Deep Agents Core

An agent harness provides foundational middleware for task planning, context management, and subagent delegation. It abstracts away complex architecture, enabling developers to build advanced AI agents and focus on capabilities rather than infrastructure.

Yes, LangGraph supports human-in-the-loop workflows and long-term memory through configurable middleware. You can customize context management and persistence to maintain state across interactions, enabling complex agentic workflows.

The best way to configure context management is using a middleware-based agent harness. This allows automatic configuration of context management alongside task planning and subagent delegation, supporting both Python and TypeScript backends.

Yes, the agent harness supports both Python and TypeScript with configurable backends. You can implement long-term memory, human-in-the-loop workflows, and subagent delegation in either language environment.

Middleware handles task planning by automatically configuring core components for multi-step execution. Subagent delegation is managed through the harness, allowing the primary agent to distribute specialized work while maintaining context and persistence.

You need an agent harness when your AI application requires multi-step task planning, file context management, or delegation to specialized subagents. It provides the essential middleware and configuration options to handle complex agentic workflows.