functions — string, numeric, date, conditional · Aggregates — COUNT, SUM, AVG, MIN, MAX · GROUP BY and HAVING · Window functions — ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD · JOIN operations — INNER, LEFT, RIGHT, FULL OUTER, CROSS, SELF

•3.3 Advanced QueriesHANDS-ON

Subqueries — scalar, row, table subqueries · CTEs (Common Table Expressions) — readable analytical queries · Recursive CTEs for hierarchical data · Set operators — UNION, UNION ALL, INTERSECT, EXCEPT · DML — INSERT, UPDATE, DELETE patterns · Transactions — BEGIN, COMMIT, ROLLBACK

•3.4 ProgrammingHANDS-ON

ALTER TABLE for schema evolution · Indexes — B-tree, Hash, GiST, GIN · Views — virtual tables, materialized views · Stored functions with CREATE FUNCTION · PL/pgSQL — variables, control structures, exception handling · Triggers — automation for data quality

•3.5 Design & OptimizationTHEORY

ER modelling, normalization (1NF, 2NF, 3NF) · OLTP vs analytics workload patterns · pgvector extension — vector similarity search inside PostgreSQL (preview of Section 7 RAG) · Query plan analysis with EXPLAIN and EXPLAIN ANALYZE · Index strategies — selectivity, covering indexes, multi-column indexes · VACUUM, ANALYZE, partitioning · AI engineers read query plans daily — invest in EXPLAIN ANALYZE fluency

Format

5 modules · 2 theory · 3 hands-on. Live mentor-led sessions with guided practice.

Section 3 of 8

Prepares you for section 4: Python Libraries for AI.

You ship

PostgreSQL schemaAdvanced SQLPL/pgSQL functions

Python Libraries for AI

Data layer4 modules

Essential Python libraries every AI engineer needs daily. Four modules covering the core data manipulation and HTTP libraries — lighter than the full Data Science Python Libraries track, focused on AI engineering essentials.

Modules & topics

•4.1 NumPy for AI EngineersHANDS-ON

N-dimensional arrays — the foundation of numerical Python · Array creation, indexing, slicing · Vectorised operations and broadcasting · Why vectorisation is 100x faster than Python loops · Linear algebra basics with np.linalg · Embedding vectors are NumPy arrays — fluency matters

•4.2 Pandas for AI EngineersHANDS-ON

Series and DataFrame structures · Data loading — read_csv, read_excel, read_sql, read_json · Data cleaning — handling missing data, duplicates, type conversion · groupby operations — the split-apply-combine pattern · Merging, joining, reshaping · Time series basics · Use Pandas for data prep before AI training/inference, not for production AI pipelines

•4.3 HTTP & API ClientsHANDS-ON

requests library — HTTP for humans · GET, POST, PUT, DELETE patterns · Headers, authentication, sessions · Error handling and retries with tenacity · httpx — modern async HTTP client (preview of Section 6 FastAPI) · Essential for calling external AI APIs

•4.4 Common AI UtilitiesHANDS-ON

pydantic — data validation and settings management (preview of FastAPI) · python-dotenv — environment variable management for API keys · loguru — better logging for AI applications · rich — pretty terminal output for AI agent debugging · tiktoken — OpenAI tokeniser for cost estimation

Format

4 modules · 0 theory · 4 hands-on. Live mentor-led sessions with guided practice.

Section 4 of 8

Prepares you for section 5: Advanced Python Concepts.

You ship

NumPy pipelinespandas analysisAPI client

Advanced Python Concepts

Engineering4 modules

Production-grade Python patterns for AI engineering. Four modules covering async, packaging, testing, and the engineering practices that separate scripts from systems.

Modules & topics

•5.1 Async/Await Deep DiveHANDS-ON

asyncio fundamentals — event loops, coroutines, tasks · async def and await patterns · Async context managers with async with · Async iterators with async for · asyncio.gather() for concurrent calls · httpx.AsyncClient for concurrent HTTP/AI API calls · Async is essential for AI engineering — every LLM call is I/O-bound

•5.2 Type Hints & Static AnalysisHANDS-ON

PEP 484 type hints · Generic types — List[int], Dict[str, Any], Optional[X], Union[X, Y] · typing module — Callable, Protocol, TypeVar, Generic · mypy for static type checking · Pyright / Pylance for IDE integration · Type hints are non-negotiable in production AI codebases

•5.3 Packaging & Project StructureHANDS-ON

pyproject.toml — modern Python packaging · Building packages with uv / poetry / hatch · Project structure best practices for AI applications · Distribution patterns — PyPI, internal package indexes · Reusable AI components belong in packages, not copy-paste scripts

•5.4 Testing AI ApplicationsHANDS-ON

pytest fundamentals — fixtures, parametrize, markers · Mocking LLM calls with pytest-mock · Recording and replaying API calls with vcr.py / pytest-vcr · Async testing with pytest-asyncio · Coverage with pytest-cov · Testing AI applications requires mocking the non-deterministic AI calls — invest in this pattern

Format

4 modules · 0 theory · 4 hands-on. Live mentor-led sessions with guided practice.

Section 5 of 8

Prepares you for section 6: Modern Python Framework FastAPI.

You ship

Async servicesTyped packageTest suite

Modern Python Framework FastAPI

Services5 modules

FastAPI represents the next generation of Python web frameworks — combining speed, modern Python features, and automatic documentation. Built on Starlette and Pydantic, it delivers exceptional performance through asynchronous capabilities. Five modules taking you from FastAPI fundamentals through production-grade authentication and database integration.

Modules & topics

•6.1 FastAPI Fundamentals & SetupHANDS-ON

Environment Setup — create virtual environment, install FastAPI with Uvicorn ASGI server · First Endpoint — define path operations using decorators: @app.get, @app.post, @app.put, @app.delete · Run Server — launch development server with automatic reload for rapid iteration · 3x Performance — FastAPI matches Node.js and Go performance · 100% Type Safety — complete type hints enable automatic validation, serialisation, and IDE support · Auto Documentation — Swagger UI and ReDoc generated automatically from code annotations

•6.2 Parameters & ValidationHANDS-ON

Path parameters with type hints · Query parameters with defaults and Pydantic validation · Pydantic models for request bodies — automatic validation · Response models with response_model · Status codes and HTTPException patterns · Pydantic is FastAPI's killer feature — validation for free

•6.3 Database IntegrationHANDS-ON

SQLAlchemy ORM with dependency injection · Database session management patterns · CRUD operations through repository pattern · Alembic migrations — schema evolution for production · Async database access patterns · The production data layer for FastAPI services

•6.4 Project StructureHANDS-ON

APIRouter — organises code into modular components · Repository pattern — separates database logic from API logic · Environment variables for configuration (python-dotenv + pydantic-settings) · Layered architecture — routes → services → repositories → models · Docker containerisation for FastAPI · Production projects scale with proper structure — invest here

•6.5 Authentication & AuthorisationHANDS-ON

Bcrypt hashing — industry-standard password encryption · Salt and pepper patterns · Password complexity policies · JSON Web Tokens with expiration enable stateless authentication · Token claims (sub, exp, iat, aud) · Refresh token patterns · OAuth2PasswordBearer dependency verifies tokens · Extract current user information · Multiple OAuth2 flows · Authentication dependencies secure endpoints · Role-based access control (RBAC) manages permissions · Permission decorators · API key authentication alternatives · Section Project — build and deploy a production FastAPI service with PostgreSQL backend, JWT auth, role-based access control, Pydantic validation, async endpoints, and full Swagger documentation

Format

5 modules · 0 theory · 5 hands-on. Live mentor-led sessions with guided practice.

Section 6 of 8

Prepares you for section 7: Generative AI Deep Dive.

You ship

FastAPI serviceSQLAlchemy modelsJWT auth

Generative AI Deep Dive

Generative AI10 modules

Master the frontier — from Large Language Models to autonomous AI agents. Modules covering the foundations of GenAI and Agentic AI, the 2026 frontier model landscape, prompt engineering and context engineering, generative content across every modality, no-code AI workflows, the SDLC walk-through with AI agents, enterprise AI agent platforms (ServiceNow, Salesforce Agentforce, enterprise HCM AI), LLM APIs in production, embeddings and vector databases, and production RAG pipelines — the depth that distinguishes AI Engineers from API callers.

Modules & topics

•7.1 Foundations of GenAI & Agentic AIAI

Narrow AI — image classifiers, speech recognition, pre-2022 era of task-specific intelligence · Generative AI — LLMs, image/video/audio generation, post-2022 era unleashed by ChatGPT · Agentic AI — Plan / Reason / Act / Learn loops, tool use, the post-2024 era of autonomous systems · 2022 inflection point — ChatGPT launch enters mainstream professional workflows · 2024 inflection point — AI systems begin planning, using tools, completing multi-step tasks autonomously · What's coming 2026-2030 — increasingly capable reasoning models, deeper tool integration · Multi-agent collaboration at scale and continuous learning from real-world feedback · LLM internals — Transformer architecture, attention mechanism, tokenisation · Frontier model landscape — GPT, Claude, Gemini, Llama, DeepSeek · Agent architecture — perceive, reason, act, learn loops · AI safety — alignment, hallucination, prompt injection · Workstation setup — Python, Jupyter, VS Code, API keys, billing

•7.2 Mastering the Frontier ModelsAI

GPT-5.5 — The Autonomous Agent. First fully retrained base since GPT-4.5. Natively omnimodal · GPT-5.5 — Terminal-Bench 2.0 leader at 82.7%, OSWorld-Verified at 78.7% · GPT-5.5 best for autonomous agents, computer use, terminal automation, multi-tool orchestration · GPT-5.5 — 40% token efficiency gain over GPT-5.4 on Codex tasks. Pricing: $5/$30 standard, $30/$180 Pro · Claude Opus 4.7 — The Precision Coder. Hybrid reasoning model with extended thinking mode · Claude Opus 4.7 — SWE-bench Pro leader at 64.3%, lowest hallucination rate on long-form work at 36% · Claude Opus 4.7 — deepest native MCP support of any frontier model. Pricing: $5/$25 (17% cheaper than GPT-5.5 on output) · Gemini 3.1 Pro — The Context Giant. Natively multimodal with 2M+ token context window — largest of any frontier model · Gemini 3.1 Pro — GPQA Diamond leader at 94.3%, ARC-AGI-2 leader at 77.1%. Three thinking levels: low/medium/high · Gemini 3.1 Pro best for high-volume batch jobs, multimodal media analysis, cost-sensitive workloads. Pricing: $2/$12 · Llama 4 (Meta) — strong general-purpose open-weights · DeepSeek — reasoning-focused, low cost · Mistral — European AI, GDPR-aligned, strong multilingual · Qwen — Alibaba's frontier-competitive open-weights · Intelligent Routing — Opus 4.7 as daily driver for writing and code review · GPT-5.5 for autonomous agents and terminal tasks; Gemini 3.1 Pro for cost-sensitive and multimodal work · Mini/nano sub-models for high-volume orchestration · Microsoft Copilot Suite — Word, Excel, PowerPoint, Outlook, Teams integration · Copilot Studio — enterprise no-code agent building · Perplexity — citation-grounded AI search · NotebookLM — long-document analysis with audio overview · ChatGPT Codex — agentic coding environment

•7.3 Prompt Engineering & Context EngineeringAI

Zero-shot prompting — instruction-only patterns · Few-shot prompting — example-driven prompts · Chain-of-Thought (CoT) — step-by-step reasoning · ReAct (Reasoning + Acting) — interleaved thought and action · Tree-of-Thought — exploring multiple reasoning paths · System prompts — persona and constraint design · Output formatting — JSON, XML, structured outputs · Prompt chaining — multi-step LLM workflows · Context Engineering — the 2026 frontier discipline; what separates it from prompt engineering · Context window optimisation — what to include, what to summarise, what to retrieve · Memory patterns — short-term, long-term, episodic · Tool result formatting for agent context · Domain prompts for AI Engineering work — API design reviews, code refactoring, debugging · Project — build a 30+ portfolio prompt library across zero-shot, few-shot, CoT, ReAct, Tree-of-Thought patterns

•7.4 Generative Content Across Every ModalityAI

AI Image Generation — DALL-E 3, Midjourney v7, Stable Diffusion XL, Flux as the 2026 frontier of open image generation · Prompt structure for images — subject + style + composition + lighting + medium · AI Voice & Audio — ElevenLabs for voice cloning, multilingual synthesis, emotion control; Whisper / Deepgram speech-to-text; Suno / Udio music generation · Ethical considerations — voice cloning consent, watermarking, disclosure obligations · AI Video Generation — Sora 2 (OpenAI), Runway Gen-4 for text-to-video and motion control, Pika and Kling as alternatives · AI for Marketing — copywriting for ads, emails, landing pages, social posts; SEO content; brand voice consistency · Module Capstone — produce a coordinated multi-asset marketing package entirely with AI: hero image + headline + body copy + voiceover + short-form video, with prompts and process documented for portfolio

•7.5 No-Code AI Workflows & Building Your First AgentsAI

ChatGPT Custom GPTs — name, description, instructions, knowledge files; Actions via OpenAPI for light agentic capabilities · Claude Projects — long-context document workflows leveraging Opus 4.7's massive context window; persistent project memory · Microsoft Copilot Studio — enterprise no-code agent platform; topics, triggers, conversation flows; publish to Teams, websites, SharePoint · Workflow Automation — Make.com (visual scenario builder), n8n (open-source self-hostable), Zapier (broadest integration ecosystem) · Connect ChatGPT / Claude / Gemini to Gmail, Sheets, Slack, Notion, CRMs — build AI-powered email triage and content generation pipelines · Other enterprise no-code platforms — IBM watsonx Orchestrate, UiPath AI Agents, Google Vertex AI Agent Builder · Module Capstone — ship a working Make.com or n8n AI workflow

•7.6 Real-World SDLC Walk-Through with AI AgentsAI

Ten stages of the software development lifecycle, each augmented by purpose-built AI agents — with documented time savings at every stage · Key productivity statistics — 41% of code AI-generated globally in 2026, 77% of QA teams using AI, 57% of orgs running AI in production · Developer productivity gains — 26% more weekly tasks, 13% more commits, 38% more successful first compiles · The synthesis — every role becomes more productive, more strategic, more high-leverage; "where would we plug an AI agent, what would it do, what would it save us" · Portfolio Capstone — pick a domain you know, walk through all 10 SDLC stages with AI agents, document the time savings, and ship the analysis as a portfolio piece

•7.7 Enterprise AI Agent Platforms ShowcaseAI

ServiceNow AI Agent Studio — the workflow operating system; Now Assist, AI Agent Orchestrator, Workflow Data Fabric, 450+ integrations · Salesforce Agentforce — the customer operating system; Atlas Reasoning Engine; pre-built templates including Service Agent, Sales Development Rep, Sales Coach, Personal Shopper, Campaign Optimiser; documented ROI of 213% · enterprise HCM AI — the people & finance operating system; integrates with Sana and Flowise; Microsoft Entra Agent ID for enterprise identity · Microsoft Copilot Studio — enterprise no-code agent building across Microsoft 365 · IBM watsonx Orchestrate — enterprise governance, explainability, regulated-industry compliance · Google Vertex AI Agent Builder — no-code agent construction on GCP with Gemini at the core · Fluency across this enterprise platform landscape is what separates a candidate who uses AI from one who can architect AI-powered enterprise systems — the interview edge this course is designed to give you

•7.8 LLM APIs in ProductionAI

OpenAI Python SDK — chat completions, function calling, structured outputs · Anthropic Python SDK — messages API, tool use, extended thinking · Google GenAI Python SDK — Gemini API patterns · DeepSeek Python SDK — cost-efficient alternative · Function calling and structured outputs (Pydantic-validated) · Streaming with async iteration · Retry logic with exponential backoff · Cost tracking with tiktoken and provider usage APIs · Rate limiting and queueing · Caching patterns for repeated prompts

•7.9 Embeddings & Vector DatabasesAI

OpenAI text-embedding-3-large — the default choice · Voyage AI — high-performance alternative · Cohere embed models · Open-source — Sentence Transformers, BGE, E5 · ChromaDB — local/dev embedded database · Pinecone — managed serverless vector DB · Qdrant — open-source production-grade · pgvector — PostgreSQL extension (continuing from Section 3) · Weaviate, Milvus, LanceDB — alternatives · HNSW (Hierarchical Navigable Small World) indexing · IVF (Inverted File) indexing · Distance metrics — cosine, Euclidean, dot product · Filtering with metadata

•7.10 RAG Pipelines — Production ImplementationAI

The canonical RAG flow — Chunk → Embed → Index → Retrieve → Augment → Generate · Fixed-size chunking with overlap · Semantic chunking — embedding-based segmentation · Hierarchical chunking — parent-child relationships · Recursive character text splitting · Dense retrieval — embedding similarity · Sparse retrieval — BM25 keyword matching · Hybrid search — BM25 + embeddings combined · Re-ranking with cross-encoders (Cohere Rerank, BGE Reranker) · Agentic RAG — self-improving retrieval with agents that refine queries · Multi-hop RAG — chaining retrievals for complex questions · Graph RAG — knowledge graph-augmented retrieval · MCP-enhanced RAG — preview of Section 8 · Retrieval metrics — recall@k, MRR, NDCG · Generation metrics — RAGAS, faithfulness, answer relevance · End-to-end evaluation with LLM-as-judge · Section Project — build a production RAG service over a domain corpus (technical documentation, codebase, or knowledge base) with hybrid search, re-ranking, and full evaluation, deployed via FastAPI

Format

10 modules · 0 theory · 0 hands-on · 10 AI deep dive. Live mentor-led sessions with guided practice.

Section 7 of 8

Prepares you for section 8: Agentic AI Deep Dive + Coding Agent Project.

You ship

Prompt libraryVector DB indexProduction RAG

Agentic AI Deep Dive + Coding Agent Project

Agentic AI10 modules

The 2026 flagship — and the section that produces your brochure-named Coding Agent project. Ten modules covering the complete production agentic AI stack: LangGraph 1.0, Claude Agent SDK, CrewAI, Pydantic AI, Model Context Protocol (MCP), agent design patterns, multi-agent orchestration, observability, and the deployed AI Coding Agent that closes every 2026 AI Engineer interview.

Modules & topics

•8.1 Agentic AI FoundationsAI

Perceive → Reason → Act → Learn loop · Tools and tool use — the foundation of agency · State and memory — what agents remember · Plans and goals — autonomy vs orchestration · The post-2024 emergence of autonomous AI systems · Industry adoption — code generation, research, customer service, security · Every AI Engineer in 2026 ships at least one agent · LLM (the brain) — typically GPT-5.5, Claude Opus 4.7, or Gemini 3.1 Pro · Tools — functions the agent can call · Memory — short-term context, long-term storage · Planner — decomposes goals into steps · Executor — runs the plan with tool calls · Observer — handles results, errors, loops

•8.2 LangGraph 1.0 — The Production DefaultAI

#1 ranked production agent framework for 2026 — stateful, graph-based agent workflows with first-class support for cycles, branching, and human-in-the-loop checkpoints · LangGraph fundamentals — nodes, edges, state · StateGraph — typed state with TypedDict / Pydantic · Conditional edges for branching logic · Cycles and loops — what LangGraph does better than DAG frameworks · Checkpointing — pause and resume agent state · Persistence — durable agent state with SQLite/Postgres · Streaming agent intermediate steps · Human-in-the-loop patterns for production safety · Build your first LangGraph agent — a Q&A agent with retrieval, tool use, and human approval

•8.3 Claude Agent SDK — Deepest MCP IntegrationAI

#2 ranked framework — Anthropic's official agent SDK with the deepest native MCP integration of any agent framework · Claude Agent SDK Python library · Subagents and orchestration patterns · Native MCP integration — connect to any MCP server with zero glue code · Computer use — browser and OS automation through Claude · Extended thinking integration for complex reasoning · Streaming agent execution · Build an agent with Claude Agent SDK + 3 MCP servers (filesystem, GitHub, postgres)

•8.4 CrewAI — Multi-Agent CrewsAI

#3 ranked framework — role-based multi-agent collaboration · CrewAI fundamentals — agents, tasks, crews · Role-based agent design — Researcher, Writer, Reviewer, Tester patterns · Task delegation between agents · Sequential vs hierarchical crew patterns · Agent collaboration — when crews outperform single agents · Build a 3-agent crew for a software engineering task

•8.5 Pydantic AI & Type-Safe AgentsAI

Pydantic AI — type-safe agent design from Pydantic team · Structured outputs with Pydantic models · Type-safe tool definitions · Dependency injection patterns for agents · Validation guarantees — production-grade reliability · Type safety transforms agent reliability — invest here

•8.6 Model Context Protocol (MCP) — The 2026 StandardAI

The open standard reshaping agent infrastructure — originally created by Anthropic, now stewarded by the Linux Foundation with 200+ server implementations and 97M+ monthly SDK downloads · What MCP is — open protocol for connecting AI agents to tools, data, and systems · Architecture — MCP hosts, MCP clients, MCP servers · Transport — stdio, SSE, WebSocket · Primitives — Resources, Tools, Prompts, Sampling · Python SDK — mcp package · Defining tools, resources, prompts · Server lifecycle and capabilities · Build an MCP server exposing a custom tool · 200+ community servers — filesystem, GitHub, Postgres, Slack, Linear, etc. · Official Anthropic servers — the reference implementations · Enterprise MCP servers — building internal tool access for agents · Security — authentication, scopes, sandboxing · Performance — caching, batching, async patterns · Observability — tracing MCP calls

•8.7 Agent Design PatternsAI

ReAct — Reasoning + Acting interleaved · Plan-and-Execute — separate planning from execution · Reflection loops — agent reviews its own work · Self-consistency — multiple reasoning paths, vote on outcome · Tool calling — function calling at scale · Supervisor pattern — one agent orchestrates many · Swarm pattern — peer agents collaborate · Debate pattern — agents argue to refine answers · Hierarchical — managers and workers · Human-in-the-loop checkpoints — required for production · Confidence thresholds — autonomous vs human-routed · Approval gates for destructive actions · Audit trails for every agent action

•8.8 A2A Protocol & Agent-to-Agent CommunicationAI

A2A Protocol — agent-to-agent communication standard · Message formats and routing · Discovery and capability negotiation · Inter-agent trust and authentication · When to use A2A vs MCP

•8.9 Agent Observability & Production OperationsAI

LangSmith — agent tracing and evaluation platform · Trace analysis — debugging multi-step agent runs · Token usage tracking and cost attribution · Evaluation datasets — agent regression testing · Prompt versioning and A/B testing · Helicone, Langfuse, Arize Phoenix — observability options · OpenTelemetry for agent tracing · Deployment patterns — Docker, Kubernetes, serverless · Scaling agents — concurrency, queueing, batch processing · Cost optimisation — model routing, caching, batch APIs · Incident response for agent failures · Agents in production need ops discipline — invest in observability

•8.10 Final Project — The AI Coding AgentAI

The brochure-named final project — a deployed multi-agent AI Coding Agent that demonstrates production AI engineering competency across every previous section · LangGraph state machine for stateful coding workflows · Claude Agent SDK for deepest MCP integration · CrewAI multi-agent collaboration — Code-Writer Agent generates Python code from natural language · Code-Reviewer Agent reviews generated code for quality, security, style · Tester Agent writes and runs pytest tests · Documenter Agent generates docstrings and README updates · Filesystem MCP server — read/write code files · Git MCP server — version control operations · GitHub MCP server — PR creation, issue management · Postgres MCP server — code metrics, history · Python execution MCP server — run tests, validate code · Generate Python code from natural language requirements · Refactor existing code with safety guarantees · Debug failures by reading error messages and proposing fixes · Test generated code with pytest · Document code with docstrings and README updates · Commit and PR with proper message conventions · FastAPI backend (from Section 6) serving the agent · Streamlit or React frontend for user interaction · PostgreSQL (from Section 3) for agent state and history · JWT/OAuth2 auth (from Section 6) · LangSmith observability for every agent run · Human-in-the-loop checkpoints before every commit · Cost tracking and budget enforcement · Public verification URL with the 2026 Agent-Ready rubric · A deployed AI Coding Agent accessible via public URL, GitHub repository with full source code, demo video, LangSmith dashboard, and architecture decision records — the named project for the entire AI Engineer programme

Format

10 modules · 0 theory · 0 hands-on · 10 AI deep dive. Live mentor-led sessions with guided practice.

Section 8 of 8

The capstone: a deployed multi-agent AI Coding Agent with a public verification URL.

You ship

LangGraph agentMCP server fleetAI Coding Agent

Tools you'll master

32+ GenAI & agentic tools, one production project.

Real-time projects

You don't watch videos. You ship software.

Three full-production projects, each threaded through the entire curriculum. By the project, you've built the whole stack around them.

Hero project

Production agentic system on LangGraph + MCP + A2A

Build an end-to-end multi-agent platform — supervisor + specialist nodes coordinating over A2A, an MCP server fleet exposing your agents as tools, a hybrid RAG layer, and a full eval + safety harness.

01Multi-agent topology — supervisor + specialist nodes communicating over A2A, with retries, fallback models, replay-capable state, and human-in-the-loop checkpoints.

02MCP server fleet that exposes your agents as tools to Claude/ChatGPT desktop, plus OpenAPI tools for arbitrary REST APIs — auth, scopes, rate-limit, audit logging.

03Production RAG layer with hybrid search across Postgres + Pinecone, contextual chunking, semantic caching, and a LangSmith golden-dataset eval suite.

04Eval + safety harness — DSPy optimization, Guardrails AI / NeMo Guardrails for safety, Arize for drift monitoring, hallucination + cost guardrails on a public dashboard.

Outcome: ~80% task automation

p95 latency: <1s

Reviewer: AI Engineering panel

LangGraphA2AMCPDSPyPinecone

Enterprise

Multi-agent A2A workshop

Build a 5-agent system that negotiates work via the A2A protocol — a supervisor, a researcher, a coder, a reviewer, and a deployer. Each agent runs as its own service with auth, telemetry, and replay.

A2AMulti-agentLangGraphTelemetry

Real-time

DSPy-optimized RAG service

Build a self-tuning RAG service that uses DSPy to automatically optimize prompts and retrieval strategy against a golden dataset, with Arize-tracked drift monitoring.

DSPyRAGArizeLangSmith

Project

Your AI agent system in a controlled project environment.

Pick a real partner workflow. Deploy a production GenAI + agentic system — multi-agent topology, MCP-served tools, A2A coordination, production evals — into a partner team that's running it for real users.

Download the real world project

Full scope, sample deployment contexts, project milestones, and grading rubric — PDF, 14 pages.

Production-style capstoneCareer support included

Your instructor

Taught by engineers who shipped agentic AI to production.

Manikanta Kona

Founder, RoboEdify · AI Engineering Architect

GenAI · Agentic AI · LangGraph · MCP · A2A · Production RAG

"Production GenAI is where AI engineers earn their keep — LangGraph orchestrating multi-agent topologies, MCP serving your tools to any client, A2A coordinating across services, and an eval harness that catches regressions before users do. That's the bar I teach to, every class."

15 yrs

AI ENGINEERING

2,400+

LEARNERS

4.9 /5

RATING

Manikanta is the founder of RoboEdify and brings 15 years of platform architecture from AT&T, Salesforce, Cox Communications, and Broadcom — where he led production ML and GenAI rollouts for Fortune-500 banks, telcos, and insurers. Most recently he architected production LangGraph + MCP + A2A systems that replaced traditional case-handling tiers with autonomous multi-agent flows, with full eval and observability harnesses behind them.

His classes get you two things other programs don't give you: a founding architect who's shipped agentic AI from inside the Fortune 500, and a curriculum rewritten every quarter — so when hiring managers ask about MCP server fleets, A2A negotiation, DSPy optimization, or LangSmith eval suites, you've already built it. Holds LangChain Academy badges and the AWS Solutions Architect — ML Specialty; M.S. in Engineering, Purdue University.

Ravi Krishna

Chief Technologist, RoboEdify · Agent Platform & Eval Lead

LangGraph · MCP · A2A · DSPy · Vector DBs · Evals · Agent Observability

"Shipping a multi-agent system to production is the easy part — keeping it healthy is the work. MCP fleets that don't drift, A2A handshakes that recover from partial failures, golden datasets that catch regressions before users do. That's what I teach."

10 yrs

AI ENGINEERING

1,800+

LEARNERS

4.8 /5

RATING

Ravi is Chief Technologist at RoboEdify, where he leads the Agent Platform and evaluation practice. After 8 years shipping production ML and DevOps pipelines, he stepped into the Chief Technologist seat to wire LangGraph, MCP fleets, and A2A into the way real engineering teams actually run agents — replay-able state, golden-dataset evals, drift monitoring, and cost guardrails that keep multi-agent systems quiet on purpose.

His agent and eval modules are built from real production post-mortems, not slide decks. Expect to leave with working MCP servers, an A2A-coordinated multi-agent topology, a DSPy-optimized RAG service, and an Arize + LangSmith observability stack you can stake an SLA on. Holds the Pragmatic AI Engineer track credential and Azure AI Engineer Associate; ten years at RoboEdify, hands-on, and known for the unglamorous parts of agentic AI that everyone else skips.

HIRING PARTNERS · INDUSTRY VOICES

What AI engineering employers say about RoboEdify grads.

Real feedback from engineering leaders at AI labs and the firms hiring our AI Engineer · GenAI & Agentic graduates.

RoboEdify grads ramp 40% faster on GenAI agent rollouts than typical AI engineering hires. Best GenAI engineering pipeline in India.

Aakash Mehta, Engineering Director, Microsoft

We've onboarded 80+ RoboEdify alumni in 18 months. Lowest ramp time we've seen for multi-agent systems and eval practices.

Anita Sharma, Senior Manager, Deloitte

The AI Engineer · GenAI & Agentic programme is comprehensive — LangGraph, MCP, A2A, evals. Grads come pre-trained for production agentic AI engineering.

Rahul Bhatt, Solutions Lead, Mphasis

Their LangGraph + DSPy + eval track produces PMs who ship production multi-agent systems on day one. Rare combination of engineering rigor and AI craft.

Deepak Pillai, Senior Architect, TCS

What sets RoboEdify apart is the agentic platform layer baked into the AI engineer track. Our enterprise clients ask for exactly this profile.

Suresh Menon, Practice Lead, Accenture

Their LangChain Academy + Pragmatic AI Engineer prep is rigorous, and the shipped project — multi-agent system, MCP fleet, eval harness — is what closes interviews for us.

Vikram Iyer, Director, Infosys

RoboEdify's AI engineers ship production multi-agent systems twice as fast in the first 90 days. Our internal engineering metrics back this up clearly.

Lakshmi Nair, VP Engineering, Wipro

Best GenAI engineering pipeline we've sourced from in India. Their projects are real shipped agent systems, not toy demos.

Karthik Subramanian, Engineering Director, Cognizant

Strong GenAI and agentic engineering foundation. Their AI Engineer grads need almost zero ramp time on enterprise agent platform engagements with us.

Arun Joshi, Practice Director, Capgemini

We've placed 40+ RoboEdify alumni across our GenAI and watsonx engineering teams. Strong fundamentals, sharp on eval and observability.

Sanjay Verma, Talent Director, IBM

multi-agent systems + LangGraph evals is exactly the talent gap we've been struggling to close. RoboEdify is filling it for us reliably.

Anjali Desai, Practice Head, LTIMindtree

Their AI Engineer track delivers engineers who navigate LangGraph, MCP, and A2A on customer engagements unsupervised.

Ramesh Iyer, Senior Manager, Tech Mahindra

Hired 25+ RoboEdify graduates for our GenAI engineering practice. Strong on LangGraph, sharp on MCP/A2A, fluent in agent eval.

Geetha Pillai, Talent Acquisition Lead, Cyient

RoboEdify grads who blend multi-agent systems with Azure OpenAI evals land production-ready on day one. Rare combination, well-trained.

Priya Reddy, Talent Lead, Microsoft

03Program certifications

An Agent‑Ready credential, not a participation trophy.

RoboEdify · Institute Certificate

Agent‑Ready AI Engineer

Presented to

Spandana Bala

For the successful design, build, and production deployment of a multi-agent system — LangGraph topology, MCP server fleet, A2A coordination, and an eval harness — evaluated against the LangChain Academy badges, AWS ML Specialty, and Pragmatic AI Engineer credential rubrics.

Manikanta Kona

CEO · RoboEdify

AGENT
READY
2026

Industry‑recognized

Co‑branded with the AI engineering community and mapped to LangChain Academy and Pragmatic AI Engineer credentials — names that hiring managers already scan for on resumes.

Project artifact included

Every certificate carries your shipped project — multi-agent system, MCP fleet, A2A coordination, eval harness — with a link to the live partner-org deployment. Proof, not a promise.

Enhanced skill validation

Graded against the 2026 Agent‑Ready rubric: LangGraph topologies, MCP servers, A2A coordination, eval harnesses, drift monitoring & cost guardrails. No pass/fail — a level 1‑5 band.

Verifiable on a public URL

Each credential has a public verification page recruiters can check in 10 seconds — no PDF back‑and‑forth.

Job roles

Roles this program prepares you for.

AI EngineerBuild and operate LLM-powered products: APIs, retrieval, evaluation and deployment.

GenAI EngineerOwn prompt and context engineering, fine-tuning choices and multimodal generation in production.

Agentic AI EngineerDesign multi-agent systems on LangGraph, Claude Agent SDK and CrewAI, wired through MCP and A2A.

RAG / Retrieval EngineerChunking, embeddings, vector databases, hybrid search and re-ranking at production scale.

MCP Server EngineerExpose repositories, tools and execution environments to agents through the Model Context Protocol.

AI Platform / Evaluation EngineerObservability with LangSmith and Arize, eval harnesses, guardrails and cost control for agent fleets.

04Job placement support

Your first AI Engineer offer isn't a lottery ticket. It's a built process.

GitHub, LinkedIn, resume — and most importantly, warm intros into AI labs and AI-first product orgs. Our placement team works your search like an account, not a helpdesk.

01 / GITHUB & PORTFOLIO

A portfolio, not a graveyard.

Guidance on building a portfolio that showcases your multi-agent system, MCP fleet, A2A coordination, eval dashboard, and a public verification URL — reviewed 1:1, not via template.

02 / RESUME PREP

Rewrite, don't proofread.

A one-page resume rebuilt around the AI systems you shipped (multi-agent topologies, MCP fleets, eval harnesses), the partner-org project, and the business outcome. Reviewed by AI engineers who've read 10,000+ resumes.

03 / LINKEDIN + INTROS

Where most opportunities actually live.

Profile tuning plus direct warm introductions into AI labs and AI-first product orgs — Microsoft, Anthropic, OpenAI partners, Hugging Face, LangChain, Cohere, Mistral, Databricks, Snowflake, Scale AI, Stripe, Razorpay, Freshworks, Zoho, plus services that staff GenAI teams (Deloitte, Accenture, Cognizant, TCS). You leave with recruiter contacts, not a generic "good luck."

AI Engineer alumni

Hundreds of AI engineering careers launched — here are eight.

Spandana Bala

AI Engineer

Hyderabad · India

Now at · Microsoft

Naveen Vedala

Senior AI Engineer (Agent Platforms)

Hyderabad · India

Now at · Atlassian

Tejashwini Addla

Staff GenAI Engineer

Hyderabad · India

Now at · Salesforce

Tharunesh Dillikar

Principal Engineer (Multi-Agent Systems)

Seattle · United States

Now at · Anthropic

Mujahed Mohammed

LangGraph Backend Lead

Hyderabad · India

Now at · Databricks

Bhargav Kumar Murala

MCP Server Engineer

Hyderabad · India

Now at · Adobe

Sai Manasa Leburi

RAG Engineer

New York · United States

Now at · Hugging Face

Rahul Dhamma

AI Evaluation Lead

Hyderabad · India

Now at · Cohere

Our locations

Come chat with us — over coffee, or over Zoom.

One flagship campus in Hyderabad, plus online Principal Engineer (Multi-Agent Systems) classes running on Indian and US timezones.

Flagship campus

Hyderabad

2nd Floor, Hitech City Road · Above Domino's · Opp. Cyber Towers, Jai Hind Enclave · Hyderabad, Telangana

Call

+91 8142998866

US desk

+1 256 388 7766

Hours

Mon–Sun · 7 AM–9 PM

Online class

Global

Weekend and evening AI Engineer classes running on IST and PST. Every online class ships the same shipped project — multi-agent system, MCP fleet, A2A coordination, eval harness — as the on‑campus track.

Timezones

IST & PST

Format

Live + 1:1 mentorship

Admissions

ENROLLING NOW

FAQ

Questions we actually get — answered honestly.

Straight answers on prerequisites, the GenAI / agentic stack, certifications, and placement. If something's missing, book a 20-minute advisor call — no slides, no pitch.

Do I need a CS background or prior ML experience?+

No on both counts. Roughly 40% of every class comes from non-CS streams — mechanical, electrical, BCom, BBA, and self-taught coders. The opening modules cover the GenAI fundamentals, LangGraph patterns, and agent design from scratch. What you do need is consistency and regular practice.

Will I actually ship production agents, or only build toy demos?+

You actually ship. Every learner deploys a working multi-agent system on LangGraph with MCP-served tools, A2A coordination between agents, and a real eval harness with golden datasets, drift monitoring, and cost guardrails. The project runs in a partner org — not a notebook.

Which models, frameworks, and protocols will I use?+

Models: OpenAI, Anthropic, Gemini, Hugging Face, vLLM, Ollama. Frameworks: LangChain, LangGraph, LangSmith, LlamaIndex, DSPy. Protocols: MCP, A2A. Vector DBs: Pinecone, Chroma, Weaviate, Qdrant, Milvus. Safety & obs: Guardrails, NeMo Guardrails, Arize, MLflow, Weights & Biases.

Will I prep for AIPMM AI Engineer and Pragmatic Principal Engineer (Multi-Agent Systems) certs?+

Yes. The curriculum is mapped to the AIPMM AI Engineer track and the Pragmatic Principal Engineer (Multi-Agent Systems) credential. We run two full mock exams and reimburse the voucher fee on first-attempt pass.

How is the learning workload structured?+

The program combines live mentor-led classes, guided labs, project work, and optional support sessions. An advisor can explain the current class format before enrolment.

Is placement support really 1:1, and which companies hire AI engineers?+

Yes. Career support includes portfolio and profile preparation, interview practice, and role-fit introductions where available. RoboEdify does not guarantee an interview, offer, salary, employer, location, or timeline.

Online, weekend, or on-campus?+

All three. On-campus at the Hyderabad flagship, live online (IST and PST classes), and a weekend track for working professionals. Every format ships the same shipped project — multi-agent system, MCP fleet, A2A coordination, eval harness — only the schedule changes.

What if I fall behind, or can't continue mid-class?+

Freeze your seat for up to 90 days and rejoin the next class — no extra fee. TAs run catch-up sessions every Saturday for learners needing additional support, and recordings of every live session are available for the lifetime of your account.

Still have a question? Talk to an advisor — no slides, no pitch.

One million AI‑native professionals by 2027.
Let's put you in that number.

Book a 20‑minute advisor call. We'll map your current role to the right program, talk honestly about timelines, and walk you through a real class's project.

Plan your learning

Course: AI Engineer + Coding Agent
Preparation: Diagnostic-based preparation before the common core
Level: Specialist
Curriculum: 53 modules across 8 sections

Confirm your intake dates, delivery mode, fees, assessment and practical access with RoboEdify before enrolling. Course content describes the learning scope; an enquiry does not reserve a seat.

AI Engineer + Coding Agent

What is an AI Engineer — and why build the whole stack?

Models learned to act. The jobs followed.

Built for engineers who want to ship AI systems, not call APIs.

Take an idea from prompt to a deployed, observable agent.

Eight sections. 53 modules. Python → FastAPI → GenAI → Agentic AI.

Fundamentals of IT & AI

Python for AI & Data

SQL for AI & Data

Python Libraries for AI

Advanced Python Concepts

Modern Python Framework FastAPI

Generative AI Deep Dive

Agentic AI Deep Dive + Coding Agent Project

32+ GenAI & agentic tools, one production project.

You don't watch videos. You ship software.

Production agentic system on LangGraph + MCP + A2A

Multi-agent A2A workshop

DSPy-optimized RAG service

Your AI agent system in a controlled project environment.

Taught by engineers who shipped agentic AI to production.

What AI engineering employers say about RoboEdify grads.

An Agent‑Ready credential, not a participation trophy.

Roles this program prepares you for.

Your first AI Engineer offer isn't a lottery ticket. It's a built process.

A portfolio, not a graveyard.

Rewrite, don't proofread.

Where most opportunities actually live.

Hundreds of AI engineering careers launched — here are eight.

Come chat with us — over coffee, or over Zoom.

Questions we actually get — answered honestly.

One million AI‑native professionals by 2027.Let's put you in that number.

Plan your learning

One million AI‑native professionals by 2027.
Let's put you in that number.