The CV-to-job-search demo is a good showcase. For multi-step workflows like this, one issue I've run into is that the agent instructions degrade across steps. The initial task description is clear, but by step 5 the model is interpolating intent from earlier context rather than following explicit instructions.
Structuring the task prompt into named blocks (objective, constraints, expected output format per step) before the workflow starts makes each step much more reliable. The agent has less to infer.
Built github.com/Nyrok/flompt to help with this, a visual builder that decomposes instructions into semantic blocks and compiles to Claude-optimized XML. Useful for defining the "task shape" before handing it to an agent.
Yeah, instruction drift is a real problem in long agent chains. In this case the workflow gets decomposed into steps up front and each step is executed by a separate sub-agent.
So the model isn’t carrying the whole instruction chain across multiple steps, it’s just solving the current task. Similar pattern to what tools like Codex CLI or Claude Code do.