If you’re building AI agents, there’s a fundamental architectural decision that will determine whether your system scales gracefully or collapses under its own complexity.
That decision? Understanding the difference between who your agent is and what your agent can do.
Most developers conflate these two concepts. They cram everything into one massive prompt and wonder why their agents become slow, confused, and impossible to maintain. There’s a better way.
The Hiring Analogy That Changes Everything
Think about hiring a new employee.
On day one, you don’t hand them a 500-page manual covering every possible scenario they might encounter. Instead, you give them two things:
First, the essentials. The job description. The company culture. The operating principles. The “this is who we are and how we work here” document.
Second, you point them to resources. The specialized training materials. The certifications. The tools in the toolkit. They access these when needed, not all at once.
This is exactly how AI agents should be built.
What is AGENTS.md?
AGENTS.md is your agent’s identity document. It contains the declarative context that remains constant across all interactions:
Core identity. Who is this agent? What role does it play? What’s its expertise domain?
Operating principles. What rules does it follow? What values guide its decisions? What boundaries exist?
Company context. What organization does it represent? What’s the communication style? What terminology is standard?
Behavioral guidelines. How should it handle edge cases? When should it escalate? What tone should it maintain?
This information loads with every interaction. It’s the foundation your agent builds upon. It rarely changes, and when it does, you update it in one place.
What are Skills?
Skills are modular capabilities that your agent discovers and loads on demand. They’re the specialized toolkits:
Task-specific instructions. Detailed procedures for creating documents, processing data, or handling specific workflows.
Domain expertise. Deep knowledge about particular industries, technologies, or processes.
Tool integrations. Instructions for using APIs, databases, or external services.
Format specifications. Templates and structures for specific output types.
The key difference? Skills are loaded contextually. An agent helping with spreadsheet creation loads the spreadsheet skill. One handling customer support loads the support protocols. They don’t carry every skill in memory constantly.
Why This Separation Matters
1. Reduced Cognitive Load
Large language models have context windows. Every token counts. When you stuff everything into one prompt, you’re wasting precious context space on information that isn’t relevant to the current task.
A modular approach means your agent carries only what it needs, when it needs it.
2. Easier Maintenance
When your document creation process changes, you update one skill file. When your company policies shift, you update AGENTS.md. You’re not digging through a monolithic prompt trying to find the right section to modify.
3. Faster Iteration
Testing becomes simpler. You can validate individual skills in isolation. You can A/B test different versions. You can roll back changes without affecting the entire system.
4. Better Consistency
With a clear separation, your agent maintains consistent identity across all tasks while adapting its capabilities to the specific job at hand. It’s the same employee using different tools, not a different employee for each task.
5. Scalable Architecture
Adding new capabilities means adding new skill files, not rewriting your entire prompt. Your agent ecosystem grows without growing unwieldy.
The Practical Implementation
Here’s how this looks in practice:
AGENTS.md contains:
- Agent name and role
- Organization context
- Communication style guidelines
- Core rules and boundaries
- Escalation protocols
- Standard terminologyIndividual skill files contain:
- Specific task instructions
- Required tools and APIs
- Output format specifications
- Quality criteria
- Examples and templatesThe loading logic:
- Agent always reads AGENTS.md first
- Based on the user’s request, agent identifies relevant skills
- Agent loads only the skills needed for the current task
- Agent executes with focused, relevant context
Common Mistakes to Avoid
Mistake 1: Putting task instructions in AGENTS.md. Your identity document shouldn’t contain step-by-step procedures. Those belong in skills.
Mistake 2: Duplicating context across skills. If something applies to all tasks, it goes in AGENTS.md. Skills should contain only task-specific information.
Mistake 3: Loading all skills by default. The whole point is on-demand loading. If you’re loading everything anyway, you’ve just created a monolithic prompt with extra steps.
Mistake 4: Neglecting skill discovery. Your agent needs a way to know what skills exist and when to use them. This might be a skill index, a classification layer, or explicit user triggers.
Real-World Application
Consider a customer service agent that handles inquiries, processes refunds, and schedules appointments.
AGENTS.md defines: The company voice, escalation thresholds, customer data handling rules, and general service principles.
Skill: Inquiry Handling contains: FAQ knowledge, product information, common troubleshooting steps.
Skill: Refund Processing contains: Refund policies, approval workflows, system integration steps.
Skill: Appointment Scheduling contains: Calendar integration instructions, availability rules, confirmation templates.
When a customer asks about a refund, the agent loads its identity plus the refund skill. It doesn’t carry appointment scheduling instructions in that context. Clean. Focused. Efficient.
The Bottom Line
As AI agents become central to business operations, architecture decisions made early will compound over time. The monolithic approach might feel faster initially. But six months in, when you’re maintaining a 10,000-word prompt that nobody fully understands, you’ll wish you’d built modular from the start.
The distinction is simple:
AGENTS.md = Who the agent IS
Skills = What the agent CAN DO
Get this right, and you’ll build AI agents that are focused, maintainable, and ready to scale.
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