Multi-step AI agents struggle with context bleeding between tasks

The developer's observation reveals a subtle but critical gap in multi-step agent architecture. When a single language model processes a sequence of tasks, each step's outputs naturally persist in the message history or context window. Without explicit isolation, the model treats all prior information as equally relevant to the current task—so a weather API result from step two can influence the summary of step three, even though they are logically independent.

This is not a flaw in model capability or instruction-following; the model is behaving rationally given its context. The problem is architectural: most multi-step frameworks preserve full conversation history for continuity and debugging, but this design choice creates a state-bleeding risk. The developer notes that existing agent-builder tools (such as EnterPro Agent Builder) offer useful features like versioning and rollback, but they do not appear to solve the underlying boundary-isolation problem. The open question—whether developers are systematically isolating context or relying on luck—suggests this remains a largely manual design burden rather than a solved infrastructure problem.