·Agentic AI
Final Capstone: The Autonomous Market Analyst
Bringing it all together. Build a complete, secure, and sovereign multi-agent system that researches stocks and generates a report.
Agentic AI
98 articles
·Agentic AI
Module 1 Lesson 1: What Agentic AI Is
The shift from generative to agentic. Understanding the third wave of AI development.
·Agentic AI
Module 1 Lesson 2: Agents vs Chatbots
Defining the boundary. Why adding a 'Search' tool to a chatbot doesn't make it an agent.
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Module 1 Lesson 3: Reasoning, Planning, and Acting
The engine of agency. Understanding the ReAct pattern and how LLMs navigate complexity.
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Module 1 Lesson 4: Deterministic vs Non-Deterministic Systems
Stability in the storm. Managing the unpredictability of LLMs within structured software environments.
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Module 1 Lesson 5: When You Should Not Use Agents
Avoiding the 'Everything is a Nail' trap. Understanding the cost, latency, and reliability trade-offs.
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Module 1 Lesson 6: Real-World Agent Use Cases
From theory to production. Where agents are actually providing business value today.
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Module 1 Wrap-up: Your First Agentic Decision
Hands-on: Compare a chatbot vs an agent workflow and identify agent-worthy problems.
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Module 10 Lesson 1: What ADK Solves
Standardizing the agent factory. Understanding the need for enterprise-grade management, configuration, and policies.
·Agentic AI
Module 10 Lesson 2: Agent Lifecycle Management
From cradle to grave. Managing versioning, rolling updates, and retirement of AI agents.
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Module 10 Lesson 3: Configuration-Driven Agents
Separating logic from code. Using YAML and JSON to define agent personas, tools, and constraints.
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Module 10 Lesson 4: Policy and Guardrails
Governance at scale. Implementing global rules that restrict agent behavior regardless of the prompt.
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Module 10 Lesson 5: Enterprise Integration
Connecting to the real world. Integrating agents with SQL, ERP systems, and internal authentication.
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Module 10 Wrap-up: Scaling with Governance
Hands-on: Design a configuration-driven agent with a global PII-filtering policy.
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Module 11 Lesson 1: Side-by-Side Comparison
The Framework Showdown. Comparing the architecture, complexity, and performance of the top 3 agent frameworks.
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Module 11 Lesson 2: When to use LangChain
The Speed Demon. Understanding the scenarios where LangChain's core abstractions outshine more complex frameworks.
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Module 11 Lesson 3: When to use LangGraph
The Enterprise Choice. Why LangGraph is the standard for high-reliability, long-running agent systems.
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Module 11 Lesson 4: When to use CrewAI
The Creative Engine. Scenarios where specialized roles and collaborative personas result in superior quality output.
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Module 11 Lesson 5: Mixing Frameworks
The Hybrid Approach. How to combine LangGraph's control with CrewAI's collaboration for the ultimate system.
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Module 11 Wrap-up: The Strategic Selection
Hands-on: Use a decision matrix to select the right framework for three real-world business scenarios.
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Module 12 Lesson 1: Why Agents Hallucinate
Understanding the glitch. The psychological and technical causes of AI hallucinations in agentic systems.
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Module 12 Lesson 2: Prompt Engineering for Accuracy
Words that matter. Advanced prompting techniques like Chain-of-Thought and Few-Shot that reduce agentic errors by 40%.
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Module 12 Lesson 3: Output Parsing and Validation
Structured safety. Using Pydantic and JSON schemas to ensure the agent's output is machine-readable and error-free.
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Module 12 Lesson 4: Self-Correction Loops
The double-check. Implementing internal loops where one agent reviews and corrects the errors of another.
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Module 12 Lesson 5: Benchmarking Agent Performance
Measuring progress. How to build an Evals suite to test your agent's accuracy, tool usage, and cost over time.
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Module 12 Wrap-up: Engineering for 99% Accuracy
Hands-on: Build a self-correcting agent loop that uses Pydantic to validate outputs.
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Module 13 Lesson 1: Prompt Injection Attacks
Hacking the brain. Understanding how users can trick your agent into bypassing its own rules.
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Module 13 Lesson 2: Data Privacy and PII
Protecting the user. How to handle Personally Identifiable Information (PII) and ensure your agent is GDPR/CCPA compliant.
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Module 13 Lesson 3: Sovereignty and Local Models
Zero data leakage. Running high-performance agents on your own hardware using Ollama, MLX, and Llama.cpp.
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Module 13 Lesson 4: Rate Limiting and Cost Guards
Economic security. Preventing runaway costs and protecting your API keys from exhaustion.
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Module 13 Lesson 5: Compliance and Ethics
The Responsible Engineer. Navigating the legal and ethical landscape of autonomous agents.
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Module 13 Wrap-up: The Secure Perimeter
Hands-on: Secure an agent against jailbreaking and implement a PII redaction layer.
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Module 14 Lesson 1: The Reliability Gap
Why LLMs aren't enough. Understanding the limit of probabilistic reasoning in deterministic business systems.
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Module 14 Lesson 2: Finite State Machines (FSMs)
Old school intelligence. Learning how to define states, transitions, and triggers to create unshakeable logic.
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Module 14 Lesson 3: Using LLMs as State Transitions
The bridge. How to use an LLM to classify user intent and trigger a specific state change in your FSM.
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Module 14 Lesson 4: Constraining Output with Outlines
Mathematically impossible to fail. Using libraries like Outlines and Guidance to force the LLM to follow a specific grammar or regex.
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Module 14 Lesson 5: FSMs in Customer Service
The support architect. Designing an automated ticket system that uses an FSM for policy and an LLM for empathy.
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Module 14 Wrap-up: The Sovereign Architect
Hands-on: Build a state-machine governed agent that handles a login and payment flow.
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Module 15 Lesson 1: Vector Databases for Agents
The Long-Term Memory. Learning how to use Pinecone, Chroma, and PGVector to give your agents a massive knowledge base.
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Module 15 Lesson 2: Semantic Caching
Save time, save money. Using GPTCache to avoid calling the expensive LLM for identical (or similar) queries.
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Module 15 Lesson 3: Monitoring and Logs
The eyes of the system. Implementing real-time tracking for agent health, token usage, and user sentiment.
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Module 15 Lesson 4: Scaling Agent Concurrency
Handling the crowd. How to manage thousands of concurrent agents without crashing your database or hit API limits.
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Module 15 Lesson 5: CI/CD for Agents
Pipelining intelligence. How to automate the testing, benchmarking, and deployment of your agentic code and prompts.
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Module 15 Wrap-up: Engineering for the Millions
Hands-on: Build a simple RAG agent that retrieves context from a local Vector DB before answering.
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Module 16 Lesson 1: The Chat Box is Not Enough
Breaking the box. Why traditional chat interfaces fail to communicate the complexity of multi-agent systems.
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Module 16 Lesson 2: Generative UI
Beyond text. How to use Vercel AI SDK and specialized tokens to render interactive UI components (dashboards, charts) on the fly.
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Module 16 Lesson 3: Streaming Intermediate Steps
Watch the agent work. How to capture and stream the 'Thought' and 'Action' phases to the UI in real-time.
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Module 16 Lesson 4: Designing for Long Latency
Patience is a virtue. How to handle research tasks that take minutes to finish using notifications and async status pages.
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Module 16 Lesson 5: Multi-Modal Agents
See and hear. Designing interfaces that allow agents to process images, diagrams, and voice commands.
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Module 16 Wrap-up: The Experience Engineer
Hands-on: Design a dashboard concept for a multi-agent research crew that shows planning and status.
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Module 2 Lesson 1: Agent Components
Dissecting the agent. Understanding the four pillars: LLM, Memory, Tools, and the Control Loop.
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Module 2 Lesson 2: Agent State
The snapshot of intelligence. Managing variables, message history, and flags within a running agent.
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Module 2 Lesson 3: Short-Term vs Long-Term Memory
The two speeds of learning. Understanding conversation buffers vs vector databases.
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Module 2 Lesson 4: Tool Invocation Basics
The mechanics of action. How LLMs trigger external functions using structured JSON.
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Module 2 Lesson 5: Feedback Loops
Self-correcting AI. How agents learn from errors and retry failed attempts during execution.
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Module 2 Wrap-up: Your First Minimal Agent
Hands-on: Build a manual, single-step agent loop from scratch in Python.
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Module 3 Lesson 1: The ReAct Pattern
Mastering the fundamental agent loop. How to prompt for Thought, Action, and Observation.
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Module 3 Lesson 2: The Planner–Executor Pattern
Divide and conquer. Using one agent to build the map and another to walk the path.
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Module 3 Lesson 3: The Supervisor Pattern
The CEO of Agents. Coordinating multiple specialist agents to solve multi-disciplinary problems.
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Module 3 Lesson 4: The Critic and Validator Pattern
Self-improving AI. Using one model to generate and another to find the flaws.
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Module 3 Lesson 5: Human-in-the-Loop Pattern
The safety net. When and how to pause an autonomous agent to ask for human approval.
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Module 3 Wrap-up: The Strategic Architect
Hands-on: Design a planner-executor flow for a multi-step research task.
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Module 4 Lesson 1: Agents in LangChain
The framework of choice. Understanding the high-level Agent abstractions in LangChain.
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Module 4 Lesson 2: Tool Definitions
Equipping your agent. How to define tools using decorators, pydantic, and base classes.
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Module 4 Lesson 3: Agent Executors
The heartbeat of LangChain agents. Managing the while-loop and handling the ReAct cycle.
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Module 4 Lesson 4: Memory Integration
Connecting the threads. How to implement chat history and windowed memory in LangChain agents.
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Module 4 Lesson 5: Agent Failure Modes
Why agents break. Understanding hallucinations, tool loops, and parsing errors in LangChain.
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Module 4 Lesson 6: Cost and Loop Control
Protecting your wallet. Setting limits on iterations, time, and tokens in LangChain agent executors.
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Module 4 Wrap-up: Your First LangChain Agent
Hands-on: Build a tool-using LangChain agent and observe the reasoning traces in real-time.
·Agentic AI
Module 5 Lesson 1: Infinite Loops
The AI feedback trap. Why agents get stuck in repetitive cycles and how to break them.
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Module 5 Lesson 2: Tool Abuse
When models get lazy. Understanding how agents take shortcuts or misuse tools to avoid difficult reasoning.
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Module 5 Lesson 3: Hallucinated Actions
Ghost tools. How to handle agents that try to execute functions that don't exist in their toolbox.
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Module 5 Lesson 4: Debugging Difficulty
The Black Box problem. Why traditional debuggers fail and how to use traces to find the glitch.
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Module 5 Lesson 5: Why Control Flow Matters
From magic to structure. Why giving an agent too much freedom is a recipes for disaster.
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Module 6 Lesson 1: Why LangGraph Exists
The successor to the Loop. Understanding the need for cyclic graphs in agent development.
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Module 6 Lesson 2: Graph-Based Agent Architecture
Designing the flowchart of intelligence. Understanding nodes, edges, and state transitions.
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Module 6 Lesson 3: Nodes, Edges, and State
The building blocks of LangGraph. Learning how to write functions that play nice with the graph schema.
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Module 6 Lesson 4: Deterministic Execution
Stability in the graph. Using edges and state counters to prevent infinite loops and ensure predictable behavior.
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Module 6 Lesson 5: Entry, Exit, and Guard Nodes
Managing the perimeter. Using specialized nodes to filter inputs and validate final results.
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Module 6 Wrap-up: Migrating to the Graph
Hands-on: Convert a simple LangChain AgentExecutor into a controlled LangGraph workflow.
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Module 7 Lesson 1: Conditional Routing
The logic of the graph. Mastering how to route control based on tool calls, state variables, and LLM output.
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Module 7 Lesson 2: Loop Guards
Setting boundaries. How to implement hard limits on cycles and token usage within your agent graphs.
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Module 7 Lesson 3: Retry and Fallback Nodes
Resilience by design. How to handle tool failures and rate limits within the agent graph.
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Module 7 Lesson 4: Validation Nodes
The zero-trust agent. How to implement verification steps that ensure output quality before finishing.
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Module 7 Lesson 5: Human Approval Nodes
The pause button. Implementing 'interrupts' that allow humans to review and edit agent state before final actions.
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Module 7 Wrap-up: The Master of Graphs
Hands-on: Build a complex research agent with routing, loop guards, and a validation gate.
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Module 8 Lesson 1: What CrewAI Is
From single actors to ensemble casts. Understanding the specialized role-playing framework for multi-agent systems.
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Module 8 Lesson 2: Roles and Responsibilities
Designing the persona. How to write effective Roles, Goals, and Backstories to maximize agent performance.
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Module 8 Lesson 3: Task Decomposition
Breaking it down. How to turn a complex project into a set of discrete, delegated Tasks in CrewAI.
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Module 8 Lesson 4: Agent Collaboration Models
Sequential vs. Hierarchical. Choosing the right organizational structure for your agent team.
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Module 8 Lesson 5: Communication Patterns
How agents share data. Understanding context passing, memory sharing, and the 'Kitchen' environment in CrewAI.
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Module 8 Wrap-up: Your First AI Crew
Hands-on: Build a two-agent research crew that identifies tech trends and writes a report.
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Module 9 Lesson 1: What StrandAgents Are
The lightweight alternative. Understanding the event-driven, streaming-first architecture of StrandAgents.
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Module 9 Lesson 2: Event-Driven Agent Design
Reacting in real-time. How to design agents that trigger actions based on external signals rather than internal loops.
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Module 9 Lesson 3: Stateless vs Stateful Agents
Choosing your memory model. When to build agents that remember the past vs agents that treat every event as new.
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Module 9 Lesson 4: Streaming and Async Behavior
Zero latency. Understanding how to build agents that speak while they think using async Python and SSE.
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Module 9 Lesson 5: When StrandAgents Make Sense
Choosing the right tool. Understanding when to use event-driven strands over complex graphs or crews.
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Module 9 Wrap-up: Engineering for Real-Time
Hands-on: Build an event-driven agent pipeline that detects toxicity and triggers a response.