Agentic AI
Module 16 Lesson 5: Multi-Modal Agents
See and hear. Designing interfaces that allow agents to process images, diagrams, and voice commands.
Read Article →
Latest AI News Today, Artificial Intelligence News, Agentic AI, AI Tools, AI Prompts and AI Courses
Latest Insights
3383 posts
Agentic AI
Module 16 Wrap-up: The Experience Engineer
Hands-on: Design a dashboard concept for a multi-agent research crew that shows planning and status.
Read Article →
Agentic AI
Module 2 Lesson 1: Agent Components
Dissecting the agent. Understanding the four pillars: LLM, Memory, Tools, and the Control Loop.
Read Article →
Agentic AI
Module 2 Lesson 2: Agent State
The snapshot of intelligence. Managing variables, message history, and flags within a running agent.
Read Article →
Agentic AI
Module 2 Lesson 3: Short-Term vs Long-Term Memory
The two speeds of learning. Understanding conversation buffers vs vector databases.
Read Article →
Agentic AI
Module 2 Lesson 4: Tool Invocation Basics
The mechanics of action. How LLMs trigger external functions using structured JSON.
Read Article →
Agentic AI
Module 2 Lesson 5: Feedback Loops
Self-correcting AI. How agents learn from errors and retry failed attempts during execution.
Read Article →
Agentic AI
Module 2 Wrap-up: Your First Minimal Agent
Hands-on: Build a manual, single-step agent loop from scratch in Python.
Read Article →
Agentic AI
Module 3 Lesson 1: The ReAct Pattern
Mastering the fundamental agent loop. How to prompt for Thought, Action, and Observation.
Read Article →
Agentic AI
Module 3 Lesson 2: The Planner–Executor Pattern
Divide and conquer. Using one agent to build the map and another to walk the path.
Read Article →
Agentic AI
Module 3 Lesson 3: The Supervisor Pattern
The CEO of Agents. Coordinating multiple specialist agents to solve multi-disciplinary problems.
Read Article →
Agentic AI
Module 3 Lesson 4: The Critic and Validator Pattern
Self-improving AI. Using one model to generate and another to find the flaws.
Read Article →
Agentic AI
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.
Read Article →
Agentic AI
Module 3 Wrap-up: The Strategic Architect
Hands-on: Design a planner-executor flow for a multi-step research task.
Read Article →
Agentic AI
Module 4 Lesson 1: Agents in LangChain
The framework of choice. Understanding the high-level Agent abstractions in LangChain.
Read Article →
Agentic AI
Module 4 Lesson 2: Tool Definitions
Equipping your agent. How to define tools using decorators, pydantic, and base classes.
Read Article →
Agentic AI
Module 4 Lesson 3: Agent Executors
The heartbeat of LangChain agents. Managing the while-loop and handling the ReAct cycle.
Read Article →
Agentic AI
Module 4 Lesson 4: Memory Integration
Connecting the threads. How to implement chat history and windowed memory in LangChain agents.
Read Article →
Agentic AI
Module 4 Lesson 5: Agent Failure Modes
Why agents break. Understanding hallucinations, tool loops, and parsing errors in LangChain.
Read Article →
Agentic AI
Module 4 Lesson 6: Cost and Loop Control
Protecting your wallet. Setting limits on iterations, time, and tokens in LangChain agent executors.
Read Article →
Agentic AI
Module 4 Wrap-up: Your First LangChain Agent
Hands-on: Build a tool-using LangChain agent and observe the reasoning traces in real-time.
Read Article →
Agentic AI
Module 5 Lesson 1: Infinite Loops
The AI feedback trap. Why agents get stuck in repetitive cycles and how to break them.
Read Article →
Agentic AI
Module 5 Lesson 2: Tool Abuse
When models get lazy. Understanding how agents take shortcuts or misuse tools to avoid difficult reasoning.
Read Article →
Agentic AI
Module 5 Lesson 3: Hallucinated Actions
Ghost tools. How to handle agents that try to execute functions that don't exist in their toolbox.
Read Article →
Agentic AI
Module 5 Lesson 4: Debugging Difficulty
The Black Box problem. Why traditional debuggers fail and how to use traces to find the glitch.
Read Article →
Agentic AI
Module 5 Lesson 5: Why Control Flow Matters
From magic to structure. Why giving an agent too much freedom is a recipes for disaster.
Read Article →
Agentic AI
Module 6 Lesson 1: Why LangGraph Exists
The successor to the Loop. Understanding the need for cyclic graphs in agent development.
Read Article →
Agentic AI
Module 6 Lesson 2: Graph-Based Agent Architecture
Designing the flowchart of intelligence. Understanding nodes, edges, and state transitions.
Read Article →
Agentic AI
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.
Read Article →
Agentic AI
Module 6 Lesson 4: Deterministic Execution
Stability in the graph. Using edges and state counters to prevent infinite loops and ensure predictable behavior.
Read Article →
Agentic AI
Module 6 Lesson 5: Entry, Exit, and Guard Nodes
Managing the perimeter. Using specialized nodes to filter inputs and validate final results.
Read Article →
Agentic AI
Module 6 Wrap-up: Migrating to the Graph
Hands-on: Convert a simple LangChain AgentExecutor into a controlled LangGraph workflow.
Read Article →
Agentic AI
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.
Read Article →
Agentic AI
Module 7 Lesson 2: Loop Guards
Setting boundaries. How to implement hard limits on cycles and token usage within your agent graphs.
Read Article →
Agentic AI
Module 7 Lesson 3: Retry and Fallback Nodes
Resilience by design. How to handle tool failures and rate limits within the agent graph.
Read Article →
Agentic AI
Module 7 Lesson 4: Validation Nodes
The zero-trust agent. How to implement verification steps that ensure output quality before finishing.
Read Article →
Agentic AI
Module 7 Lesson 5: Human Approval Nodes
The pause button. Implementing 'interrupts' that allow humans to review and edit agent state before final actions.
Read Article →
Agentic AI
Module 7 Wrap-up: The Master of Graphs
Hands-on: Build a complex research agent with routing, loop guards, and a validation gate.
Read Article →
Agentic AI
Module 8 Lesson 1: What CrewAI Is
From single actors to ensemble casts. Understanding the specialized role-playing framework for multi-agent systems.
Read Article →
Agentic AI
Module 8 Lesson 2: Roles and Responsibilities
Designing the persona. How to write effective Roles, Goals, and Backstories to maximize agent performance.
Read Article →
Agentic AI
Module 8 Lesson 3: Task Decomposition
Breaking it down. How to turn a complex project into a set of discrete, delegated Tasks in CrewAI.
Read Article →
Agentic AI
Module 8 Lesson 4: Agent Collaboration Models
Sequential vs. Hierarchical. Choosing the right organizational structure for your agent team.
Read Article →
Agentic AI
Module 8 Lesson 5: Communication Patterns
How agents share data. Understanding context passing, memory sharing, and the 'Kitchen' environment in CrewAI.
Read Article →
Agentic AI
Module 8 Wrap-up: Your First AI Crew
Hands-on: Build a two-agent research crew that identifies tech trends and writes a report.
Read Article →
Agentic AI
Module 9 Lesson 1: What StrandAgents Are
The lightweight alternative. Understanding the event-driven, streaming-first architecture of StrandAgents.
Read Article →
Agentic AI
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.
Read Article →
Agentic AI
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.
Read Article →
Agentic AI
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.
Read Article →
Agentic AI
Module 9 Lesson 5: When StrandAgents Make Sense
Choosing the right tool. Understanding when to use event-driven strands over complex graphs or crews.
Read Article →
Agentic AI
Module 9 Wrap-up: Engineering for Real-Time
Hands-on: Build an event-driven agent pipeline that detects toxicity and triggers a response.
Read Article →