ai-agents
38 results
AI Agent Memory Architectures: The Missing Piece in Most Agent Builds
Why most agents feel dumb after turn one — and how to fix it with mem0, pgvector, and the right memory architecture for your use case.
AI Agent Observability: How to Monitor Agents in Production
Monitor AI agents in production with LangSmith tracing, structured logging, and alert patterns that catch real failures before your users do.
Top 10 AI Agent Builders in 2026 (No-Code to Full-Code)
From drag-and-drop workflow builders to Python frameworks — the 10 best tools for building AI agents in 2026, ranked by use case and complexity.
ChatGPT vs Claude for Customer Support Agents: A Practical Comparison
Both models can power customer support agents, but they behave differently under pressure — here's what I found after testing both on real support scenarios.
Gemini 2.5 Pro vs Claude Sonnet 4 for AI Agents: Which Is Better?
I built the same agent with both models. Here's what I found about tool use reliability, reasoning quality, and cost when running multi-step agentic tasks.
What Is smolagents? HuggingFace's Lightweight Agent Library Explained
smolagents is HuggingFace's answer to complex agent frameworks — a minimal Python library that lets LLMs write and run code as their primary action mechanism.
Context Engineering in 2026: Why Prompting Alone Is No Longer Enough
In 2023, prompt engineering meant writing better ChatGPT messages. In 2026, the paradigm has shifted. What changed, what context engineering actually means, and why it matters now.
Claude 4.6 Context Compaction: How to Run Infinite Agentic Sessions
The #1 problem with long-running agents: they hit the context limit and die. Claude 4.6 context compaction fixes this with one config field. Here's how it works.
Building a Multi-Agent System with Claude: The Planner, Executor, Critic Pattern
Claude Opus 4.6 has a 'strong predilection for subagents' per Anthropic's docs. The planner/executor/critic triad is the most reliable multi-agent pattern for real tasks. Here's how to build it.
AI Agent Orchestration for Business: Moving from Single Prompts to Multi-Step AI Systems
The next step after prompting: building agent systems that handle multi-step business workflows without constant human intervention.
Prompt Chaining in Practice: How to Break Complex Tasks into Multi-Step AI Workflows
Single prompts fail on complex tasks. Prompt chaining — breaking work into sequential steps — is what makes AI reliable for real work.
Prompt Engineering for RAG Pipelines: How to Write Queries That Actually Retrieve the Right Context
Retrieval-Augmented Generation lives or dies on query quality. Most teams get the retrieval wrong, not the generation.
From Prompt to Pipeline: Building Your First AI Automation Workflow with n8n
Moving from chatting with AI to automating it with n8n — a practical walkthrough of building your first end-to-end AI workflow.
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.
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.