Ai-Agents

Anthropic launched the Advisor Strategy on April 9, 2026 — a way to get near-Opus intelligence in your AI agents at a fraction of the cost. The idea is simple. Instead of running Opus on every request, you pair a cheaper model with Opus as an on-demand advisor. The cheap model does all the work. Opus only steps in when needed. How It Works You have two roles: Executor — Sonnet or Haiku. Runs every turn. Calls tools, reads results, makes decisions. Advisor — Opus. Runs on-demand only. Reviews the shared context and sends guidance when the executor is stuck. The executor and advisor share the same context: the system prompt, tool definitions, full conversation history, and all prior tool results. When the executor hits a hard decision, it calls the advisor tool. Opus reviews the full context and sends back a plan or correction. Then the executor continues. ...

Every AI agent we have discussed so far needs the cloud. You send a prompt to Claude or GPT, wait for a response, and pay per token. That works for coding and content generation — but what about a sensor on a factory floor? A camera in a farm? A device with no internet? That is where edge AI comes in. Running AI models directly on the device — no cloud, no latency, no API costs. ...

Most “AI apps” in 2023-2024 were wrappers. Take a text box, send it to ChatGPT API, display the response. That was it. In 2026, a new category is emerging: AI-native apps. These are applications designed from the ground up with AI as the core logic — not an add-on feature. The difference matters. And understanding it will change how you build software. What is an AI-Native App? An AI-native app is an application where AI is the primary logic engine, not a helper feature. ...

You want to build a multi-agent AI system. You have three main frameworks to choose from. Each has a completely different philosophy — and picking the wrong one will cost you weeks. In the multi-agent tutorial, we covered what multi-agent systems are. This article is the practical follow-up: which framework should you actually use? I will compare CrewAI, LangGraph, and AutoGen across 8 dimensions with real code, real numbers, and honest opinions. ...

Writing tests is the thing most developers know they should do but often skip. It takes time. It is boring. And when deadlines are tight, tests are the first thing to go. AI changed that. In 2026, AI tools can generate comprehensive tests for your code in seconds — often catching edge cases you would miss yourself. But how good are these tests really? Can you trust them? And which tools should you use? ...

One AI agent is powerful. But what if you had a team of AI agents — each specialized in a different job — working together on the same project? That is what multi-agent AI systems do. And they are changing how software gets built in 2026. What is a Multi-Agent System? Instead of one AI assistant that does everything, a multi-agent system splits the work between specialized agents: ┌──────────────┐ │ Architect │ Plans the solution, defines the structure └──────┬───────┘ ↓ ┌──────────────┐ ┌──────────────┐ │ Backend Dev │ │ Frontend Dev │ Build code in parallel └──────┬───────┘ └──────┬───────┘ ↓ ↓ ┌──────────────────────────────────┐ │ QA / Testing │ Runs tests, reports bugs └──────────────────────────────────┘ Each agent has: ...

Every AI coding tool has the same problem: it can read your code, but it can’t access your database. It can’t check your Jira tickets. It can’t read your Slack messages. It can’t query your production logs. Until now. MCP (Model Context Protocol) is a new standard that lets AI tools connect to anything — databases, APIs, file systems, project management tools, and more. It is like a USB-C port for AI. One standard connection that works with everything. ...

You open Claude Code on a new project. You ask it to fix a bug. It changes the wrong file. It uses the wrong architecture pattern. It runs the wrong build command. Not because Claude is bad. Because it doesn’t know your project. That is what context files solve. A CLAUDE.md or AGENTS.md file tells the AI everything it needs to know about your project before it starts working. It is the single most impactful thing you can do to improve AI coding agent output. ...

In the previous article, we learned what AI coding agents are. Now let’s build one. Not a toy demo. A real agent that reads your code, finds problems, fixes them, runs tests, and commits the result. You will understand how agents work from the inside. We will use Python and the Anthropic SDK (Claude API). The concepts apply to any AI provider. What We Are Building A simple agent that: Reads a Python file Sends it to Claude with instructions to fix linting errors Writes the fixed code back to the file Runs the linter to verify the fix If it still fails — tries again (up to 3 attempts) Commits the result with git This is the same loop that Claude Code and Copilot Agent use internally — just smaller and simpler. ...

A few years ago, AI coding tools just suggested the next line while you typed. Helpful, but you still wrote most of the code. That changed completely. In 2026, AI coding agents don’t just suggest — they plan, write, test, and ship code on their own. You describe what you want, and the agent builds it. It creates files, edits existing code, runs your tests, fixes failures, and opens a pull request. ...