2 posts tagged with "task-planning"

Two agents receive the same user message. One finishes in 3 seconds and 400 tokens. The other enters a Reflexion loop, burns through 40,000 tokens, hits the context limit mid-task, and produces a half-finished answer. Neither the agent nor the calling system predicted which outcome was coming. This is not an edge case — it is the default behavior when agents start tasks without any model of how deep the work will go.

LLM-based agents have no native sense of task scope before execution. A request that reads as simple in natural language might require a dozen tool calls and multiple planning cycles; a complex-sounding request might resolve in a single lookup. Without pre-execution complexity estimation, agents commit resources blindly: the context window fills quadratically as turn history accumulates, planning overhead dominates execution time, and by the time the system detects a problem, the early decisions that caused it are irreversible.

Most agentic systems are built with a single architectural assumption that goes unstated: the LLM handles both planning and execution in the same inference call. Ask it to complete a ten-step task, and the model decides what to do, does it, checks the result, decides what to do next—all in one continuous ReAct loop. This feels elegant. It also collapses under real workloads in a way that's hard to diagnose because the failure mode looks like a model quality problem rather than a design problem.

The agent planning module—the component responsible purely for task decomposition, dependency modeling, and sequencing—is the seam most practitioners skip. It shows up only when things get hard enough that you can't ignore it.