What Are AI Agents? A Complete Guide
An AI agent is a language model placed inside a loop with tools and a stopping rule. How that loop works, where it breaks, and when not to build one.
A. Rahman10 min read
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Every Tech Agents article tagged AI Agents — 8 pieces across AI Agents, Tutorials and AI Development, newest first.
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An AI agent is a language model placed inside a loop with tools and a stopping rule. How that loop works, where it breaks, and when not to build one.
Rule-based automation fails on inputs it was not written for. Agents fail unpredictably. A structured comparison of where each belongs, and how to combine them.
Build a tool-calling agent as a Next.js route handler: typed tool definitions, a bounded loop, a streamed step log, and a client leaf that renders progress.
RAG solves a knowledge problem; agents solve an action problem. Where each architecture belongs, how they combine, and why their failure modes barely overlap.
MCP replaces N times M model-to-system adapters with one server many clients can use. What the protocol standardises, and what it deliberately leaves to you.
When a change can be drafted by a process that runs your tests, the constraint moves from writing code to reviewing it. What that shift breaks and rewards.
Agentic systems are limited less by model capability than by compounding reliability, context economics, machine identity and interoperability. What each gates.
Prompt injection cannot be fixed by wording: instructions and data share one channel. The controls that work are architectural — scoping, provenance, approval.
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