ai-agents
25 results
How to Build an AI Agent with Claude (Step-by-Step)
Build a real working AI agent using Claude's API and tool use — from zero to a functioning agent that can search, reason, and take actions.
The Hottest AI Topics Right Now (March 2026)
Reasoning models, agentic AI failures, DeepSeek's cost bomb, MCP, vibe coding, voice AI — a practitioner's take on what's actually shifting this month.
AI Agent Security: How to Red Team Your Agents
How to adversarially test AI agents before deploying them — prompt injection, privilege escalation, tool misuse, and systematic security testing frameworks.
Claude Computer Use: A Practical Prompting Guide
How to prompt Claude's computer use API effectively — from basic desktop automation to reliable multi-step workflows. Real examples and failure patterns.
Prompt Injection Defense in Production AI Systems
How to detect, prevent, and harden real AI applications against prompt injection attacks — with code patterns and system prompt templates.
Voice AI Prompting: System Prompts for VAPI, ElevenLabs, and Twilio Agents
How to write system prompts for voice AI agents — the specific patterns that work for phone-based and real-time voice interfaces using VAPI, ElevenLabs Conversational AI, and Twilio.
Agentic RAG — Moving Beyond Simple Q&A
Simple RAG retrieves once and answers. Agentic RAG lets the model decide what to retrieve, when, and how many times — here's how it works and when to use it.
AI Agent Evaluation: How to Know If Your Agent Actually Works
Move beyond vibes-based testing — build a proper eval framework for AI agents covering task completion, hallucination rate, latency, and cost with real tooling recommendations.
Build a Customer Support AI Agent That Doesn't Hallucinate
How to architect a grounded AI support agent using RAG, strict system prompt rules, and adversarial testing — so it never makes up answers about your product.
How to Build Calling AI Agents with n8n
Step-by-step guide to building AI agents that call APIs, send messages, and trigger phone calls using n8n — not just chatbots that respond to text.
MCP (Model Context Protocol) — A Deep Dive Beyond the Basics
Go past the 'MCP connects AI to tools' explainer: understand the 3 primitives, set up real MCP servers, build your own in Python, and learn which servers are worth using in 2026.
Multi-Agent Workflows: When to Use One Agent vs Many
A practical framework for deciding when to split into multiple agents — covering pipeline, parallel, and hierarchical patterns with real cost and complexity trade-offs.
n8n AI Agent System Prompt Templates (Customer Support, Sales, Research)
Five production-ready system prompt templates for n8n AI agents — customer support, sales qualification, research, IT helpdesk, and e-commerce. Copy, customize, deploy.
AI Agent Design Patterns: ReAct, Plan-and-Execute, and Reflexion Explained
The three most important agent architectures — ReAct, Plan-and-Execute, and Reflexion — each solve different problems. Learn when to use which and how they work in practice.
Build Your First AI Agent: A Beginner's Step-by-Step Guide
Build a working AI agent from scratch — one that can use tools, make decisions, and complete multi-step tasks. No prior agent experience needed.
What is Context Engineering? The Term Replacing 'Prompt Engineering' in 2025
Context engineering is the practice of designing everything that goes into an AI's context window — not just the prompt. Here's why it matters and how to get better at it.
What is an AI Agent?
Understand what separates an AI agent from a regular prompt. Learn how agents perceive, reason, act, and loop — and why this architecture unlocks a completely new class of AI applications.
Agent Components: Memory, Tools, Planning, and Perception
Break down the anatomy of an AI agent. Every agent — no matter how complex — is built from four components: memory, tools, a planning mechanism, and perception. Learn what each does and how they interact.
Function Calling: Giving LLMs Tools
Function calling is the technical mechanism that lets an LLM invoke external tools. Learn how to define tools, how models decide when to call them, and how to structure results so agents act reliably.
ReAct Prompting: Reason Before You Act
ReAct is the reasoning pattern that makes agents dramatically more reliable. By explicitly writing out thoughts before every action, the model plans better, catches errors earlier, and produces work you can follow and debug.
AI Workflows vs. AI Agents: Choosing the Right Architecture
Not every AI task needs an agent. Learn the difference between deterministic workflows and autonomous agents, when to use each, and how to avoid over-engineering with agents when a simpler pipeline would be more reliable.
Context Engineering for Agents
Context engineering is the discipline of deciding what information goes into an agent's context window, in what form, and when. It's the highest-leverage skill for building reliable agents at scale.