What are AI Agents?
“From Passive Tools to Active Assistants: The Cognitive Revolution in Software.”
AI Agents
A comprehensive collection exploring AI agents—autonomous systems that perceive, reason, and act. From foundational concepts to production deployment, covering voice agents, vision agents, multi-agent systems, and cutting-edge research.
Each topic includes:
- Core concepts and architecture patterns
- Implementation approaches (tool-agnostic)
- Production engineering considerations
- Code examples and best practices
- Connections to LLMs, RAG, and multimodal AI
Browse by Topic
Foundations:
- What are AI Agents
- LLM Capabilities for Agents
- Prompt Engineering for Agents
- Tool Calling Fundamentals
- Memory Architectures
Voice Agents:
Vision Agents:
Multi-Agent Systems:
Advanced Topics:
- Web Browsing Agents
- Code Execution Agents
- Autonomous Agent Architectures
- Self-Reflection and Critique
- Hierarchical Planning
- World Models for Agents
- Agent Evaluation Frameworks
- Testing AI Agents
- Prompt Injection Defense
- Data Leakage Prevention
- Ethical AI Agents and Safety
- Agent Benchmarking Deep Dive
- Future of AI Agents
Optimization & Efficiency:
Memory & Long Context:
Deployment & Reliability:
Knowledge & Reasoning:
Real-Time & Streaming:
Orchestration & Planning:
Domain Specialization:
Problem Index
Below you’ll find all AI Agents topics in chronological order:
Content created with the assistance of large language models and reviewed for technical accuracy.
LLM Capabilities for Agents
“The Engine of Autonomy: Understanding the Agentic ‘Brain’.”
Prompt Engineering for Agents
“Programming with English: The High-Level Language of 2024.”
Tool Calling Fundamentals
“Giving the Brain Hands to Act: The Interface Between Intelligence and Infrastructure.”
Memory Architectures
“The difference between a Chatbot and a Partner is Memory.”
Agent Frameworks Landscape
“To Framework or Not to Framework? Navigating the Agent Ecosystem.”
Building Your First Agent
“Hello World? No, Hello Agent.”
Agent Workflow Patterns
“Better workflows beat better models.” — Dr. Andrew Ng
Retrieval-Augmented Generation (RAG)
“Giving the Brain a Library: The Foundation of Knowledge-Intensive Agents.”
Document Processing for Agents
“Garbage In, Garbage Out. The Art of Reading Messy Data.”
Vector Search for Agents
“Finding a Needle in a High-Dimensional Haystack: The Mathematics of Recall.”
Context Window Management
“The Finite Canvas of Intelligence: Managing the Agent’s RAM.”
Multi-Step Reasoning
“Thinking Fast and Slow: How to make LLMs stop guessing and start solving.”
The ReAct Pattern Deep Dive
“Reason + Act: The Loop that Changed Everything.”
Planning and Decomposition
“If you fail to plan, you are planning to fail (and burn tokens).”
Real-Time Agent Pipelines
“Speed is not a feature. Speed is the product.”
Voice Agent Architecture
“Talking to machines: The end of the Keyboard.”
Voice Agent Frameworks: LiveKit & Pipecat
“Don’t build the phone network. Just build the app.”
Voice Activity Detection (VAD)
“The art of knowing when to shut up.”
Real-Time Speech-to-Speech Agents
“Removing the Text Bottleneck: The Omni Future.”
Vision Agent Fundamentals
“Giving eyes to the brain: How Agents see the world.”
Screenshot Understanding Agents
“Giving agents the eyes to read the screen as a human does.”
UI Automation Agents
“The ultimate API: The User Interface.”
Computer Use Agents
“Moving from ‘Chatting’ with an AI to ‘Co-working’ with an OS.”
Human-in-the-Loop Patterns
“The safest way to deploy AI: Keep the human in the driver’s seat.”
Tool Design Principles & Agentic Orchestration
“An agent is only as good as the tools it can wield.”
API Integration Patterns for AI Agents
“Connecting the brain to the world’s nervous system.”
Database Interaction Agents: Mastering Text-to-SQL & Beyond
“Democratizing data access through natural language.”
Multi-Agent Architectures: The Power of Coordination
“If you want to go fast, go alone. If you want to go far, go together.”
Agent Communication Protocols: The Language of Cooperation
“The final frontier: Standardizing the Agent-to-Agent dialogue.”
Role-Based Agent Design
“Generalists are okay, but Specialists win: Why Role-Based Design is the secret to production AI.”
State Management and Checkpoints
“Agents that don’t forget: Building reliability through state persistence.”
Error Handling and Recovery
“Agents that don’t quit: Building resilient AI that can fix itself.”
Observability and Tracing
“Inside the mind of the machine: Mastering agentic observability.”
Structured Output Patterns
“Make agents predictable: enforce schemas, validate outputs, and recover automatically when the model slips.”
Web Browsing Agents
“Turn the open web into a reliable tool: browse, extract, verify, and cite—without getting prompt-injected.”
Code Execution Agents
“Let agents run code safely: sandbox execution, cap damage, and verify outputs like a production system.”
Autonomous Agent Architectures
“Architecture beats prompting: build autonomous agents with clear state, strict tool boundaries, and measurable stop conditions.”
Self-Reflection and Critique
“Make agents less overconfident: separate drafting from critique, force evidence, and turn failures into actionable feedback.”
Hierarchical Planning
“Make agents reliable at large tasks: plan at multiple levels, execute in small verified steps, and stop when budgets say so.”
World Models for Agents
“Agents become reliable when they carry an internal model of reality: state, uncertainty, and predictions—not just chat history.”
Agent Evaluation Frameworks
“If you can’t measure an agent, you can’t improve it: build evals for success, safety, cost, and regressions.”
Testing AI Agents
“Test agents like systems: validate tool calls, pin behaviors with replayable traces, and catch regressions before users do.”
Prompt Injection Defense
“Treat prompts like an attack surface: isolate untrusted content, validate every tool call, and fail closed under uncertainty.”
Data Leakage Prevention
“Prevent leaks by design: minimize data access, redact outputs and logs, and enforce least privilege for tools and memory.”
Token Efficiency Optimization
“The most expensive token is the one you didn’t need to send.”
Cost Management for Agents
“Intelligence is cheap. Reliable, scalable intelligence is expensive.”
Streaming Real-Time Agents
“Waiting 10 seconds for a thoughtful answer is okay. Waiting 10 seconds for a blank screen is broken.”
Dependency Graphs for Agents
“An Agent without a Plan is just a stochastic parrot reacting to noise.”
Building Domain-Specific Agents
“Don’t build a generalist. Build a specialist.”
Knowledge Graphs for Agents
“RAG gives you documents. A knowledge graph gives you facts with structure—and agents need structure to act reliably.”
Long-Context Agent Strategies
“Long context isn’t ‘more tokens’—it’s a strategy for keeping the right boundaries of information.”
Agent Deployment Patterns
“The hardest part of agents isn’t reasoning—it’s deploying them safely when the world is messy.”
Scaling Multi-Agent Systems
“A single agent is a demo. Scaling agents is distributed systems with language models in the loop.”
Agent Orchestration
“Single agents are limited by their context window and specialized knowledge. Orchestration is the art of composing a symphony of agents to solve problems no...
Fine-Tuning for Agent Tasks
“Fine-tuning is the bridge between a general-purpose reasoner and a specialized autonomous agent—it’s about teaching the model not just what to know, but how...
Agent Reliability Engineering (ARE)
“Reliability is not a state you reach; it is a discipline you practice. In the era of autonomous agents, SRE (Site Reliability Engineering) is evolving into ...
Ethical AI Agents and Safety Guardrails
“An autonomous agent without safety guardrails is not an assistant; it is a liability. Ethics in AI is not a ‘layer’ you add at the end—it is the operating s...
Agent Benchmarking: A Deep Dive
“If you cannot measure an agent, you cannot improve it. Benchmarking is the process of defining what it means for a machine to ‘think’ through a task.”
The Future of AI Agents: 2025 and Beyond
“The agents of today are assistants; the agents of tomorrow will be colleagues. We are moving from a world where we tell AI what to do, to a world where AI t...