AI Skills vs. Sub-Agents: the architecture decision that will define your next AI project

There's this moment every builder hits when designing an AI system. You've got your main agent working, it's doing the thing, but now you need it to do more. More domains. More capabilities. More specialization.

Two paths appear: spin up sub-agents, or give it skills.

I've been thinking about this a lot lately, especially after seeing how Anthropic rolled out Agent Skills as an open standard. It's not just a feature—it's a philosophical statement about how we should be building AI systems. And honestly? It changes how I think about architecture.

The Problem We're Actually Solving

Here's the thing nobody talks about enough: the "tool explosion problem" is real.

As tasks get more complex, agents need more capabilities. Traditional approach? Add more tools. But there's a ceiling—somewhere around 10-20 tools, the LLM starts fumbling tool selection. Context windows fill up with definitions. Reliability drops. Your agent that was doing great suddenly forgets how to use half its toolkit.

So the industry split into two camps.

Camp One: Multi-agent systems. Spin up specialized sub-agents. A coding agent. A research agent. An analysis agent. Let them coordinate.

Camp Two: Skill-based systems. One agent, modular capabilities. Load what you need, when you need it.

Both work. But they solve different problems.

Sub-Agents: The Team Approach

The multi-agent architecture is seductive. It mirrors how humans work—you don't ask your accountant to also be your lawyer. Specialization makes sense.

In practice, a sub-agent setup looks like this: a lead agent coordinates specialized workers. The research agent goes deep on data. The coding agent writes code. The reviewer agent checks work. They pass context, share memory, and collectively tackle complex problems.

Anthropic's own research showed that multi-agent systems outperformed single, more powerful agents by over 90% on complex research tasks. The reason? Specialization allows greater accuracy. Parallelism allows greater scale.

But here's the catch.

Coordination is expensive. Not just in tokens—in complexity. You need communication protocols. Failure handling. Conflict resolution. Every handoff between agents is a potential failure point. And debugging? Good luck tracing an issue through four agents having a conversation you can barely follow.

I've seen teams spend more time tuning agent-to-agent communication than actually building features. The overhead is real.

Skills: The Knowledge Injection Approach

Skills take a fundamentally different approach. Instead of creating new agents, you're extending an existing one with modular capabilities.

Think of it like onboarding a new hire. You don't clone them into specialists. You give them a procedures manual, domain knowledge, and the tools they need.

A skill is just a folder with a SKILL.md file containing instructions, scripts, and resources. The agent loads it when relevant and ignores it when not. Progressive disclosure—the agent only reads what it needs for the current task.

Here's the clever part: skills cost almost nothing when idle. The metadata sits in context (about 50 tokens per skill), but the full instructions only load when triggered. You can have dozens of skills installed without context penalty.

Compare that to sub-agents, where each agent maintains its own context window, memory, and state—even when doing nothing.

When Each Approach Makes Sense

After building with both patterns, here's my mental model:

Go with sub-agents when:

• Tasks genuinely require parallel execution (not just sequential steps)
• Domain expertise is so deep that it needs a different model or fine-tuning
• You need true autonomy—agents that can operate independently for extended periods
• Scale is the constraint, and you need to distribute work across infrastructure

Think warehouse robotics. Swarms of trading algorithms. Fleet coordination. Real parallelism, real independence.

Go with skills when:

• You're building a generalist agent that needs specialized knowledge on demand
• Tasks are sequential, even if they span multiple domains
• Context and consistency matter more than parallelism
• You want composability without coordination overhead

Think document generation. Code assistance. Research workflows. Customer support that spans products.

The Hybrid Reality

Here's what's actually happening in production: people are using both.

The smartest architectures I've seen use skills for domain knowledge and sub-agents for true parallelism. The main agent has skills installed for common workflows. When it hits something that genuinely benefits from parallel execution or deep specialization, it delegates to sub-agents.

It's not either/or. It's knowing which tool fits which problem.

Amazon's warehouse automation does this—centralized AI for logistics with swarms of local robots coordinating in real-time. Different layers, different patterns.

What This Means for Builders

If you're starting an AI project today, here's what I'd do:

Start with a single agent and skills. It's faster to build, easier to debug, and cheaper to run. You can always add complexity later. Most projects don't actually need multi-agent coordination—they just need well-organized knowledge.

Treat skills as documentation, not code. The best skills read like onboarding guides. Step-by-step procedures. Common pitfalls. Domain expertise captured in prose. If you're writing mostly code in your skills, you're probably building tools instead.

Evaluate where parallelism actually matters. Not "could benefit from" parallelism—actually requires it. Sequential execution with good skill loading is surprisingly fast and dramatically simpler.

Watch the coordination tax. Every agent-to-agent handoff has overhead. If your multi-agent system spends more time coordinating than executing, you've over-engineered it.

The Bigger Picture

What's interesting about Anthropic releasing Agent Skills as an open standard is the implicit bet: the future is composable, not fragmented.

Instead of building bespoke agents for every use case, the industry is converging on general-purpose agents with skill libraries. One agent. Unlimited specializations. Dynamic composition.

"We used to think agents in different domains will look very different. The agent underneath is actually more universal than we thought." That's Barry Zhang from Anthropic Research, and he's onto something.

Skills are the plumbing that makes this work. MCP handles the connections to external systems. Skills handle the procedural knowledge for using those connections effectively. Together, they're creating a shared standard—a common format that any AI agent can understand, regardless of who built it. Write a skill once, use it anywhere.

The Real Question

Ultimately, the choice between skills and sub-agents isn't about technology—it's about understanding your problem.

Are you building one smart thing that needs to know many domains? Skills.

Are you building many specialized things that need to work together? Sub-agents.

Are you building something complex that does both? You probably need both.

The architecture you choose will shape everything that follows: development speed, maintenance burden, cost, and reliability. Choose based on what your problem actually needs, not what's trending on AI Twitter.

Start simple. Add complexity when you've earned it. And remember—the best systems aren't the most sophisticated. They're the ones that actually ship.