Individual skill

ai-agents-architect

Architect autonomous agent systems — perception/action loops, tool design, planning strategies, memory, and multi-agent orchestration patterns. Use when designing an agent from scratch or scaling a prototype to production.

skills/ai-agents-architect/SKILL.md

Read pinned source

Source commit faf37c0859ebf627bfcb5225e8057d088691e0ae

What was checked

Matches the observed repository HEAD: Yes
Scanner rules are current: No
Repository scan complete: No
Skill scan complete: Yes
Observed behavior: Not checked yet
Last checked: Nov 8, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings): 54
Repository last updated: Oct 10, 2026
License: MIT

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

agent-skills Repository topic
ai-agents Repository topic
ai-agents-automation Repository topic
ai-skills Repository topic
awesome Repository topic
awesome-list Repository topic
chatgpt Repository topic
claude-skills Repository topic
meta-ai Repository topic
muse-ai Repository topic
design Derived category

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files

SKILL.mdcore · 4,201 bytes

Read excerpt


# AI Agents Architect

Agent architecture is the set of decisions that turn a capable language model into a system that 
acts reliably: how it perceives, plans, uses tools, remembers, and recovers. This skill covers 
those decisions as engineering trade-offs.

## Overview

Every agent is a loop: perceive state, reason about it, take an action, observe the result, repeat. 
Around that loop sit the supporting systems — tools with clean interfaces, a planner that 
decomposes goals, memory that persists context, and guardrails that bound behavior. The architect's 
job is to pick the right loop topology (single agent, supervisor, swarm), the right planning 
strategy (reactive vs. deliberative), and the right failure semantics for the task's risk level.