Architecture & How It Works
Inside the machinery: planning loops, memory, tool use, and the patterns that turn a language model into an actor.
The Anatomy of an AI Agent: Core Components
Explore the anatomy of an AI agent and its core components, from the language model brain to memory, tools, and the planning loop that drives action.
How AI Agents Plan and Execute Tasks
Learn how AI agents plan and execute tasks, from goal interpretation and step sequencing to acting on tools and adjusting plans when reality pushes back.
Understanding the ReAct Pattern in AI Agents
Understand the ReAct pattern in AI agents: how interleaving reasoning and acting improves reliability, and when this thought-action loop works best.
What Is Chain-of-Thought Reasoning in Agents?
What is chain-of-thought reasoning in agents? Learn how step-by-step thinking improves accuracy on complex tasks, and where the technique falls short.
How Agent Memory Systems Work
How do agent memory systems work? Explore how AI agents store, retrieve, and manage information so they stay coherent across steps, sessions, and long tasks.
Short-Term vs Long-Term Memory in AI Agents
Short-term vs long-term memory in AI agents: learn how each type works, how they differ, and why combining both keeps agents coherent across tasks and sessions.
Vector Databases and Agent Memory Explained
Vector databases and agent memory explained: see how embeddings power semantic search so AI agents recall the right information by meaning, not just keywords.
How AI Agents Use External Tools and APIs
How AI agents use external tools and APIs: learn how agents decide when to act, format calls, and fold real-world results back into their reasoning safely.
Function Calling in AI Agents: A Technical Overview
A technical overview of function calling in AI agents: how models emit structured calls, how schemas guide them, and how results flow back into reasoning.
What Is the Model Context Protocol (MCP)?
What is the Model Context Protocol (MCP)? Learn how this open standard connects AI agents to tools and data sources through one common interface.
How Multi-Agent Systems Coordinate
How do multi-agent systems coordinate? Explore the communication, role division, and shared context that let AI agents collaborate on complex tasks.
The Orchestrator–Worker Pattern in Agentic AI
The orchestrator-worker pattern in agentic AI: how a lead agent plans and delegates while specialized workers execute, keeping complex tasks organized.
Planning Algorithms Used by AI Agents
Planning algorithms used by AI agents: from search and decomposition to tree-of-thought exploration, learn how agents chart a path from goal to completed task.
Task Decomposition: How Agents Break Down Problems
Task decomposition explained: how AI agents break complex problems into manageable subtasks, order them by dependency, and execute each piece toward the goal.
Reflection and Self-Critique in AI Agents
Reflection and self-critique in AI agents: learn how agents review their own work, catch mistakes, and revise outputs to improve quality on complex tasks.
How Agents Handle Errors and Recover
How do AI agents handle errors and recover? Learn how agents detect failures, retry intelligently, adapt their approach, and escalate when needed.
The Role of Prompts in Agent Behavior
The role of prompts in agent behavior: learn how instructions shape an agent's goals, tools, and judgment, and why prompt design is central to reliable agents.
System Prompts vs User Prompts in Agentic AI
System prompts vs user prompts in agentic AI: understand how each shapes agent behavior, why their roles differ, and how they work together in a single request.
How Agents Maintain Context Over Long Tasks
How do agents maintain context over long tasks? Learn the summarization, retrieval, and state techniques that keep AI agents coherent over time.
Understanding Agent State Management
Understanding agent state management: learn what state an AI agent tracks, how it persists progress and context, and why it underpins reliability.
What Is an Agent Loop? Step by Step
Learn what an agent loop is and how it works step by step, from perceiving input to acting and reflecting in agentic AI systems.
How Agents Decide Which Tool to Use
Discover how AI agents decide which tool to use, from tool descriptions and reasoning to selection patterns and the failure modes to avoid.
The Role of Feedback Loops in Agentic AI
Explore the role of feedback loops in agentic AI, how they let agents self-correct, learn within a task, and stay aligned with their goals.
How AI Agents Use Knowledge Graphs
Learn how AI agents use knowledge graphs to ground reasoning, answer multi-hop questions, and connect facts that plain retrieval cannot.
Hierarchical Agent Architectures Explained
Understand hierarchical agent architectures, how manager and worker agents divide tasks, and when this layered design beats a single agent.
The Blackboard Architecture for Multi-Agent Systems
Learn how the blackboard architecture lets multiple AI agents collaborate through shared memory, contributing solutions to a common workspace.
How Agents Communicate With Each Other
Explore how AI agents communicate with each other, from shared messages and structured formats to protocols that keep multi-agent systems coherent.
Message Passing in Multi-Agent Systems
A clear guide to message passing in multi-agent systems: how AI agents exchange messages, the patterns involved, and how to keep it reliable.
What Is Agent Routing?
Learn what agent routing is, how systems direct requests to the right agent or tool, and why routing keeps agentic AI accurate and efficient.
How Guardrails Work in Agentic AI Systems
Understand how guardrails work in agentic AI systems, the layers that keep autonomous agents safe, and where to place checks on inputs and actions.
Understanding Agent Observability and Tracing
Learn how agent observability and tracing work, what to capture in agent traces, and why visibility is essential for debugging agentic AI.
How Agents Handle Parallel Tasks
Learn how AI agents handle parallel tasks, when to run work concurrently, and how systems coordinate and merge results from simultaneous operations.
The Role of Fine-Tuning in Agent Performance
Explore the role of fine-tuning in agent performance, when it improves agentic AI, and how it compares with prompting and retrieval alternatives.
How Retrieval Improves Agent Accuracy
Discover how retrieval improves agent accuracy by grounding responses in real data, reducing hallucination, and supplying current, relevant context.
Caching Strategies for AI Agents
Explore caching strategies for AI agents that cut cost and latency, from prompt and result caching to tool-output reuse and invalidation.
How Agents Manage Token and Cost Budgets
Learn how AI agents manage token and cost budgets, control runaway spending, and balance thoroughness against the price of every model call.
The Sense–Plan–Act Cycle Explained
Understand the sense-plan-act cycle, a foundational model for autonomous agents, and how it maps onto modern agentic AI reasoning and action.
How Agents Use Scratchpads and Working Memory
Learn how AI agents use scratchpads and working memory to think through problems, track progress, and hold intermediate results across steps.
State Machines vs LLM-Driven Agent Control
Compare state machines vs LLM-driven agent control, weighing predictability against flexibility to choose the right control flow for your agent.
How Agentic AI Handles Ambiguous Instructions
Learn how agentic AI handles ambiguous instructions, from asking clarifying questions to making reasonable assumptions and recovering from misreads.
Designing Reward Signals for AI Agents
Designing reward signals for AI agents shapes what they optimize for. Learn how reward design works, common pitfalls, and how to align behavior with goals.
How Agents Verify Their Own Work
How agents verify their own work matters for reliability. Learn the self-verification techniques agents use to catch errors, from self-critique to external checks.
The Role of Simulation in Training Agents
The role of simulation in training agents explained: why simulated environments let agents learn safely, cheaply, and at scale before facing the real world.
How Agents Use Reinforcement Learning
How agents use reinforcement learning explained: the trial-and-error loop, rewards, and policies that let AI agents learn to act toward goals over time.
Prompt Chaining vs Agentic Workflows
Prompt chaining vs agentic workflows compared: learn how fixed prompt sequences differ from autonomous agent loops, and when to choose each approach.