# Deep Agent (Beta) Deep Agent prompt is an advanced agentic workflow capability that enables sophisticated AI-powered task execution with enhanced control, tool management, and human-in-the-loop capabilities. It extends the standard Agent functionality with features like tool interruption, subagent orchestration, and advanced workflow management. ### Key Features Deep Agent enables controlled, modular, and stateful AI workflows for production environments. It prioritizes safety, composability, and transparency, and the following features support reliable orchestration with built-in governance and oversight. #### 1. Tool Interruption Deep Agent supports the designation of specific tools as controlled interruption points within a workflow. When invoked, execution is deterministically paused pending human approval or supplemental input. This mechanism enforces governance over high-impact or irreversible operations. It is particularly suited for production systems requiring auditability and risk mitigation controls. #### 2. Subagent Orchestration The platform enables hierarchical agent composition by allowing specialized agents to be invoked as callable tools. This architecture promotes modularity, separation of concerns, and capability reuse across workflows. Complex objectives can be decomposed into domain-specific sub-tasks, improving scalability and maintainability. The result is a structured, extensible orchestration model for advanced automation scenarios. #### 3. Thread Management Deep Agent maintains persistent execution state across workflow lifecycles, ensuring contextual continuity. This supports deterministic resumption after interruptions and enables multi-turn, stateful interactions. By preserving intermediate artifacts and decision variables, the system minimizes context loss and enhances operational reliability. ### Key Components of Deep Agent Deep Agent follows the same architectural model defined in [Agentic prompts](/karini-ai-documentation/prompt-management/agentic-prompts.md). The core components include * Large Language Model (LLM) * Deep Agent Prompt * Tools * MCP Server --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://karini-ai.gitbook.io/karini-ai-documentation/prompt-management/deep-agent-beta.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.