difficulty-and-time-estimation

Estimate LLM task complexity by evaluating token budgets, turn counts, and risk metrics instead of wall-clock time. This approach uses a multi-factor difficulty model weighing atomic steps, batchable operations, and verification cost to calibrate effort accurately.

Delegate tasks to subagents versus handling them in the main thread by applying a structured delegation decision framework. It uses guardrails and multi-factor criteria to determine when to execute directly or outsource based on token estimates and risk considerations.

Scope AI-driven workflows by applying a multi-factor difficulty model that calibrates task complexity using token budgets and batchable operations. This enables better project scoping, risk mitigation, and planning across LLM-driven workflows where time estimates fail.

Calibrate task effort for delegation decisions using token budgets, turn counts, and risk metrics. The model weighs atomic steps, batchable operations, token estimates, and verification cost to determine whether main-thread handling or subagent delegation is appropriate.

This difficulty estimation approach works for batching and context management by emphasizing batchable operations and token budget evaluation. The model incorporates context management and verification costs into its scoring to guide subagent delegation and main-thread handling decisions.