Why do LLM workflows feel smart in isolation but dumb in pipelines?

<!-- SC_OFF --><div class="md"><p>I’ve been noticing something while building.</p> <p>If I test a prompt alone, it works well.</p> <p>Even chaining 2–3 steps feels okay.</p>

<!-- SC_OFF --><div class="md"><p>Been building with LLM workflows recently.</p> <p>Single prompts → work well </p> <p>Even 2–3 steps → manageable </p> <p>But once the workflow

<!-- SC_OFF --><div class="md"><p>One recurring issue with domain-specific agents is that overly defensive refusal behavior can make them much less useful once the workflow gets

<!-- SC_OFF --><div class="md"><p>For people building multi-step AI workflows:</p> <p>Are you repeatedly sending the same context between steps?</p> <p>Example:</p> <p>summarize →

<!-- SC_OFF --><div class="md"><p>One thing that got weird for us pretty fast was “pause/resume”.</p> <p>At first it sounded simple enough.<br /> Workflow is doing multiple steps,

<!-- SC_OFF --><div class="md"><p>I apologize if this is low-effort, but I’m curious about where and how people are successfully combining frontier and local models to accomplish