5 AI Agent Terms Explained: AGENTS.md, MCP, A2A and More

You ask an AI agent to fix a software bug.

It reads your project rules, finds the right files, runs the correct tests, checks documentation and returns a proposed solution. It may even ask another agent to review the security implications.

From the outside, this can look like one intelligent system doing everything.

Under the hood, however, several different components are working together.

To understand how modern AI agents operate, there are five important terms you need to know:

1. AGENTS.md
2. Agent Skills
3. Model Context Protocol, or MCP
4. Agent2Agent Protocol, or A2A
5. Subagents

You do not need to be a developer to understand these concepts. Knowing them will help you use agents more effectively, evaluate AI products and diagnose why an agent succeeds—or fails—at a task.

The five terms in 30 seconds

Term	What it does	Easy way to remember it
AGENTS.md	Gives an agent project-specific instructions	The project handbook
Agent Skills	Teaches an agent how to perform a particular task	A specialist training manual
MCP	Connects an agent to tools and data	A universal adapter
A2A	Helps independent agents communicate	A shared language
Subagents	Divide complex work into focused assignments	A specialist team

Together, these five concepts explain much of what turns a language model into an agent capable of completing real work.

What makes an AI system an agent?

A large language model provides the language generation and reasoning at the centre of an AI system.

On its own, however, the model is mainly a conversational partner.

An agent adds other layers around the model, including:

• Instructions that shape its behaviour
• Access to tools and data
• Specialist workflows
• Rules and permissions
• Ways to delegate work

Think of the model as a highly capable new employee.

Intelligence helps, but the employee still needs to know how the organisation works, which systems they may use, what procedures they should follow and when to ask a specialist for help.

That is where these five terms come in.

1. AGENTS.md: A README written for AI agents

The simplest way to understand AGENTS.md is this:

AGENTS.md is like a README file written specifically for an AI coding agent.

A traditional README explains a software project to human developers. AGENTS.md gives compatible coding agents the instructions they need to work inside that project.

An AGENTS.md file might explain:

• How to install the project
• Which command runs the tests
• Which coding conventions to follow
• Which files should not be changed
• How pull requests should be formatted
• What checks must happen before committing code

For example, the file might contain this instruction:

Run pnpm test before committing any changes.

When the agent reaches the commit stage, it knows that running the test suite is part of the expected process.

You do not need to repeat the instruction in every conversation. It lives inside the project.

Why AGENTS.md matters

Without project-specific instructions, an agent has to guess how the codebase works.

It might run the wrong command, ignore a team convention, modify generated files or miss an important validation step.

AGENTS.md turns unwritten team knowledge into guidance an agent can follow consistently.

Can a project have multiple AGENTS.md files?

Yes.

A project can have a general AGENTS.md file at its root and more specific files inside individual folders.

For example:

• The root file could define company-wide coding standards.
• A frontend folder could contain accessibility requirements.
• A backend folder could specify database testing rules.

Instructions closer to the code being changed can provide more specific guidance.

What about CLAUDE.md?

Not every coding agent uses the filename AGENTS.md.

Claude Code uses CLAUDE.md files for persistent project instructions. These files can describe architecture, coding standards, workflows and other information Claude should consider when working in the project.

The filename is different, but the basic idea is similar:

• AGENTS.md is an open instruction format for compatible coding agents.
• CLAUDE.md provides persistent instructions specifically for Claude Code.

Both answer the same question:

What should this agent know whenever it works in this project?

AGENTS.md in one sentence

AGENTS.md tells an AI coding agent how to work inside a particular project.

2. Agent Skills: Specialist knowledge invoked when needed

AGENTS.md contains information the agent may need whenever it works in a project.

But some knowledge is useful only for a particular task.

Imagine an agent that knows how to:

• Build a PowerPoint presentation
• Review a legal agreement
• Analyse a spreadsheet
• Prepare a research report
• Convert a document into a particular format

Loading all those instructions every time the agent starts would waste valuable context.

The agent does not need presentation templates and slide-design rules while fixing a software bug.

That is the problem Agent Skills are designed to solve.

What is an Agent Skill?

An Agent Skill is a folder containing instructions and resources that teach an agent how to perform a specialised task.

At the centre of the folder is usually a file called SKILL.md.

The folder can also contain:

• Scripts
• Templates
• Reference documents
• Examples
• Images and other assets

A presentation skill, for example, might include:

• Instructions for structuring a slide deck
• A branded PowerPoint template
• Typography and formatting rules
• Examples of effective slides
• Scripts for creating charts
• A checklist for reviewing the final presentation

How does an agent know when to invoke a skill?

A skill includes a description explaining what it does and when it should be used.

A description might effectively say:

Use this skill when the user asks to create, revise or review a PowerPoint presentation.

The agent initially sees the skill's name and description. When the request is relevant, it can load the complete instructions and any supporting resources it needs.

When the request is unrelated, the skill stays out of the way.

This approach is often called progressive disclosure. The agent loads detailed knowledge only when the task calls for it, rather than carrying every available instruction in its context window.

Some platforms can select a relevant skill automatically, while others also let users invoke a skill directly.

Why Agent Skills matter

Skills turn repeated prompting into reusable capabilities.

Without a skill, you may have to explain the same process every time:

• Which template to use
• How to structure the output
• Which standards to follow
• Which files to consult
• How to check the final result

With a well-designed skill, those instructions can be packaged once and reused.

AGENTS.md versus Agent Skills

This is the key distinction:

AGENTS.md explains how to work in a project. An Agent Skill explains how to perform a task.

For example:

• AGENTS.md might explain how to test your company's application.
• A presentation skill might explain how to create your company's slide decks.

One provides persistent project context. The other is a specialist capability invoked when relevant.

Agent Skills in one sentence

An Agent Skill is a reusable package of instructions and resources that an agent loads for a matching task.

3. MCP: How agents connect to tools and data

An agent may understand your request and know how to complete the task, but it still needs access to the outside world.

Suppose you ask an agent to:

• Find a document in Notion
• Check your calendar
• Search a GitHub repository
• Query a company database
• Look up a customer in your CRM
• Create a payment link

The information and functionality required for those tasks do not live inside the language model.

The agent needs a way to connect to external systems.

That brings us to MCP, or the Model Context Protocol.

What is MCP?

MCP is an open standard for connecting AI applications to tools, data sources and workflows.

Without a shared standard, developers may need to build a separate integration for every combination of agent and external tool.

MCP provides a more consistent interface.

A tool or data source can be exposed through an MCP server. The AI application communicates with that server, and the server handles the underlying connection to the service, API or database.

For example:

1. You ask an agent to find a project plan in Notion.
2. The agent uses an available MCP connection.
3. The MCP server communicates with Notion.
4. The relevant information is returned.
5. The agent uses it to complete your request.

MCP is often compared to a universal adapter for AI applications.

MCP does not mean unlimited access

An MCP connection should not automatically give an agent permission to do everything.

A well-designed setup still needs:

• Authentication
• Permission controls
• Approval steps for sensitive actions
• Activity logging
• Limits on the data and tools the agent can access

The important question is not only whether an agent supports MCP.

You should also ask:

Which systems can it access, what actions can it take and what controls are in place?

MCP in one sentence

MCP gives AI agents a standard way to connect to external tools, data and workflows.

4. A2A: How agents communicate with other agents

MCP helps an agent communicate with tools and data.

But what happens when an agent needs to work with another agent?

That is the purpose of A2A, the Agent2Agent Protocol.

A2A is an open standard designed to help independent AI agents discover one another, communicate and delegate work.

An example of A2A

Imagine a company has several specialist agents:

• A procurement agent negotiates with suppliers.
• A finance agent approves spending.
• A legal agent reviews contracts.
• A research agent investigates vendors.

The procurement agent may negotiate a contract and then need approval from finance.

Without a common protocol, the two agents might require a custom integration—or a human may need to move the information manually.

With A2A, the procurement agent can discover the finance agent's capabilities and hand over the relevant work.

What is an Agent Card?

A central concept in A2A is the Agent Card.

An Agent Card is a structured description of an agent. It can include:

• The agent's identity
• Its capabilities
• The skills or services it offers
• How to communicate with it
• Its authentication requirements

Think of it as a digital business card.

Another agent can inspect the card, decide whether the agent is suitable for a task and determine how to contact it.

MCP versus A2A

The distinction is simple:

• MCP connects agents to tools and data.
• A2A connects independent agents to one another.

They are complementary.

An agent might use MCP to retrieve information from a company database and A2A to ask a finance agent to approve a proposed expense.

A2A in one sentence

A2A provides a shared way for independent agents to discover one another, communicate and delegate work.

5. Subagents: A specialist team inside the system

Sometimes one agent is not enough.

The task may be too large for one context window, require several kinds of expertise or contain many independent pieces that could be completed in parallel.

A subagent is a specialised child agent given a focused part of a larger assignment.

How subagents work

Imagine a main agent is asked to review a software project containing thousands of files.

Instead of analysing everything in one overloaded context, it might delegate separate tasks:

• One subagent reviews authentication.
• Another checks database queries.
• Another examines automated tests.
• Another looks for security issues.
• Another summarises the documentation.

Each subagent completes its assignment and returns the relevant findings to the main agent.

The main agent then combines those findings into a final result.

This is similar to a manager dividing a project among specialist team members.

Why separate context matters

A model's context window is the amount of information it can actively consider during a task.

Filling it with thousands of files, logs or search results can push important details aside.

A subagent can process that material in a separate context and return only the useful conclusion.

For example:

Review these 500 files and return a summary of the authentication architecture and its main risks.

The coordinating agent receives the summary rather than all 500 files.

Subagents may also work in parallel when tasks are independent, although the system still needs to manage costs, permissions and inconsistent results.

Are subagents an open standard?

Not in the same way as MCP or A2A.

"Subagent" describes a common architectural pattern. Different platforms implement child agents, specialist agents and manager-agent workflows differently.

A subagent may also have its own instructions, tools and permissions rather than receiving everything available to its parent.

Subagents in one sentence

Subagents allow a coordinating agent to divide complex work among focused child agents.

How all five terms work together

Imagine asking an AI agent to prepare a product-launch presentation.

Here is how the five components could fit together:

1. AGENTS.md or CLAUDE.md gives the agent your project rules, preferred terminology and company context.
2. A presentation skill teaches it how to structure and design the slide deck.
3. MCP allows it to retrieve product notes from Notion and performance data from your analytics system.
4. A2A lets it request budget information from a finance agent.
5. Subagents research competitors, customer feedback and market trends in parallel.

The final presentation may look like the output of one agent, but several layers of instructions, tools and delegated work helped produce it.

How these terms can help you use AI agents better

You do not need to build an agent to benefit from understanding its architecture.

When an agent produces a poor result, ask where the failure happened.

• Did it lack project instructions? It may need a clearer AGENTS.md, CLAUDE.md or equivalent instruction file.

• Did it lack specialist knowledge? The workflow may need an Agent Skill.

• Could it not access the required information? It may need an MCP connection or another approved integration.

• Was another specialist system required? An A2A-style handoff may be useful.

• Was the task too large or unfocused? It may need to be divided among subagents.

This is more useful than simply concluding that the model is not capable enough.

Sometimes the model is the problem. Often, the missing piece is the instructions, tools or architecture surrounding it.

Frequently asked questions

What is the difference between an AI agent and a chatbot?

A chatbot primarily responds to messages. An AI agent is designed to pursue a goal through multiple steps and may use tools, data, instructions and other agents to complete the task.

Is CLAUDE.md the same as AGENTS.md?

They serve a similar purpose, but they are not the same format. CLAUDE.md provides persistent instructions for Claude Code, while AGENTS.md is an open instruction format supported by compatible coding agents.

Are Agent Skills always active?

No. An agent can initially see only a skill's name and description, then load the complete instructions when the task is relevant. Some platforms also let users invoke a skill directly.

What is the difference between MCP and A2A?

MCP helps agents connect to external tools and data. A2A helps independent agents communicate and delegate work to one another.

Final thoughts

The vocabulary around AI agents can sound complicated, but the five central ideas are straightforward:

• AGENTS.md tells an agent how to work in a project.
• Agent Skills provide specialist knowledge when needed.
• MCP connects agents to tools and data.
• A2A enables communication between independent agents.
• Subagents divide complex work into focused assignments.

Understanding these terms helps you look beyond the language model itself.

You can begin asking better questions about the instructions, skills, integrations and delegation systems that determine what an AI agent can actually accomplish.