Field
AI Agents
Models that take actions, and the gap between the demo and production.
An AI agent is a model given the ability to do things rather than just say them — call a search, read a database, send a message. It's the most exciting area in AI and the one with the widest gap between what demos promise and what production delivers.
That gap is the theme of this field, and the entries don't soften it. Multi-agent systems fail in ways that are well documented and rarely mentioned. Agents don't reliably catch each other's errors, because they share training data and therefore share blind spots. A system that's 90% reliable per step is a coin flip after seven steps.
The field covers what makes agents work (tool use, agent memory) and what keeps them safe (guardrails) — where the honest lesson is that the strongest guardrail isn't a clever prompt, it's not granting the capability.
Start with AI Agent, then Guardrails before you build anything that can act.
5 concepts in this field
AI Agent
Software that pursues a goal by taking its own steps — deciding, acting, and reacting — instead of answering once and stopping.
Tool Use
Letting a model call real software — a search, a calculator, your database — instead of trying to answer everything from memory.
Agent Memory
Giving an AI a way to remember across conversations, since the model itself forgets everything the moment a session ends.
Multi-Agent Systems
Several AI agents working together on one problem, each with a role — powerful in demos, awkward in production.
Guardrails
The checks around a model that decide what it's allowed to receive, say, and do — the part that stops a demo becoming an incident.