deep-agents-core

To configure an AI agent harness for multi-step planning and persistent memory, use the Deep Agents core framework, which supplies standard middleware for orchestration, TodoListMiddleware for task decomposition, and MemoryMiddleware for cross-session context.

Delegating tasks to specialized subagents in a multi-step AI workflow is handled by SubAgentMiddleware, a core component of the Deep Agents harness that manages delegation to specialized agents.

Adding human-in-the-loop approvals and safeguards to an autonomous agent workflow requires HumanInTheLoopMiddleware, which integrates into the Deep Agents harness to provide necessary oversight.

Yes, you can load on-demand skills from a filesystem directory during agent execution using SkillsMiddleware, which manages skill directories configured in the Deep Agents harness.

Yes, the framework requires specific SKILL.md frontmatter and configuration options to define the model, tools, subagents, and persistence settings for the Deep Agents harness.

You should use filesystem-backed context management for AI applications when your multi-step workflows require persistent storage and organized context across multiple operations.