What is an agent? What is a skill?

The difference between the two and how you use them are critical to your outputs and outcomes.

Your AI agent can access your CRM. It can send emails. It can analyze a pipeline report and spit out recommendations.

And it still screws up the 10-step process your team uses to qualify enterprise deals.

Not because it’s dumb. Because nobody taught it how your team actually works.

This is the gap that’s quietly killing agentic AI deployments across B2B — and it’s the same gap I kept hitting when I built out 60+ agents across our own GTM operation. The agents were powerful. They had tools. They had models. They had access. And they kept doing things wrong in ways that were expensive to debug and embarrassing to explain.

The fix wasn’t better models. It was a concept most teams haven’t encountered yet: Skills.

The Distinction That Changes Everything

Here’s the cleanest way I can frame it, borrowed from Christopher Penn: agents are like employees in departments — HR, Finance, Sales. They’re vertical. Domain-specific. They own a function.

Skills are like apps those employees use — Excel, Salesforce, your internal wiki. Horizontal. Reusable across departments. Any employee can pick them up.

Another way to think about it: tools let agents act. Skills provide the knowledge of how and when to act — including the company-specific, team-specific, and user-specific context that separates a capable AI from a competent one.

That last sentence is the whole game.

What Agents Actually Are

An agent is a full decision-making entity. It has a system prompt (its identity and instructions), tool access (what it can do), a backing model (Claude, GPT, etc.), and an agentic loop that lets it orchestrate workflows and manage state.

Think of it as an autonomous AI pre-prompted to handle a specific kind of task — debugging code, acting as the voice of the customer, running a competitive analysis, being a virtual CMO. Agents are vertical. They do one specific kind of work within a domain.

In systems like Claude Code (which I use daily), agents even have their own working memory — a little sandbox where they maintain context about what they’re doing and why.

In our system, agents are the parallel workers. Standalone files that execute work concurrently. They invoke skills and workflows as needed. But the agent itself? It’s the who. It owns the decision-making.

What Skills Actually Are

Skills are something different entirely. They’re modular, declarative bundles of expertise — organized procedural knowledge packaged into reusable units that agents load as needed.

Practically? A skill is a folder containing a SKILL.md file (the playbook), optional scripts, templates, and reference docs. It’s a standardized format that packages procedural knowledge and context for agents to load on demand.

The critical word there is “on demand.”

Because here’s where the architecture gets interesting.

Below I will share how to build them, when to use them, and give you a comprehensive step by step guide.