AI Agents: Build & Deploy
Learn to build and deploy autonomous AI agents in this hands-on course. LangChain, LangGraph, CrewAI, AutoGen, OpenAI Assistants, tool integration, multi-agent orchestration, and production deployment. 60+ hours, 6 projects.
Course Snapshot
| Course Code | AIM-605 |
| Duration | 60+ hours |
| Modules | 14 |
| Projects | 6 hands-on projects |
| Level | Intermediate to Advanced |
| Format | Self-paced + live sessions |
| Frameworks | LangChain, LangGraph, CrewAI, AutoGen, OpenAI Assistants |
| Certificate | Yes, upon completion |
| Price | ₹5,999 (early bird ₹3,999) |
Course Overview
AI agents represent the next leap beyond chatbots — autonomous systems that reason, plan, use tools, and collaborate to accomplish complex goals. This course takes you from foundational agent concepts to production-ready multi-agent systems deployed in real environments. You will begin with the theory of agentic architectures, understanding the difference between reactive, deliberative, and LLM-based agents. Then you will build agents with LangChain, mastering chains, tools, and memory before advancing to LangGraph for stateful, cyclic agent workflows. You will integrate the OpenAI Assistants API for managed agent infrastructure, then explore multi-agent frameworks like AutoGen and CrewAI for collaborative task execution. You will wire up real tools — web search, code execution, database queries, and APIs — and tackle advanced topics like computer use agents that interact with GUIs. You will implement persistent memory and knowledge bases, orchestrate teams of agents for complex workflows, and harden agents with safety guardrails and output validation. Finally, you will deploy agents to production with monitoring, observability, and cost controls. By the end, you will have six deployed agent projects and the confidence to build autonomous systems for any domain.
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Enroll Now — Limited Seats →Who This Course Is Built For
This course is designed for specific professional profiles. If you match any of these, you will get maximum value.
Backend Engineers
Developers building AI-powered backends who want to add autonomous agent capabilities to their applications.
AI/ML Engineers
Engineers moving from model training and inference to building systems that use models as reasoning engines for task automation.
Product Managers
PMs who need to understand agent architectures to design AI products, evaluate feasibility, and communicate with engineering teams.
Automation Specialists
Professionals in RPA and workflow automation transitioning to LLM-driven agents for more flexible, intelligent automation.
This Course Is NOT For You If:
- Those with no Python experience — agents require solid programming skills.
- People who only want to use ChatGPT — this course is about building agents, not using them.
- Anyone expecting a no-code course — every module involves substantial Python programming.
What You Will Learn
After completing this course, you will be able to:
- 1 You will be able to explain the architecture of LLM-based agents and the components that make them autonomous.
- 2 You will be able to build agents with LangChain using chains, tools, memory, and custom prompts.
- 3 You will be able to design stateful, multi-step agent workflows with LangGraph including cycles and branching.
- 4 You will be able to use the OpenAI Assistants API to create managed agents with built-in tools and file search.
- 5 You will be able to build multi-agent systems with AutoGen and CrewAI for collaborative task execution.
- 6 You will be able to integrate external tools like web search, code execution, databases, and REST APIs into agents.
- 7 You will be able to build computer use agents that interact with graphical interfaces and automate desktop tasks.
- 8 You will be able to implement short-term, long-term, and shared memory systems for persistent agent state.
- 9 You will be able to orchestrate teams of agents with role-based delegation and conflict resolution.
- 10 You will be able to deploy agents to production with safety guardrails, monitoring, and cost controls.
Download Free Course Syllabus
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Download Free Syllabus →Complete Course Curriculum
14 modules with detailed lessons. Every lesson includes specific learning points.
MODULE 1 — Agent Foundations
4 hours- Definitions: reactive, deliberative, and LLM-based agents
- The autonomy spectrum from scripted to fully autonomous
- Core components: perception, reasoning, action, and memory
- Real-world agent applications and current industry use cases
- Reasoning + Acting interleaved in a single loop
- Thought-action-observation cycles and how LLMs drive them
- Comparing ReAct to chain-of-thought and plan-and-execute patterns
- When ReAct works well and when it breaks down
- Single-agent vs multi-agent architectures
- Hierarchical, flat, and networked agent topologies
- Planning strategies: forward planning, tree of thought, reflection
- Trade-offs between autonomy, reliability, and cost
MODULE 2 — LangChain Agents
5 hours- Models, prompts, and output parsers as building blocks
- Chaining components with LCEL (LangChain Expression Language)
- RunnablePassthrough, RunnableLambda, and parallel execution
- Streaming, batching, and async patterns in LangChain
- Defining tools with @tool decorator and structured schemas
- Binding tools to models for function calling
- Tool selection and the agent executor loop
- Handling tool errors, retries, and fallbacks
- ConversationBufferMemory and windowed memory strategies
- Summary memory for long conversations
- Entity memory for tracking facts across turns
- Vector-backed memory for retrieval-augmented context
MODULE 3 — LangGraph
5 hours- Why LangGraph: stateful, cyclic, and controllable agent graphs
- Nodes, edges, and conditional routing in StateGraph
- Defining and managing shared state with TypedDict
- Compiling graphs and visualizing the agent topology
- Adding cycles for iterative refinement and self-correction
- Breakpoints and human approval gates in agent workflows
- Checkpointing state for resumable agent runs
- Time travel and replaying agent execution for debugging
- Subgraphs and modular agent composition
- Parallel node execution and fan-out/fan-in patterns
- Dynamic tool routing based on intermediate results
- Error handling and recovery in graph execution
MODULE 4 — OpenAI Assistants API
4 hours- Assistants vs Chat Completions: when to use which
- Configuring instructions, model, and temperature for assistants
- Built-in tool types: code interpreter, file search, and function calling
- Managing assistant lifecycle and versioning
- Creating threads for conversation state management
- Adding messages and initiating runs on threads
- Polling run status and handling streaming responses
- Handling tool call submissions and the run lifecycle
- Uploading files and building vector stores for retrieval
- Configuring file search with max results and ranking
- Using code interpreter for data analysis and computation
- Combining multiple tools in a single assistant run
MODULE 5 — AutoGen
4 hours- AutoGen’s conversational agent model and message passing
- AssistantAgent, UserProxyAgent, and GroupChat classes
- Configuring agent personas, system messages, and termination
- Running two-agent conversations and group chats
- Enabling code execution with Docker and local environments
- Safe code execution sandboxes and isolation strategies
- Iterative code generation, execution, and debugging loops
- Handling execution failures and automatic retry logic
- Nested chats and sequential conversation workflows
- Custom agents with specialized roles and capabilities
- Retrieval-augmented agents with document knowledge bases
- Scaling AutoGen with async and batch processing
MODULE 6 — CrewAI
4 hours- Crews, agents, tasks, and tools in CrewAI’s vocabulary
- Defining agent roles, goals, and backstories
- Creating tasks with descriptions, expected outputs, and dependencies
- Assembling crews with process types: sequential and hierarchical
- Assigning tools to agents and configuring delegation
- Running crews and collecting structured output
- Memory and context sharing between crew members
- Handling crew failures and partial task completion
- Hierarchical crews with a manager agent for delegation
- Custom tools and integration with external APIs
- Planning and reflection modes for complex tasks
- Deploying crews as APIs with CrewAI’s built-in server
MODULE 7 — Tool Integration
4 hours- Integrating Tavily, SerpAPI, and DuckDuckGo for web search
- Building browsing agents with Playwright and BeautifulSoup
- Handling dynamic content, authentication, and rate limits
- Caching search results and deduplicating sources
- Connecting agents to SQL databases with natural language queries
- Wrapping REST and GraphQL APIs as agent tools
- Authentication, pagination, and error handling in API tools
- Schema discovery and dynamic tool generation from OpenAPI specs
- Sandboxed code execution with Docker and E2B
- File reading, writing, and manipulation tools
- Git integration for code-aware agents
- Building custom tools with input validation and schemas
MODULE 8 — Computer Use & GUI Agents
4 hours- What computer use agents do: screen reading, clicking, and typing
- Anthropic’s computer use API and OpenAI’s vision-based approaches
- Screenshot analysis and element identification strategies
- Safety, consent, and ethical considerations for GUI automation
- Using Playwright and Selenium for browser automation
- Combining vision models with DOM inspection for robust interaction
- Handling dynamic pages, popups, and authentication flows
- Recording and replaying agent actions for reproducibility
- OS-level automation with pyautogui and accessibility APIs
- Cross-platform considerations for Windows, macOS, and Linux
- Building agents that interact with desktop applications
- Monitoring, logging, and rollback for desktop agent safety
MODULE 9 — Memory & Knowledge
4 hours- Short-term memory: conversation context and working memory
- Long-term memory: vector stores, databases, and file systems
- Episodic memory for past interactions and learned experiences
- Semantic memory for facts, rules, and domain knowledge
- Building retrieval-augmented memory with vector databases
- Chunking, embedding, and indexing strategies for agent knowledge
- Updating knowledge bases dynamically during agent execution
- Hybrid retrieval combining keyword and semantic search
- Shared memory architectures for multi-agent systems
- Blackboard patterns and message queues for agent coordination
- Persistent state with Redis, Postgres, and dedicated memory stores
- Memory compaction and forgetting strategies for efficiency
MODULE 10 — Multi-Agent Orchestration
5 hours- When to use multiple agents vs a single powerful agent
- Role specialization and division of labor principles
- Communication protocols: direct, broadcast, and pub-sub
- Coordination strategies: centralized, decentralized, and hierarchical
- Dynamic task assignment based on agent capabilities
- Manager-worker patterns and task decomposition
- Conflict resolution when agents disagree on actions
- Voting, consensus, and arbitration mechanisms
- Comparing LangGraph, CrewAI, AutoGen, and custom orchestration
- Building custom orchestration layers for domain-specific needs
- Scaling multi-agent systems with async and parallel execution
- Monitoring and debugging complex agent interactions
MODULE 11 — Agent Safety
4 hours- Understanding prompt injection attacks on agent systems
- Input sanitization and output validation strategies
- Tool permission models and least-privilege design
- Sandboxing and isolation for untrusted agent actions
- Using guardrail frameworks like NeMo Guardrails and Guardrails AI
- Structured output enforcement with JSON schemas and Pydantic
- Content filtering and toxicity detection for agent outputs
- Human-in-the-loop checkpoints for high-stakes actions
- Tracing agent decisions with LangSmith and Langfuse
- Logging tool calls, token usage, and latency metrics
- Setting up alerts for agent failures and cost overruns
- Replay debugging and root cause analysis for agent errors
MODULE 12 — Production Deployment
4 hours- Serverless vs containerized vs always-on agent deployments
- Deploying agents as APIs with FastAPI and Flask
- Queue-based architectures for long-running agent tasks
- Scaling strategies: horizontal scaling and worker pools
- Token budgeting and cost tracking for agent runs
- Caching strategies to reduce redundant LLM calls
- Model routing: using cheaper models for simple tasks
- Rate limiting and concurrency control for API stability
- Writing integration tests for agent workflows
- Evaluating agent quality with task-specific benchmarks
- Regression testing when models or prompts change
- Automated deployment pipelines for agent updates
MODULE 13 — Domain-Specific Agents
4 hours- Designing agents for ticket routing, FAQ, and escalation
- Integrating with CRM systems and knowledge bases
- Handling multi-turn conversations with empathy and accuracy
- Measuring CSAT, resolution rate, and deflection metrics
- Building agents that gather, synthesize, and report information
- Using code interpreter tools for data analysis and visualization
- Citing sources and maintaining provenance for trustworthiness
- Generating structured reports with charts and tables
- Automating business processes with agent-driven orchestration
- Integrating with Zapier, n8n, and custom workflow engines
- Handling approvals, notifications, and human handoffs
- Measuring ROI and time savings from agent automation
MODULE 14 — Capstone Projects
3 hours- Selecting a domain and problem that benefits from agent autonomy
- Scoping the project to be achievable in the capstone timeframe
- Designing the agent architecture and tool inventory
- Defining evaluation criteria and success metrics
- Implementing the full agent system from architecture to deployment
- Testing edge cases, failure modes, and recovery scenarios
- Iterating on prompts, tools, and orchestration based on results
- Documenting the system for reproducibility and handoff
- Deploying the agent to a public endpoint or interactive demo
- Recording a walkthrough video showing the agent in action
- Writing a technical blog post about the architecture and lessons
- Gathering peer feedback and planning future improvements
Real-World Projects & Portfolio Outcomes
6 portfolio-worthy projects that prove your skills to employers.
Autonomous Research Assistant
Build a LangChain agent that takes a research question, searches the web, reads multiple sources, and produces a cited summary report with key findings and source links.
Multi-Step Workflow Agent with LangGraph
Create a LangGraph-based agent that handles a complex multi-step business workflow — such as processing a loan application — with conditional routing, human approval gates, and checkpointed state.
OpenAI Assistant with File Search
Build an OpenAI Assistant that answers questions about a set of uploaded documents using file search, with custom function tools for database lookups and email sending.
CrewAI Research Crew
Assemble a CrewAI crew with a researcher, analyst, and writer agent that collaboratively produce a market analysis report on a given industry, with structured output and citations.
Computer Use Automation Agent
Build a computer use agent that automates a repetitive desktop task — such as data entry from a spreadsheet into a web form — using vision models and browser automation.
Production Multi-Agent System
Deploy a multi-agent system to production with at least three specialized agents, shared memory, safety guardrails, monitoring, and cost tracking — solving a real business problem of your choice.
Tools, Technologies & Models Covered
LLM Models Covered
| GPT-4o | OpenAI | Reasoning, tool calling, and multimodal agent tasks |
|---|---|---|
| Claude 3.5 Sonnet | Anthropic | Long-context reasoning and computer use |
| Llama 3.1 | Meta | Open-source agent backbone for self-hosted deployments |
| Mistral Large | Mistral AI | Function calling and structured output |
| Gemini 1.5 Pro | Multimodal agents with long context windows | |
| Phi-3 | Microsoft | Lightweight agents for cost-sensitive tasks |
| Command R+ | Cohere | RAG-optimized agents with tool use |
Tools & Frameworks Covered
| LangChain | Agent chains, tools, and memory | Deep |
|---|---|---|
| LangGraph | Stateful graph-based agent workflows | Deep |
| LangSmith | Tracing and evaluation | Intermediate |
| CrewAI | Multi-agent crew orchestration | Deep |
| AutoGen | Conversational multi-agent framework | Intermediate |
| OpenAI Assistants API | Managed agent infrastructure | Deep |
| Playwright | Browser automation for GUI agents | Intermediate |
| FastAPI | Agent API deployment | Intermediate |
| Langfuse | Open-source LLM observability | Intermediate |
| NeMo Guardrails | Agent safety and guardrails | Intermediate |
| Redis | Agent memory and state stores | Intermediate |
| Docker | Sandboxed code execution | Intermediate |
Book a Free Counselling Call
Not sure which course is right for you? Book a free 1:1 counselling call with our AI training advisors.
Book Free Counselling Call →Your Instructor
Rajesh Kumar
AI Agent Architect & Former Principal Engineer at a Top AI Startup
8+ years building production AI systems and agent platforms
20+ production agent deployments serving millions of requests
3,500+ engineers trained in LLM application development
The best way to learn agent engineering is to build agents that fail, debug why they failed, and make them resilient. Theory only gets you so far — shipping is the real teacher.
Course Team & Curriculum Design
Our teaching team includes two senior AI engineers who have deployed multi-agent systems in production, plus three teaching assistants available on Discord for daily Q&A and project support.
Learning Path & Prerequisites
Prerequisites
- Solid Python programming including async and class-based design
- Understanding of LLM APIs and basic prompt engineering
- Familiarity with REST APIs, JSON, and basic web concepts
- Experience with at least one web framework (FastAPI, Flask, or Django)
- Basic Docker knowledge for containerized deployments
Recommended Learning Paths
Developer building AI-powered applications
- Start with Modules 1-2 for agent fundamentals and LangChain
- Deep-dive Module 3 (LangGraph) for complex workflows
- Use Module 4 (OpenAI Assistants) for managed, low-maintenance agents
- Complete the Autonomous Research Assistant project as your first build
Engineer building multi-agent systems
- Master Modules 1-3 for single-agent foundations
- Focus on Modules 5-6 (AutoGen and CrewAI) for multi-agent patterns
- Study Module 10 (Orchestration) for advanced coordination
- Build the CrewAI Research Crew and Production Multi-Agent System projects
Engineer deploying agents to production
- Complete Modules 1-7 for full agent building capability
- Prioritize Module 11 (Safety) and Module 12 (Deployment)
- Study Module 13 for domain-specific patterns relevant to your industry
- Build the Production Multi-Agent System as your capstone
What Comes After This Course
Pricing & Enrollment
Choose the plan that fits your learning goals. All plans include a 7-day money-back guarantee.
| Self-Paced | ₹5,999 | All 14 modules, 6 projects, community access, certificate |
|---|---|---|
| Early Bird | ₹3,999 | Same as Self-Paced, limited time pricing |
| Cohort + Mentorship | ₹15,999 | Live sessions, 1:1 mentorship, project reviews, priority support |
What Is Included
- Lifetime access to all 14 modules and future updates
- 6 hands-on projects with detailed walkthroughs
- Downloadable code templates and agent starter kits
- Community Discord with instructors and peers
- Certificate of completion recognized by partner companies
- Access to a private library of production agent design patterns
30-day money-back guarantee — if you complete the first three modules and don’t feel this course delivers, get a full refund, no questions asked.
Frequently Asked Questions
Do I need to know LangChain before starting?
No. Module 2 covers LangChain fundamentals from scratch. However, you should be comfortable with Python and have basic experience calling LLM APIs.
Which LLM provider do I need access to?
The course examples use OpenAI, Anthropic, and open-source models via Ollama. You can follow along with any one provider. We recommend starting with OpenAI or Anthropic for the easiest setup, then exploring open-source models in later modules.
How much will I spend on API calls during the course?
Most exercises can be completed for under ₹2,000 in API costs. We provide cost optimization strategies in Module 12 and use caching extensively in the provided templates to keep your spending low.
Is this course about building agents or using existing agent platforms?
This course is about building agents from scratch using frameworks like LangChain, LangGraph, CrewAI, and AutoGen. You will write the code that powers the agents, not just configure a no-code tool.
Will I learn about computer use and GUI agents?
Yes. Module 8 is dedicated to computer use and GUI agents, covering Anthropic’s computer use API, browser automation with Playwright, and desktop automation strategies.
How is this different from the Hugging Face course?
The Hugging Face course focuses on model training, fine-tuning, and the model ecosystem. This course focuses on building systems that use models as reasoning engines — agents that plan, use tools, and accomplish multi-step goals autonomously.
Can I deploy the agents I build to production?
Absolutely. Module 12 covers production deployment in detail, and your capstone project involves deploying a real agent system with monitoring and safety guardrails.
How often is the course updated?
The agent ecosystem moves fast. We update the course quarterly to cover new frameworks, models, and best practices. The lastUpdated field reflects the most recent revision.
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