The standard agentic harness relies on a basic loop where an LLM receives a task, selects tools, and processes results until completion. However, this simple approach lacks the infrastructure needed for complex operations, such as mission planning, parallel execution, and performance verification.
To improve reliability, the proposed framework incorporates several key components. These include typed tools, directed acyclic graphs (DAGs), and parallel task execution. The system also introduces multi-level memory, error recovery mechanisms, and a structured role separation between Planner, Worker, and Critic.
The architecture further implements multi-dimensional budgets and comprehensive tracing to ensure the system remains measurable and debuggable. This approach aims to transform a basic agentic loop into a robust system capable of planning, executing, and validating its own performance.
