Building and Deploying AI Agents End to End
Course Curriculum
20 modules designed to master the subject.
Module 1: Course Orientation and Mental Model
Understand what AI agents are, where LangChain and LangGraph fit, and real-world use cases.
Understanding AI Agents: Definitions and Mental Models
Master the fundamental shift from static LLM prompts to autonomous AI agents. Learn the core components of agency and how agents differ from traditional software.
The Agent Ecosystem: LangChain vs LangGraph
Navigate the complex landscape of AI orchestration frameworks. Deep dive into why LangChain's chains were not enough and how LangGraph introduces the necessary circular logic for production agents.
Real-World Agent Use Cases and Constraints
Move beyond 'toy' examples and explore the industries being transformed by autonomous agents. Understand the harsh realities of cost, latency, and reliability that define production engineering.
The Production Agent Roadmap
Prepare for the journey ahead. A comprehensive walkthrough of the course architecture, the technologies we will master, and the specific skills you will acquire to build end-to-end agentic systems.
Module 2: Foundations of Agent Design
Explore core components, stateless vs stateful design, and deterministic vs adaptive flows.
The Four Pillars of Agent Anatomy
Deconstruct the AI agent into its core components. Deep dive into the Reasoning Engine, Planning, Memory, and Tool Use that enable autonomous behavior.
Agent Memory: Stateless vs Stateful Design
Discover the architectural differences between one-off AI interactions and persistent agentic sessions. Master the technical implementation of state in production environments.
Deterministic vs Adaptive Workflows
Master the spectrum of control in AI systems. Learn when to force an agent down a strict path and when to let the LLM's autonomy take the lead.
Choosing the Right Orchestrator: LangChain or LangGraph?
A definitive guide to framework selection. Learn the specific technical triggers that signal when a simple chain is insufficient and a graph-based architecture is required.
Module 3: Large Language Models in Agent Systems
Select models for different tasks and manage cost, latency, reliability, and guardrails.
The Right Brain for the Job: Model Selection for Agents
Master the art of matching LLMs to agentic tasks. Explore the specific benchmarks and features (like function calling and structured output) that make a model 'Agent-Ready'.
The Iron Triangle: Cost, Latency, and Reliability
Navigate the inevitable trade-offs of production AI engineering. Learn to balance the 'Iron Triangle' to deliver profitable and performant agentic systems.
Token Economics: Managing Context and Constraints
Master the technical limitations of tokens in agentic systems. Learn strategies for summarization, message pruning, and context management to prevent system overflow.
Agent Safety: Guardrails and Failure Handling
Build resilient agentic systems that fail gracefully. Master the patterns for output validation, prompt injection protection, and automated error recovery.
Module 4: Building Your First Agent (Conceptual)
Define agent responsibilities, tool abstraction, state flow, and observability.
Defining Agent Responsibilities: The Scope of Agency
Learn how to define the 'Role' of your agent to prevent scope creep and logic failures. Master the art of the System Prompt as a mission statement.
The Interface of Action: Tool Abstraction
Master the art of exposing functions to an LLM. Learn how to write clear, unambiguous tool descriptions and set safety boundaries for agentic actions.
The Lifecycle of an Agent Request
Trace the flow of data from user input to agent action to persistent state. Understand the synchronization between the interface and the orchestrator.
Observability: Seeing Inside the Black Box
Master the tools of the trade for debugging autonomous systems. Learn how to use LangSmith and OpenTelemetry to trace agent decisions in real-time.
Module 5: Multi-Step Reasoning and Control Flow
Master sequential vs branching workflows, conditional execution, and human-in-the-loop loops.
Mapping Thought: Sequential vs Branching Workflows
Learn how to structure complex reasoning. Master the design patterns for linear processes versus decision-heavy branching logic in autonomous agents.
Self-Correction: Conditional Execution and Retries
Build resilient agents that don't give up. Master the patterns for retry logic, error feeding, and recursive self-correction in LangGraph.
The Human in the Loop: Interrupts and Checkpoints
Master the most critical safety pattern in agentic systems. Learn how to pause an autonomous agent, wait for human approval, and resume execution seamlessly.
Failing Gracefully: Error Recovery Strategies
Prepare for the worst-case scenario. Master the technical strategies for handling model timeouts, tool crashes, and unexpected agentic behavior in production.
Module 6: LangGraph as an Agent Orchestration Layer
Learn graph-based thinking for agents using nodes, edges, and state transitions.
Graph-Based Thinking for Agents
Shift your mental model from sequential scripts to complex state machines. Learn why graphs are the superior abstraction for production AI agency.
The Mechanics of LangGraph: Nodes and Edges
Master the technical API of LangGraph. Learn how to define state schemas, write node functions, and architect conditional routing for complex autonomous workflows.
Agents That Live Forever: Managing Long-Running Tasks
Overcome the limitations of ephemeral scripts. Learn how to architect agents that maintain state across hours, days, or weeks of async execution.
Modularity and Scale: Scaling Agent Complexity
Master the art of architectural decomposition. Learn how to use sub-graphs and multi-agent patterns to prevent your LangGraph code from becoming unmanageable.
Module 7: Isolating Agents with Containers
Understand why agents must be isolated and how to containerize runtimes securely.
The Security Perimeter: Why Agents Must Be Isolated
Protect your infrastructure from the 'Autonomous Chaos'. Learn the risks of raw tool execution and why virtualization is the non-negotiable standard for production agents.
The Agent Box: Containerizing Agent Runtimes
Build a secure, reproducible environment for agent execution. Learn how to design minimalist Dockerfiles and handle persistent data in ephemeral containers.
Hard Limits: Resource Quotas and Runtime Security
Master the operational safety of AI agents. Learn how to implement CPU, Memory, and Disk quotas to protect your infrastructure from runaway processes.
Secrets Management: Protecting Keys in an Autonomous World
Keep your API keys, passwords, and credentials safe. Learn how to inject secrets into isolated agent runtimes without exposing them to the LLM's brain.
Module 8: Running Multiple Agents Safely
Strategies for process isolation, concurrency, throttling, and inter-agent communication.
Scaling the Agency: One Agent vs Many Agents
Master the shift from solo agents to collaborative swarms. Learn how to architect multi-agent systems that solve problems greater than the sum of their parts.
The Wall of Silence: Process Isolation for Swarms
Master the infrastructure for multi-agent systems. Learn how to prevent 'Crosstalk' and ensure that multiple autonomous agents can operate on the same system without conflict.
Velocity and Stability: Concurrency and Throttling
Balance speed with reliability. Learn how to manage high-volume agentic requests without triggering API rate limits or crashing your infrastructure.
The Agent Dialogue: Inter-Agent Communication
Master the protocol of collaboration. Learn how to design standardized messages and state-passing patterns for multi-agent swarms.
Module 9: Designing a UI for Agent Interaction
Principles of agent-facing UIs, streaming responses, and building user trust.
Agent UI Principles: Building for Autonomy
Design interfaces that make autonomous agents tangible. Learn the core principles of feedback, transparency, and control in agentic user experiences.
Interaction Models: Chat-based vs Task-based Interfaces
Master the two primary UI patterns for AI agents. Learn when to use a free-form chat and when to use a structured, task-driven dashboard in React.
The Heartbeat of the Agent: Streaming and Progress
Master the technical implementation of Server-Sent Events (SSE) and progress tracking. Learn how to bridge the gap between long-running agentic tasks and real-time React UIs.
The Explainability Gap: User Trust and Transparency
Master the psychology of AI interaction. Learn how to provide citations, show tool intent, and design for 'Show Your Work' transparency to build verified user trust.
Module 10: Connecting UI to Agents
Manage the request lifecycle, async execution, and error feedback in React-based agent UIs.
The Architecture of Agency: Connecting the Stack
Bridge the gap between Python reasoning and Javascript interfaces. Learn the architectural blueprints for a production-grade agent system using LangGraph, FastAPI, and React.
The Agent API: Building the Backend Backbone
Master the interface between the web and the brain. Learn how to build a robust FastAPI backend that handles streaming, thread management, and background agent tasks.
Identity and Context: Auth in Agent Systems
Master the intersection of security and personalization. Learn how to secure your agents with JWT and how to safely pass user context into the LLM's reasoning engine.
Shipping to Production: Deployment Checklist
Cross the finish line. Learn the infrastructure, monitoring, and scaling steps required to take your local agent from a prototype to a live, production-grade service.
Module 11: System Architecture Overview
Master frontend, backend, agent, and model layers with logging and cost governance.
The Art of the Prompt: Writing Tools Models Understand
Engineer the perfect interface for your agent. Learn how to write tool names, descriptions, and schemas that maximize LLM accuracy and minimize tool calling errors.
Handling Complexity: Nested Tool Schemas
Move beyond simple strings and integers. Learn how to design complex, nested Pydantic models that allow agents to handle multi-step data entry in a single tool call.
Intelligent Tools: Context-Aware Execution
Give your tools a memory. Learn how to design tools that adjust their behavior based on the current state, user history, and orchestrator metadata.
The Library of Action: High-Cardinality Tool Sets
Scale your agent's capabilities without overwhelming the model. Learn the 'Tool RAG' pattern to manage systems with hundreds or thousands of available actions.
Module 12: On-Premise and Local Deployment
Run agents on local infrastructure with Ollama, hardware optimization, and model benchmarks.
Private Power: Why Run AI Agents Locally?
Master the move from cloud-dependent to sovereign AI. Learn the privacy, cost, and reliability advantages of running autonomous agents on your own hardware.
The Local Hub: Mastering Ollama for Agents
Master the most popular local LLM runtime. Learn how to pull models, manage context, and integrate Ollama into your LangChain and LangGraph workflows.
The Intelligence Gap: Local vs Cloud Performance
Know your model's limits. Compare the reasoning capabilities of local 8B models against cloud giants and learn when to switch between them for maximum efficiency.
The Agent Engine: Hardware and Optimization
Master the physical layer of AI. Learn about GPU selection, RAM requirements, and the technical magic of quantization to run massive models on consumer hardware.
Module 13: Agentic RAG: Evolution of Retrieval
Master advanced retrieval, self-reranking, query expansion, and hybrid search patterns.
Agentic RAG: Evolution of Retrieval
Move beyond 'simple' retrieval. Learn why agentic RAG—where the model decides when and how to search—is the future of factual AI systems.
The Quality Filter: Self-Reranking and Query Expansion
Master the techniques for high-precision retrieval. Learn how to use 'LLM-as-a-Judge' to re-rank search results and how to generate multiple search variations for better coverage.
The Retrieval Toolbox: Vector, Graph, and Hybrid Search
Master the different flavors of knowledge retrieval. Learn when to use semantic vector search, structured graph search, or a hybrid of both to give your agent perfect memory.
The Retrieval Wall: Avoiding the Bottleneck
Master the performance of knowledge systems. Learn how to optimize throughput, reduce search latency, and use semantic caching to keep your agents responsive.
Module 14: Multimodal Agents
Build agents that see, hear, and speak using Vision, Audio, and Video integration.
The Visionary Agent: Integrating Image Reasoning
Master the intersection of sight and thought. Learn how to build agents that analyze UI screenshots, medical images, and construction blueprints in production.
The Conversational Agent: Voice and Audio
Build agents that listen and speak. Master the integration of Whisper (STT) and ElevenLabs (TTS) to create low-latency, empathetic voice experiences.
Moving Pictures: Video Analysis and Interaction
Master the temporal dimension of AI. Learn how to build agents that understand events over time, detect anomalies in security footage, and summarize long-form video content.
The Multi-Sensory Agent: Unified Modalities
Engineer the ultimate autonomous system. Learn how to design LangGraph workflows that process text, images, and audio in a single, synchronized reasoning loop.
Module 15: Memory and Long-Term Context
Implement persistent memory, knowledge graphs, and privacy-first storage strategies.
The Agent's Diary: Persistent Memory
Build agents that grow over time. Learn how to implement persistent memory that survives between sessions and how to distinguish between facts and preferences.
Relational Wisdom: Graph Databases in AI
Go beyond similarity. Learn how to use Neo4j and Knowledge Graphs to store complex associations and transitive relationships for your agents.
The Forgetful Agent: Memory Management Strategies
Master the lifecycle of memory. Learn how to implement 'Memory Aging', 'Consolidation', and 'Relevance Filtering' to keep your agent's mind sharp and uncluttered.
The Digital Eraser: Privacy and the Right to Forget
Master the ethics and legality of agentic memory. Learn how to implement GDPR-compliant deletion and how to ensure your agent's identity is safe from 'Memory Leaks'.
Module 16: AgentOps and Production Monitoring
Trace agent decisions, automate evaluations, and monitor cost/performance at scale.
The Agent Dashboard: Tracing and Debugging
Look inside the black box. Master LangSmith and open-source tracing tools to debug multi-step agentic loops and find exactly where your logic failed.
Measuring Intelligence: Evaluation (Eval) Pipelines
Master the science of AI testing. Learn how to build automated evaluation pipelines that use 'LLM-as-a-Judge' to grade your agent's performance across 100s of scenarios.
The Bottom Line: Cost and Performance Ops
Master the economics of AI. Learn how to track token usage, set hard spend budgets, and optimize the 'Price-per-Task' of your autonomous swarms.
The Agent Lifecycle: CI/CD for LLM Apps
Engineer the release pipeline. Learn how to manage versioning for prompts, graphs, and models, and how to perform blue-green deployments for autonomous agents.
Module 17: Specialized Agents: Coding and Automation
Architect coding assistants that manage repos, run tests, and handle Git workflows.
The Digital Architect: Building Coding Agents
Master the most powerful agent use case. Learn how to architect agents that understand recursive folder structures, read codebases, and propose complex refactors.
The Hands of the Coder: File System Tools
Master the low-level interactions of coding agents. Learn how to design robust tools for reading, writing, and executing code in a sandboxed file system.
The Team Player: Git and CI/CD Integration
Master the protocol of collaboration. Learn how to build agents that commit code, create branches, and handle PR feedback like a human engineer.
The Self-Verifying Agent: Writing Tests for Code
Ensure quality through automation. Learn how to build agents that follow Test-Driven Development (TDD) principles, writing their own unit tests before submitting features.
Module 18: Scaling and Infrastructure
Scale agent backends horizontally, manage distributed state, and optimize with caching.
Agent Swarms at Scale: Horizontal Scaling
Master the infrastructure of mass agency. Learn how to scale your agent backends horizontally and how to handle thousands of concurrent autonomous workflows.
The Global Brain: Distributed State Management
Master the data layer of mass agency. Learn how to maintain perfectly synchronized agent states across multiple regions, servers, and millions of concurrent threads.
The Agent Traffic Jam: Load Balancing
Master the flow of autonomous requests. Learn why traditional round-robin load balancing fails for long-running agents and how to implement 'Resource-Aware' routing.
The Speed of Thought: Scaling via Caching
Master the performance of mass-market AI. Learn how to use Semantic Caching and Context Prefetching to serve millions of users without bankrupting your token budget.
Module 19: Ethics, Governance, and Safety
Navigate AI bias, red-teaming, global regulations, and human-centric design.
The Bias Mirror: Fairness in Agent Behavior
Engineer the ethics of your AI. Learn how to detect, measure, and mitigate cognitive biases in autonomous reasoning engines to build fair and inclusive systems.
Breaking the Agent: Red Teaming and Security
Think like a hacker to protect your users. Learn how to perform adversarial testing, simulate prompt injections, and stress-test your agent's safety guardrails.
The Law of the Machine: AI Regulations
Navigate the global legal landscape of AI. Learn about the EU AI Act, risk classifications, and how to ensure your agent stays compliant with modern privacy and safety laws.
The Human Partner: Human-Centric Agent Design
Design for dignity and control. Learn how to build agents that feel like partners, not black boxes, and how to avoid 'Automation Anxiety' in your users.
Module 20: The Future of Autonomous Agents
Explore embodied AI, Large Action Models (LAMs), and the rise of agent economies.
The Next Leap: Embodied Agents and Robotics
Cross the bridge from digital to physical. Learn how the reasoning engines you've built are beginning to inhabit physical bodies and the unique challenges of real-world agency.
Universal Command: Large Action Models (LAMs)
Go beyond text generation. Learn about the rise of Large Action Models (LAMs) and browser-based agents that can navigate any website as easily as a human.
The Agent Economy: Swarms and Societies
Master the macro perspective. Learn how trillions of autonomous agents will interact, trade values, and form complex digital economies to solve global problems.
The Agent Engineer: Conclusion and Next Steps
Cross the finish line. Summarize the 20-module journey and learn how to position yourself as a leader in the next decade of autonomous AI development.
Course Overview
Format
Self-paced reading
Duration
Approx 6-8 hours
Found this course useful? Support the creator to help keep it free for everyone.
Support the Creator