Agentic AI Engineer Master Program
An intensive 4-month online weekend live engineering cohort (Sat & Sun • 4 hours/day: 2h live faculty lectures + 2h supervised coding labs) covering Agentic AI Engineer production capstones and 580+ AI interviews, with eligible hiring-drive access under documented placement terms and tuition-refund protection where all policy conditions are met.
- Online Weekend Live Sessions (Sat & Sun • 4 Hours / Day)
- 4 Months (16 Weeks) Comprehensive Finish
- Guaranteed Placement Drives until Placed across 1,050+ top companies
- 26 Production Microservices & Capstones with GitHub code reviews
- 580+ Topic Mock Tests & 1-on-1 AI Voice Technical Interviews
Our Graduates Get Marketed to 1,050+ Global Tech Leaders & Unicorns
Continuous corporate interview referrals until job offer letter issuance:
Online Weekend Master Track: 4 Months of Live Mentorship & Placement Drives
Designed for college students and working professionals, our Online Weekend Master Track delivers intensive 4-hour live sessions every Saturday and Sunday across 16 weeks (4 Months). Complete 128+ hours of live faculty instruction, 26 production capstones, and 580+ AI interviews with unlimited corporate interview drives until you get placed!
Live Architecture & Core Mentorship
2 Hours of interactive enterprise architecture, live faculty coding, and design patterns followed by 2 Hours of supervised capstone development.
Hands-On Labs, Tests & AI Practice
2 Hours of advanced microservices, real-time queues & cloud deployment followed by 2 Hours of timed mock tests and 1-on-1 AI voice interview rounds.
4-Month Master Roadmap to Guaranteed Placement
We transform you into a battle-tested software engineer ready to clear Tier-1 technical and system design interview rounds in 4 months with weekend online sessions.
Architecture & Core Mechanics
Core OOP, memory mechanics, data structures, algorithms & clean design patterns.
Capstones & Microservices
Build production full stack SaaS, microservices, REST APIs, queues & cloud deployment.
System Design & AI Interviews
Simulate live FAANG interview rounds, timed topic mock tests and AI voice evaluations.
Corporate Drives until Placed
Resume marketing, hiring drives across 1,050+ partners, and placement guarantee.
Projected Target CTC After Program
Industry-verified compensation brackets achieved by graduates across 1,050+ hiring partners:
₹8.5L – ₹14L /yr
Software Engineer I, Junior Backend Developer, Full Stack Associate.
- 260+ Hours Live Mentorship
- 26 Capstone Projects on GitHub
- 580+ AI Technical Interview Scorecards
₹14L – ₹24L /yr
Full Stack Java Engineer, Spring Boot Microservices Specialist, Cloud Engineer.
- Kafka Event-Driven Architectures
- Redis Caching & Performance Tuning
- Docker, Kubernetes & AWS CI/CD
₹24L – ₹36L+ /yr
Senior Full Stack Engineer, Microservices Architect, Lead Consultant.
- High-Throughput System Design (LLD/HLD)
- Fault Tolerance & Distributed Transactions
- FAANG System Design Clearing Mentorship
Topic-Wise Curriculum & Practice Hub
35 Modules • 136 Deep-Dive Topics • 136 Integrated Topic Mock Tests & AI Interviews
Understanding Agentic AI, Anatomy of an AI Agent, Agent Execution Loop, When NOT to Use Agents.
Understanding Agentic AI
AI, Generative AI, LLM applications, chatbots, assistants, agents, autonomous agents, agentic workflows and systems, AI automation, traditional vs agentic workflows, deterministic vs probabilistic systems, and autonomy levels.
- Explain the core concepts and architecture of Understanding Agentic AI.
- Apply Understanding Agentic AI in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Anatomy of an AI Agent
Agent = Model + Instructions + State + Memory + Tools + Environment + Control Logic + Guardrails; goals, context, observations, actions and termination conditions.
- Explain the core concepts and architecture of Anatomy of an AI Agent.
- Apply Anatomy of an AI Agent in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Agent Execution Loop
Observe -> reason -> select action -> execute tool -> observe result -> continue or stop; iteration limits, termination, tool selection, errors and task completion.
- Explain the core concepts and architecture of Agent Execution Loop.
- Apply Agent Execution Loop in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
When NOT to Use Agents
Choose deliberately among traditional code, workflow engines, a single LLM call, RAG, a single agent and multi-agent architecture; prefer deterministic software when possible.
- Explain the core concepts and architecture of When NOT to Use Agents.
- Apply When NOT to Use Agents in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Python Fundamentals, Object-Oriented Python, Advanced Python, Async Python.
Python Fundamentals
Variables, data types, strings, lists, dictionaries, tuples, sets, conditions, loops, functions, modules and packages.
- Explain the core concepts and architecture of Python Fundamentals.
- Apply Python Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Object-Oriented Python
Classes, objects, inheritance, encapsulation, polymorphism, interface concepts and abstraction.
- Explain the core concepts and architecture of Object-Oriented Python.
- Apply Object-Oriented Python in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Advanced Python
Decorators, iterators, generators, comprehensions, exceptions, context managers, typing, dataclasses, environment variables and configuration.
- Explain the core concepts and architecture of Advanced Python.
- Apply Advanced Python in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Async Python
Synchronous vs asynchronous execution, async/await, tasks, concurrency, timeouts, cancellation, parallel API requests and error handling.
- Explain the core concepts and architecture of Async Python.
- Apply Async Python in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
HTTP & APIs, Calling External Services, FastAPI.
HTTP & APIs
HTTP, HTTPS, REST, JSON, requests, responses, headers, status codes, authentication and API keys.
- Explain the core concepts and architecture of HTTP & APIs.
- Apply HTTP & APIs in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Calling External Services
GET, POST, PUT and DELETE requests, authentication, retries, errors, pagination and timeouts.
- Explain the core concepts and architecture of Calling External Services.
- Apply Calling External Services in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
FastAPI
Routes, request and response models, Pydantic validation, dependencies, authentication concepts, async endpoints, middleware and OpenAPI.
- Explain the core concepts and architecture of FastAPI.
- Apply FastAPI in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
LLM Fundamentals, LLM API Development, Structured Output.
LLM Fundamentals
Language models, transformers, tokens, tokenizers, context windows, inference, model parameters, instructions, output generation and hallucinations.
- Explain the core concepts and architecture of LLM Fundamentals.
- Apply LLM Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
LLM API Development
API integration, model selection, system instructions, user messages, responses, streaming, errors, retries, token limits and usage tracking.
- Explain the core concepts and architecture of LLM API Development.
- Apply LLM API Development in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Structured Output
Reliable JSON, schemas, Pydantic validation, retries and structured extraction for machine-readable agent responses.
- Explain the core concepts and architecture of Structured Output.
- Apply Structured Output in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Prompt Engineering, Context Engineering, Agent Instructions.
Prompt Engineering
Instructions, context, examples, constraints, output formats, zero-shot and few-shot prompting, templates and role instructions.
- Explain the core concepts and architecture of Prompt Engineering.
- Apply Prompt Engineering in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Context Engineering
System, user, retrieved, memory, tool, conversation and environment context; prioritization and compression.
- Explain the core concepts and architecture of Context Engineering.
- Apply Context Engineering in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Agent Instructions
Design goals, responsibilities, boundaries, allowed and prohibited actions, escalation rules, output requirements and completion criteria.
- Explain the core concepts and architecture of Agent Instructions.
- Apply Agent Instructions in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Function Calling, Tool Calling, Tool Design, Tool Routing, Parallel Tool Execution.
Function Calling
Function schemas, arguments, model tool selection, tool results, validation and error handling.
- Explain the core concepts and architecture of Function Calling.
- Apply Function Calling in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Tool Calling
Create tools for calculators, example weather APIs, SQL, search, customer and ticket systems, simulated communication, internal APIs and file lookup.
- Explain the core concepts and architecture of Tool Calling.
- Apply Tool Calling in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Tool Design
Tool naming, descriptions, input and output schemas, granularity, permissions and failure handling.
- Explain the core concepts and architecture of Tool Design.
- Apply Tool Design in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Tool Routing
Choose which tool to use, when to use it, valid parameters and failure behavior.
- Explain the core concepts and architecture of Tool Routing.
- Apply Tool Routing in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Parallel Tool Execution
Sequential and parallel execution, dependency graphs, result combination, timeouts and partial failures.
- Explain the core concepts and architecture of Parallel Tool Execution.
- Apply Parallel Tool Execution in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Custom Agent Loop, Agent State Machine, Agent Termination.
Custom Agent Loop
Implement user request -> LLM -> tool decision -> execution -> observation -> LLM -> final response without a framework.
- Explain the core concepts and architecture of Custom Agent Loop.
- Apply Custom Agent Loop in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Agent State Machine
CREATED, PLANNING, RUNNING, WAITING_FOR_TOOL, WAITING_FOR_USER, COMPLETED, FAILED and CANCELLED states.
- Explain the core concepts and architecture of Agent State Machine.
- Apply Agent State Machine in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Agent Termination
Maximum iterations, timeouts, token and cost limits, completion criteria and failed-run states.
- Explain the core concepts and architecture of Agent Termination.
- Apply Agent Termination in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Task Decomposition, Planner-Executor Pattern, Router Pattern, Reflection & Review Patterns, Supervisor Pattern.
Task Decomposition
Turn a complex goal into understand, plan, execute, verify and complete stages.
- Explain the core concepts and architecture of Task Decomposition.
- Apply Task Decomposition in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Planner-Executor Pattern
Planner -> task plan -> executor -> results -> reviewer -> final answer.
- Explain the core concepts and architecture of Planner-Executor Pattern.
- Apply Planner-Executor Pattern in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Router Pattern
Route inputs to research, support, database or other specialist agents.
- Explain the core concepts and architecture of Router Pattern.
- Apply Router Pattern in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Reflection & Review Patterns
Observable draft, evaluate, improve, verify and retry cycles without exposing private reasoning.
- Explain the core concepts and architecture of Reflection & Review Patterns.
- Apply Reflection & Review Patterns in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Supervisor Pattern
A supervisor coordinates specialists, tools, tasks and final synthesis.
- Explain the core concepts and architecture of Supervisor Pattern.
- Apply Supervisor Pattern in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
LangChain Foundations, Building LangChain Agents, Agent Middleware Concepts.
LangChain Foundations
Models, prompts, messages, tools, structured outputs, retrievers, middleware and agent abstractions.
- Explain the core concepts and architecture of LangChain Foundations.
- Apply LangChain Foundations in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Building LangChain Agents
Agents with instructions, tools, context, structured responses and error handling.
- Explain the core concepts and architecture of Building LangChain Agents.
- Apply Building LangChain Agents in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Agent Middleware Concepts
Logging, guardrails, tool filtering, dynamic prompts, permissions and retries.
- Explain the core concepts and architecture of Agent Middleware Concepts.
- Apply Agent Middleware Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
LangGraph Fundamentals, State Management, Conditional Routing, Persistence, Durable Execution, Interrupts, Human-in-the-Loop.
LangGraph Fundamentals
Graphs, state, nodes, edges, conditional edges, entry points and end states.
- Explain the core concepts and architecture of LangGraph Fundamentals.
- Apply LangGraph Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
State Management
Structured state for messages, users, tasks, tool outputs, workflow data and approvals.
- Explain the core concepts and architecture of State Management.
- Apply State Management in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Conditional Routing
Classify a request and route it to research, database, support or another node.
- Explain the core concepts and architecture of Conditional Routing.
- Apply Conditional Routing in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Persistence
Checkpoints, thread state, persistence, workflow restoration and resumable runs.
- Explain the core concepts and architecture of Persistence.
- Apply Persistence in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Durable Execution
Survive service restarts, long operations, temporary failures and delayed human approvals.
- Explain the core concepts and architecture of Durable Execution.
- Apply Durable Execution in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Interrupts
Pause workflows for user information, admin, payment, manager or sensitive-action approval.
- Explain the core concepts and architecture of Interrupts.
- Apply Interrupts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Human-in-the-Loop
Propose an action, pause for approve/reject/modify and safely continue.
- Explain the core concepts and architecture of Human-in-the-Loop.
- Apply Human-in-the-Loop in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Memory Fundamentals, Short-Term State, Long-Term Memory, Memory Retrieval, Memory Privacy.
Memory Fundamentals
Working, conversational, long-term and semantic memory, episodic concepts and user preferences.
- Explain the core concepts and architecture of Memory Fundamentals.
- Apply Memory Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Short-Term State
Current conversation, active workflow, task and intermediate results.
- Explain the core concepts and architecture of Short-Term State.
- Apply Short-Term State in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Long-Term Memory
Appropriate storage of preferences, task summaries, learned user information and reusable context.
- Explain the core concepts and architecture of Long-Term Memory.
- Apply Long-Term Memory in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Memory Retrieval
Similarity search, filtering, recency, importance and summarization for relevant memory.
- Explain the core concepts and architecture of Memory Retrieval.
- Apply Memory Retrieval in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Memory Privacy
Consent, minimization, retention, deletion, access control and sensitive-information handling.
- Explain the core concepts and architecture of Memory Privacy.
- Apply Memory Privacy in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
RAG Fundamentals, Agentic RAG, Multi-Source Retrieval, Retrieval Routing, Advanced RAG.
RAG Fundamentals
Documents -> chunking -> embeddings -> vector database -> retriever -> agent.
- Explain the core concepts and architecture of RAG Fundamentals.
- Apply RAG Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Agentic RAG
The agent decides whether retrieval is needed, which source to search, adequacy and whether another search is required.
- Explain the core concepts and architecture of Agentic RAG.
- Apply Agentic RAG in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Multi-Source Retrieval
Query documents, SQL, APIs, knowledge bases and vector stores.
- Explain the core concepts and architecture of Multi-Source Retrieval.
- Apply Multi-Source Retrieval in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Retrieval Routing
Choose among SQL, vector search, API or no retrieval based on the question.
- Explain the core concepts and architecture of Retrieval Routing.
- Apply Retrieval Routing in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Advanced RAG
Query rewriting, multi-query, metadata filters, hybrid retrieval, reranking, compression, attribution and retrieval validation.
- Explain the core concepts and architecture of Advanced RAG.
- Apply Advanced RAG in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Multi-Agent Fundamentals, Specialist Agents, Supervisor Multi-Agent Pattern, Hierarchical Agents, Agent Handoffs, Multi-Agent Failure Handling.
Multi-Agent Fundamentals
Specialization, task isolation, delegation, coordination, shared context and communication.
- Explain the core concepts and architecture of Multi-Agent Fundamentals.
- Apply Multi-Agent Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Specialist Agents
Research, data, analyst, writer and reviewer agent responsibilities.
- Explain the core concepts and architecture of Specialist Agents.
- Apply Specialist Agents in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Supervisor Multi-Agent Pattern
Supervisor assigns specialist tasks, receives results and produces final output.
- Explain the core concepts and architecture of Supervisor Multi-Agent Pattern.
- Apply Supervisor Multi-Agent Pattern in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Hierarchical Agents
Executive agents, department supervisors and specialist-agent hierarchies.
- Explain the core concepts and architecture of Hierarchical Agents.
- Apply Hierarchical Agents in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Agent Handoffs
Delegation, responsibility and state transfer, routing and returning control.
- Explain the core concepts and architecture of Agent Handoffs.
- Apply Agent Handoffs in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Multi-Agent Failure Handling
Timeouts, contradictions, tool failures, unavailable agents, duplicate work and cyclic delegation.
- Explain the core concepts and architecture of Multi-Agent Failure Handling.
- Apply Multi-Agent Failure Handling in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Model Context Protocol Fundamentals, Building an MCP Server, Building an MCP Client, MCP Security, MCP Tasks & Long-Running Work, MCP Enterprise Architecture.
Model Context Protocol Fundamentals
MCP interoperability, architecture, clients, servers, hosts, tools, resources, prompts, transports, authorization and extensions.
- Explain the core concepts and architecture of Model Context Protocol Fundamentals.
- Apply Model Context Protocol Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Building an MCP Server
Expose calculator, database lookup, product search and knowledge resources through an MCP server.
- Explain the core concepts and architecture of Building an MCP Server.
- Apply Building an MCP Server in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Building an MCP Client
Discover capabilities, list and invoke tools, read resources and process structured results.
- Explain the core concepts and architecture of Building an MCP Client.
- Apply Building an MCP Client in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
MCP Security
Authentication, authorization, least privilege, tokens, permissions, validation, malicious servers and untrusted tool output.
- Explain the core concepts and architecture of MCP Security.
- Apply MCP Security in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
MCP Tasks & Long-Running Work
Research, reports, bulk processing, code analysis and document processing beyond a quick request.
- Explain the core concepts and architecture of MCP Tasks & Long-Running Work.
- Apply MCP Tasks & Long-Running Work in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
MCP Enterprise Architecture
Agent -> MCP client -> gateway -> multiple MCP servers -> enterprise systems.
- Explain the core concepts and architecture of MCP Enterprise Architecture.
- Apply MCP Enterprise Architecture in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Agent-to-Agent Communication, A2A Concepts, MCP vs A2A, Cross-Framework Collaboration, Enterprise Agent Network.
Agent-to-Agent Communication
Why independent agents need a standardized communication layer.
- Explain the core concepts and architecture of Agent-to-Agent Communication.
- Apply Agent-to-Agent Communication in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
A2A Concepts
Agent discovery and cards, capabilities, tasks, messages, artifacts, lifecycle, authentication and streaming.
- Explain the core concepts and architecture of A2A Concepts.
- Apply A2A Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
MCP vs A2A
MCP connects agents to tools/data; A2A connects agents to agents; select the appropriate protocol.
- Explain the core concepts and architecture of MCP vs A2A.
- Apply MCP vs A2A in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Cross-Framework Collaboration
Allow an agent built with one framework to collaborate with an independently implemented agent.
- Explain the core concepts and architecture of Cross-Framework Collaboration.
- Apply Cross-Framework Collaboration in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Enterprise Agent Network
Sales, finance, inventory and support agents communicating through authorized task interfaces.
- Explain the core concepts and architecture of Enterprise Agent Network.
- Apply Enterprise Agent Network in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Long-Horizon Tasks, Checkpointing, Resume After Failure, Background Agent Jobs.
Long-Horizon Tasks
Deep research, software development, document analysis, data migration and business automation.
- Explain the core concepts and architecture of Long-Horizon Tasks.
- Apply Long-Horizon Tasks in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Checkpointing
Persist current state, completed and pending steps, tool outputs, approvals and artifacts.
- Explain the core concepts and architecture of Checkpointing.
- Apply Checkpointing in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Resume After Failure
Recover from a failure using a checkpoint and resume safely.
- Explain the core concepts and architecture of Resume After Failure.
- Apply Resume After Failure in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Background Agent Jobs
QUEUED, RUNNING, WAITING, COMPLETED and FAILED status lifecycle.
- Explain the core concepts and architecture of Background Agent Jobs.
- Apply Background Agent Jobs in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Why Agents Need Sandboxes, Sandbox Security, Code Execution Agent Architecture.
Why Agents Need Sandboxes
Controlled file manipulation, code generation, command execution, repository processing and artifact creation.
- Explain the core concepts and architecture of Why Agents Need Sandboxes.
- Apply Why Agents Need Sandboxes in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Sandbox Security
Isolation, filesystem restrictions, resource limits, network controls, temporary environments, secret separation and cleanup.
- Explain the core concepts and architecture of Sandbox Security.
- Apply Sandbox Security in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Code Execution Agent Architecture
Agent -> sandbox -> files/commands/code -> result -> agent; never allow unrestricted production-host execution.
- Explain the core concepts and architecture of Code Execution Agent Architecture.
- Apply Code Execution Agent Architecture in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Approval Workflows, Risk-Based Approval, Approval Audit Trail.
Approval Workflows
Require approval for communication, record changes, financial operations, deletion, production changes and external publishing.
- Explain the core concepts and architecture of Approval Workflows.
- Apply Approval Workflows in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Risk-Based Approval
Low-risk automatic, medium-risk policy-based and high-risk mandatory human approval.
- Explain the core concepts and architecture of Risk-Based Approval.
- Apply Risk-Based Approval in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Approval Audit Trail
Record agent, requested action, requester, approver, decision, time and reason.
- Explain the core concepts and architecture of Approval Audit Trail.
- Apply Approval Audit Trail in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Threat Model, Tool Security, Least Privilege Agents, Prompt Injection Defense, Secrets Management, Agent Identity.
Threat Model
Prompt and indirect injection, exfiltration, malicious documents, tool misuse, privilege escalation, prompt disclosure, memory poisoning, compromised tools and unsafe delegation.
- Explain the core concepts and architecture of Threat Model.
- Apply Threat Model in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Tool Security
Identity, permissions, scope, validation, rate limits and audit trail for every tool.
- Explain the core concepts and architecture of Tool Security.
- Apply Tool Security in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Least Privilege Agents
Give agents only the tools and permissions required for the current task.
- Explain the core concepts and architecture of Least Privilege Agents.
- Apply Least Privilege Agents in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Prompt Injection Defense
Separate instructions from data, distrust external content, validate actions, constrain tools, require approvals and isolate sensitive operations.
- Explain the core concepts and architecture of Prompt Injection Defense.
- Apply Prompt Injection Defense in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Secrets Management
Keep API keys out of prompts, logs, browsers, memory and repositories.
- Explain the core concepts and architecture of Secrets Management.
- Apply Secrets Management in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Agent Identity
User, service, agent and tool identity plus delegated authorization.
- Explain the core concepts and architecture of Agent Identity.
- Apply Agent Identity in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Input Guardrails, Output Guardrails, Tool Guardrails.
Input Guardrails
Validate requests, prohibited actions, malformed input and sensitive information.
- Explain the core concepts and architecture of Input Guardrails.
- Apply Input Guardrails in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Output Guardrails
Validate structured output, sensitive information, required fields and business policies.
- Explain the core concepts and architecture of Output Guardrails.
- Apply Output Guardrails in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Tool Guardrails
Validate tool, arguments, permissions, user and risk before execution.
- Explain the core concepts and architecture of Tool Guardrails.
- Apply Tool Guardrails in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Why Agent Evaluation Is Different, Evaluation Dataset, Tool-Use Evaluation, Task Completion Evaluation, Agent Trajectory Evaluation, Regression Testing.
Why Agent Evaluation Is Different
Evaluate final answers, tool selection and parameters, task completion, workflow, cost and latency across multi-step systems.
- Explain the core concepts and architecture of Why Agent Evaluation Is Different.
- Apply Why Agent Evaluation Is Different in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Evaluation Dataset
Define task, expected outcome, allowed tools and expected constraints.
- Explain the core concepts and architecture of Evaluation Dataset.
- Apply Evaluation Dataset in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Tool-Use Evaluation
Measure correct tools and parameters, sequence, unnecessary calls and failures.
- Explain the core concepts and architecture of Tool-Use Evaluation.
- Apply Tool-Use Evaluation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Task Completion Evaluation
Measure goal completion, artifact generation, user constraints and avoided unauthorized actions.
- Explain the core concepts and architecture of Task Completion Evaluation.
- Apply Task Completion Evaluation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Agent Trajectory Evaluation
Analyze observable request -> tools -> state changes -> result without hidden chain-of-thought.
- Explain the core concepts and architecture of Agent Trajectory Evaluation.
- Apply Agent Trajectory Evaluation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Regression Testing
Run benchmark datasets and compare old vs new agent versions before deployment.
- Explain the core concepts and architecture of Regression Testing.
- Apply Regression Testing in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Agent Tracing, Workflow Visualization, Production Metrics, Agent Debugging.
Agent Tracing
Run ID, agent, node, tool, latency, status, errors and token usage.
- Explain the core concepts and architecture of Agent Tracing.
- Apply Agent Tracing in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Workflow Visualization
Visualize supervisor, specialist, tool, reviewer and final-output flow for admins and debugging.
- Explain the core concepts and architecture of Workflow Visualization.
- Apply Workflow Visualization in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Production Metrics
Tasks/day, completion and failure rates, escalation, tool errors, latency, tokens, cost and approval frequency.
- Explain the core concepts and architecture of Production Metrics.
- Apply Production Metrics in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Agent Debugging
Bad routing, hallucinated arguments, repeated calls, infinite loops, missing context, bad memory and retrieval errors.
- Explain the core concepts and architecture of Agent Debugging.
- Apply Agent Debugging in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Agent Cost Model, Cost Optimization, Agent Quotas.
Agent Cost Model
Account for planning, selection, tool, analysis, verification and final-response model calls.
- Explain the core concepts and architecture of Agent Cost Model.
- Apply Agent Cost Model in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Cost Optimization
Smaller routing models, caching, iteration limits, parallelization, context reduction, model routing and tool-result compression.
- Explain the core concepts and architecture of Cost Optimization.
- Apply Cost Optimization in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Agent Quotas
Requests, tokens and cost per user plus agent-run and tool-call limits.
- Explain the core concepts and architecture of Agent Quotas.
- Apply Agent Quotas in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Agent Latency, Parallelization, Caching.
Agent Latency
Measure model, tool, database, retrieval and total task latency.
- Explain the core concepts and architecture of Agent Latency.
- Apply Agent Latency in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Parallelization
Execute independent research or processing tasks concurrently and combine results.
- Explain the core concepts and architecture of Parallelization.
- Apply Parallelization in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Caching
Cache appropriate tool, search, retrieval and static-resource results.
- Explain the core concepts and architecture of Caching.
- Apply Caching in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
PostgreSQL for Agent Systems, Redis, Vector Storage.
PostgreSQL for Agent Systems
Store users, agents, runs, threads, tasks, tool calls, approvals and artifacts.
- Explain the core concepts and architecture of PostgreSQL for Agent Systems.
- Apply PostgreSQL for Agent Systems in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Redis
Caching, temporary state, rate limits and queue concepts.
- Explain the core concepts and architecture of Redis.
- Apply Redis in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Vector Storage
Knowledge, semantic search and long-term-memory storage.
- Explain the core concepts and architecture of Vector Storage.
- Apply Vector Storage in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Agent REST APIs, Streaming Agent Events, Agent Dashboard.
Agent REST APIs
Create run, get run, approve, cancel and event endpoints for agent tasks.
- Explain the core concepts and architecture of Agent REST APIs.
- Apply Agent REST APIs in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Streaming Agent Events
Show operational status such as planning, searching, calling tools, waiting for approval and generating artifacts without hidden reasoning.
- Explain the core concepts and architecture of Streaming Agent Events.
- Apply Streaming Agent Events in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Agent Dashboard
Active, completed and failed runs, costs, tool calls and approvals.
- Explain the core concepts and architecture of Agent Dashboard.
- Apply Agent Dashboard in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Agent UI, Human Approval UI, Artifact UI.
Agent UI
Chat, task panel, progress, artifacts, approvals, status and errors.
- Explain the core concepts and architecture of Agent UI.
- Apply Agent UI in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Human Approval UI
Present action, subject and proposed change with approve and reject controls.
- Explain the core concepts and architecture of Human Approval UI.
- Apply Human Approval UI in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Artifact UI
Display and manage reports, documents, spreadsheets, code and structured data.
- Explain the core concepts and architecture of Artifact UI.
- Apply Artifact UI in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Scheduled Agents, Event-Driven Agents, Workflow Automation.
Scheduled Agents
Daily reports, weekly summaries, reconciliation and content monitoring.
- Explain the core concepts and architecture of Scheduled Agents.
- Apply Scheduled Agents in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Event-Driven Agents
Trigger from new records, API events, uploads and business events.
- Explain the core concepts and architecture of Event-Driven Agents.
- Apply Event-Driven Agents in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Workflow Automation
Support-ticket classification -> knowledge -> response draft -> human approval -> close.
- Explain the core concepts and architecture of Workflow Automation.
- Apply Workflow Automation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Customer Support Agent, HR Agent, Sales Agent, Finance Operations Agent, Developer Agent, Research Agent.
Customer Support Agent
Customer identification, knowledge retrieval, ticket lookup, response generation and human escalation.
- Explain the core concepts and architecture of Customer Support Agent.
- Apply Customer Support Agent in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
HR Agent
Policy Q&A, leave policy, employee documents and onboarding workflows.
- Explain the core concepts and architecture of HR Agent.
- Apply HR Agent in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Sales Agent
Lead and product information, recommendations and follow-up workflows.
- Explain the core concepts and architecture of Sales Agent.
- Apply Sales Agent in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Finance Operations Agent
Invoice lookup, reconciliation assistance, reports and anomaly flags with approval for sensitive actions.
- Explain the core concepts and architecture of Finance Operations Agent.
- Apply Finance Operations Agent in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Developer Agent
Repository analysis, code, tests, documentation and issue analysis in a controlled environment.
- Explain the core concepts and architecture of Developer Agent.
- Apply Developer Agent in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Research Agent
Query generation, retrieval, source collection, synthesis and report generation.
- Explain the core concepts and architecture of Research Agent.
- Apply Research Agent in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Vision Agents, Document Agents, Voice Agent Concepts.
Vision Agents
Analyze screenshots, images, charts and scanned documents.
- Explain the core concepts and architecture of Vision Agents.
- Apply Vision Agents in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Document Agents
Process PDF, DOCX, spreadsheets, presentations and structured files.
- Explain the core concepts and architecture of Document Agents.
- Apply Document Agents in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Voice Agent Concepts
Speech -> text -> agent -> tools -> response -> speech.
- Explain the core concepts and architecture of Voice Agent Concepts.
- Apply Voice Agent Concepts in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Unit Testing, Agent Integration Testing, Failure Simulation.
Unit Testing
Test tools, validators, routing and permissions.
- Explain the core concepts and architecture of Unit Testing.
- Apply Unit Testing in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Agent Integration Testing
Test agent -> tool -> database -> result integrations.
- Explain the core concepts and architecture of Agent Integration Testing.
- Apply Agent Integration Testing in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Failure Simulation
Simulate unavailable APIs, model timeouts, tool failures, malformed responses, database outages and human rejection.
- Explain the core concepts and architecture of Failure Simulation.
- Apply Failure Simulation in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Docker Fundamentals, Containerized Agent Platform.
Docker Fundamentals
Images, containers, Dockerfiles, volumes, networking and environment variables.
- Explain the core concepts and architecture of Docker Fundamentals.
- Apply Docker Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Containerized Agent Platform
Containerize FastAPI, agent services, PostgreSQL and Redis.
- Explain the core concepts and architecture of Containerized Agent Platform.
- Apply Containerized Agent Platform in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Continuous Integration, Agent Evaluation Gate.
Continuous Integration
Code -> tests -> agent evaluation -> build -> security checks -> deploy.
- Explain the core concepts and architecture of Continuous Integration.
- Apply Continuous Integration in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Agent Evaluation Gate
Block deployment when task completion falls, security evaluation fails or regression thresholds are exceeded.
- Explain the core concepts and architecture of Agent Evaluation Gate.
- Apply Agent Evaluation Gate in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
AWS Fundamentals, Production Agent Deployment, Scaling Agent Systems.
AWS Fundamentals
IAM, EC2, S3, RDS, networking, secrets, logs, monitoring and container concepts.
- Explain the core concepts and architecture of AWS Fundamentals.
- Apply AWS Fundamentals in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Production Agent Deployment
Client -> API -> agent runtime -> tools -> database -> external services.
- Explain the core concepts and architecture of Production Agent Deployment.
- Apply Production Agent Deployment in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Scaling Agent Systems
Stateless APIs, durable state, queues, workers, concurrency, persistence and failover.
- Explain the core concepts and architecture of Scaling Agent Systems.
- Apply Scaling Agent Systems in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Designing Agent Systems, Single vs Multi-Agent Design, Enterprise Architecture.
Designing Agent Systems
Design an enterprise service agent with CRM, knowledge and ticket access; define tools, permissions, state, memory, RAG, approval, failures and monitoring.
- Explain the core concepts and architecture of Designing Agent Systems.
- Apply Designing Agent Systems in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Single vs Multi-Agent Design
Justify multiple agents and determine whether one agent could solve the task more efficiently.
- Explain the core concepts and architecture of Single vs Multi-Agent Design.
- Apply Single vs Multi-Agent Design in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
Enterprise Architecture
User -> API gateway -> agent runtime -> supervisor -> specialists -> MCP/A2A -> enterprise tools -> data -> observability.
- Explain the core concepts and architecture of Enterprise Architecture.
- Apply Enterprise Architecture in a full stack or Generative AI product.
- Evaluate implementation trade-offs, security and production considerations.
26 Hands-On Production Capstones & Microservices
Build, deploy, and showcase real-world enterprise architectures on GitHub to prove your production engineering readiness:
Enterprise E-Commerce Microservices & Event-Driven Streaming Platform
Architected with Spring Boot 3.3, Apache Kafka event streams, Redis distributed caching, React 19 UI, and PostgreSQL. Features distributed ACID transaction sagas, payment webhook handling, dynamic inventory locking, and Dockerized Kubernetes deployment.
High-Throughput Banking & Core Transaction Engine
Concurrent multithreaded financial transaction ledger with ACID compliance, optimistic row locking, idempotent payment endpoints, and audit logging.
Real-Time Logistics & Fleet Tracking Service
Bi-directional live vehicle telemetry dashboard processing 10,000+ geo-coordinate events/sec with live map rendering and ETA calculations.
Multi-Tenant SaaS Subscription & Webhook Gateway
Multi-tenant automated billing engine with webhook signature verification, dynamic token bucket rate-limiting, and tenant data isolation schemas.
Distributed URL Shortener & Analytics System (Bitly Scale)
Low-latency URL redirection engine with distributed ID generation (Snowflake), sub-5ms Redis caching, and real-time click analytics.
Automated Cloud DevOps CI/CD Pipeline on AWS
Production containerization pipeline with automated testing, sonar code quality gates, container image vulnerability scanning, and zero-downtime rolling deploys.
AI-Powered Code Reviewer & Assessment Engine
Automated coding interview evaluator that parses Java AST trees, detects algorithmic time complexity, and simulates 1-on-1 voice technical interview feedback.
MockAttempt Academy vs. Traditional Bootcamps & Self-Study
Transparent side-by-side comparison of daily schedule, duration, curriculum, and placement support:
| Feature & Deliverables | MockAttempt Fast-Track Track | Expensive Bootcamps | Self-Study / YouTube |
|---|---|---|---|
| Live Weekend Schedule | Sat & Sun (4 Hours / Day) | 1 - 1.5 Hours / Day | Self-Paced / Inconsistent |
| Duration to Placement Readiness | 4 Months (16 Weeks Weekend) | 6 - 9 Months | 12+ Months (Uncertain) |
| Candidate Placement Guarantee | Unlimited Drives until Placed (or Full Refund) | Limited to 3-6 Months only | None (Apply blindly) |
| Topic Mock Tests & AI Interviews | Integrated for Every Topic (580+ Rounds) | End of Course Only | None |
| Tuition Fee | ₹49,999 ₹98,999 | ₹1,20,000 - ₹2,50,000 | Free (No Mentorship/Jobs) |
Institutional Course Assurances & 100% Placement Policy
100% Job Placement Guarantee
Our placement team arranges unlimited corporate interview drives across 1,050+ hiring partners until you receive an official offer letter. If unplaced, 100% of your tuition fee is refunded.
1-on-1 Expert Faculty Mentorship
Every cohort is taught live by seasoned lead architects from Tier-1 product companies with daily live coding and supervised code reviews.
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