Agent stack comparisons

Choose the right layer before adding more machinery.

Teams often use the same word for instructions, capabilities, tools, workers, and controls. These source-backed comparisons make the decision boundary explicit—and show when two layers work better together.

8

decision guides

Primary docs

evidence standard

30 days

maximum review interval

High-intent decision library

Start with the distinction blocking your architecture

Decision 01

AGENTS.md vs Agent Skills

Use AGENTS.md for concise repository truth that should shape most sessions. Use a Skill for a specialized procedure that should load only when the task calls for it.

Where should durable agent instructions and repeatable workflows live?

Open comparison

Decision 02

Agent Skills vs Plugins

A Skill is a focused unit of reusable knowledge or procedure. A plugin is a distribution boundary that can package Skills with agents, hooks, tools, and connectors.

Should a capability be one portable workflow or an installable extension bundle?

Open comparison

Decision 03

Subagents vs Automations

Subagents divide bounded work inside an active agent session. Automations run a stable workflow later or repeatedly, with durable scheduling, permissions, and recovery.

Should work be delegated inside a live session or run unattended on a trigger?

Open comparison

Decision 04

MCP vs A2A

MCP exposes tools, resources, and prompts to a client. A2A exposes an independently operated agent that accepts messages, manages task state, and returns artifacts.

Does an agent need a tool, or does it need to collaborate with another agent?

Open comparison

Decision 05

Agent Memory vs AGENTS.md

AGENTS.md carries deliberate team-owned repository instructions. Memory stores inspectable observations or preferences that may evolve across sessions.

Should information be remembered dynamically or committed as shared project guidance?

Open comparison

Decision 06

Hooks vs Agent Instructions

Instructions guide model judgment. Hooks run deterministic handlers at defined lifecycle events regardless of whether the model remembers to invoke them.

Should the model be told to do something, or should the system enforce it?

Open comparison

Decision 07

MCP vs Agent Skills

A Skill teaches the agent how to perform a workflow. MCP connects the agent to tools, resources, or prompts supplied by a server.

Does the agent need new knowledge and procedure, or access to an external capability?

Open comparison

Decision 08

Permissions vs Prompt Guardrails

Prompt guardrails communicate intent and decision policy. Permissions, sandboxes, approval gates, and scoped credentials enforce what actions are actually possible.

Which safety boundaries belong in instructions, and which require platform enforcement?

Open comparison