call_agent

To delegate complex tasks to specialized agents, you invoke an orchestrator that routes your inputs to specific agent graphs. You define the agent_name, such as code_agent or researcher_agent, and the task, loading the appropriate graph to execute the workload safely.

The best way to orchestrate multiple agents for software engineering and deep research is using a single entry point that loads specialized agent graphs. This approach enables both code development and investigative reasoning by delegating tasks to appropriate subsystems.

Yes, you can use specialized agents to handle both code generation and deep research investigations. The orchestration system supports delegating development tasks to a code_agent and investigative reasoning tasks to a researcher_agent from one centralized entry point.

Yes, you need langchain-core, pydantic, and rich installed to run specialized agent orchestration workflows. These dependencies provide the foundational graph loading, structured data validation, and formatted result reporting required for controlled execution.

Controlled recursion limits execution by enforcing a defined boundary on agent graph processing, ensuring safe outcomes. This prevents infinite loops during complex task delegation, providing deterministic results and structured error handling within the orchestration workflow.

Your delegated agent task returns a formatted panel with error handling feedback to provide clear result reporting. The orchestration system presents agent outputs in a readable format using rich, ensuring consistent feedback and structured result handling after execution.