Managing Agent Behavior and Context

Modern AI agents require more than just a base LLM to function effectively; they need structured instructions and modular knowledge to operate within specific environments.

  • agents.md: This markdown file acts as a project-specific "readme" for agents. Located at the root of a project, it provides the agent with essential context like coding conventions, test commands, and style rules. These files can be nested, with files closer to the working directory overriding those at the root. Similar implementations exist, such as Claude.md.
  • Agent Skills: To prevent context window bloat, agents use a modular "skill" system. A skill is a folder containing a skill.md file (which includes metadata and a description) and necessary scripts. The agent only pulls in these resources when the user's request matches the skill's description, keeping the primary context window clean.

Interfacing with Tools and Other Agents

Standardized protocols are critical for agents to interact with external systems and other AI entities without requiring custom integrations for every connection.

  • Model Context Protocol (MCP): An open standard for connecting AI applications to external tools, databases, and SaaS platforms. By using an MCP server, developers can wrap a tool in a standard interface, allowing any MCP-compliant agent to interact with it without needing a bespoke connector.
  • Agent-to-Agent (A2A): This protocol enables coordination between different agents. Each agent publishes an "agent card"—a description of its capabilities and communication interface. This allows one agent (e.g., a procurement agent) to discover and delegate tasks to another (e.g., a finance agent) seamlessly.

Scaling Workflows with Sub-Agents

When tasks exceed the capacity of a single context window or require parallel processing, developers utilize the sub-agent pattern.

  • Sub-Agents: A parent agent spawns child agents to handle specific, isolated pieces of work. Each sub-agent operates in its own fresh context window, performs its assigned task, and returns a result to the parent. This pattern is essential for managing large codebases or executing independent checks in parallel to improve performance.