AI Agents vs Traditional Automation
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.
A. Rahman9 min read
Loading the homepage
Every Tech Agents article tagged Evaluation — 6 pieces across AI Agents, AI Development and AI Tools, newest first.
6 articles12 related tags
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.
RAG solves a knowledge problem; agents solve an action problem. Where each architecture belongs, how they combine, and why their failure modes barely overlap.
A criteria-first assessment of AI coding tools by category — completion, repo chat, agentic editors and review bots — with a harness for testing them yourself.
A comparison of what actually differs between the two API surfaces — system prompts, tool schemas, structured output, state and streaming — and how to choose.
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.
The layers between a working AI demo and a system people depend on: call boundaries, budgets, tenant isolation, evaluation, observability, degradation.
Tags that appear on the same articles as #Evaluation.