5 AI Agent Terms You Need to Know

2026-06-23 · Source: IBM Technology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

Frontier AI agents, capable of complex planning and code generation, rely on specific underlying components. Five essential terms define their operation: "agents.md", "Agent Skill", "MCP - Model Context Protocol", "A2A - Agent to Agent", and "Subagents". "agents.md" is a project-root markdown file, like a Readme for agents, specifying setup commands and code style rules, read upon project initiation. "Agent Skill" folders contain "skill.md" and resources, enabling agents to dynamically load context for specific tasks, such as creating a PowerPoint deck. "MCP - Model Context Protocol" is an open standard, initiated by Anthropic, that uses an MCP server to standardize connections between agents and external tools or data sources like Notion or Stripe. "A2A - Agent to Agent", a Google-developed open protocol, facilitates communication and task delegation between agents via "agent cards". Finally, "Subagents" represent a common architectural pattern where a main agent spawns child agents to manage large or parallel tasks, each with its own context, returning results to the parent.

Key takeaway

For AI Engineers designing or implementing agentic systems, understanding these five core components is crucial for building robust, scalable, and interoperable solutions. You should define project-specific behaviors using "agents.md", modularize task capabilities with "Agent Skills", and integrate external tools via "MCP". Utilize "A2A" for seamless agent-to-agent workflows and consider "Subagents" to manage complex, parallelizable tasks efficiently, optimizing context window usage.

Key insights

Frontier AI agents leverage structured files, protocols, and sub-agents for project context, external interaction, and task delegation.

Principles

• Project-specific rules are externalized in "agents.md".
• Task-specific knowledge is modularized as "Agent Skills".
• Standardized protocols enable external and inter-agent communication.

In practice

• Define project conventions in "agents.md" for agent adherence.
• Create "Agent Skills" for reusable, context-specific agent capabilities.
• Utilize MCP for tool integration and A2A for agent collaboration.

Topics

• AI Agents
• Agentic AI Foundation
• Model Context Protocol
• Agent to Agent (A2A)
• Subagents
• agents.md
• Agent Skill

Best for: AI Engineer, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by IBM Technology.