7 docs tagged with "ai"

Coding agents go beyond inline suggestions — they can read your codebase, run commands, browse documentation, and iterate through a task autonomously. This page explains how they work and how to use them effectively.

These are team-wide guidelines for using AI tools at Aliz. They apply regardless of which tool you're using.

AI tooling has shifted from smart autocomplete to full agentic workflows. This section covers how the Aliz web team uses these tools effectively — from writing better prompts to orchestrating multiple AI agents on complex tasks.

Model Context Protocol (MCP) is an open standard for connecting AI models to external data sources and tools. Instead of every AI tool reinventing integrations, MCP provides a single protocol that any client — Copilot, Cursor, Cline, Claude Code — can use to talk to any server: a database, the GitHub API, your file system, a browser.

A single AI agent has limits — context window size, task scope, and specialization. Multi-agent systems address these by having multiple agents collaborate, each with a defined role. This page covers the patterns and frameworks that make this practical.

How you phrase a prompt directly affects the quality of the output. This page covers practical techniques for code-related tasks, plus the workspace instruction files that let you front-load context once for the whole repo.

What vibe coding is, when it's a legitimate tool, and when it's not appropriate for professional work at Aliz.