Skills, not giant agents: the next abstraction for AI workflows

[Devy]

Audience: Senior engineers / AI builders
Format: Opinion + architecture patterns
Context: Simple, composable, and maintainable systems

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TL;DR

• Monolithic agents are hitting a practical ceiling
• The new direction: small, specific, reusable skills
• Less “centralized intelligence”, more explicit composition

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The problem with giant agents

The dominant pattern in 2024–2025 was:

one agent that “does everything”

• decides
• plans
• executes
• iterates

In theory it sounds powerful.

In practice:

• hard to test
• hard to debug
• unpredictable behavior

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Signs of saturation

Real teams are seeing:

• fragile workflows
• unnecessary loops
• dependence on complex prompts
• inconsistent results

The problem isn’t the model.

It’s the way we’re encapsulating logic.

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The alternative: skills

Instead of a monolithic agent:

break it into small capabilities

A skill is:

• specific
• testable
• reusable

Example:

• generate_summary
• validate_schema
• run_tests

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Mental model shift

Before:

“the agent decides what to do”

Now:

“the system composes capabilities”

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Recommended architecture

1. Skills as functions

async function generateSummary(input: string) {
  return llm.call({ prompt: `Resume: ${input}` });
}

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2. Explicit orchestration

if (task === "summarize") {
  return generateSummary(input);
}

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3. Composition

const result = await runPipeline([
  extractData,
  validateData,
  generateReport
]);

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4. Observability

Each step:

• clear logs
• visible inputs/outputs

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Why this works better

Control

You know exactly what’s happening.

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Testability

Each skill can be validated in isolation.

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Reusability

Same block → multiple workflows.

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Maintainability

Local changes, not systemic ones.

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When to use agents (really)

Agents are still useful when:

• the problem is open-ended
• there’s no clear flow
• exploration is necessary

But even then:

limit their scope

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Common anti-patterns

• agent with full access
• logic implicit in prompts
• autonomous loops without limits
• lack of logs

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Hybrid pattern

A practical architecture:

deterministic skills + lightweight agent

The agent:

• selects
• doesn’t execute directly

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Implications for teams

This isn’t just technical.

It’s organizational.

• better onboarding
• less dependence on experts
• more speed without losing control

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Verdict

The problem isn’t that agents don’t work.

It’s that we’re trying to use them for everything.

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Final reflection

The next generation of AI systems won’t be:

• more autonomous

It will be:

• more modular
• more observable
• more composable

Because in real systems:

simplicity scales better than “intelligence”.