Running two or more AI workers at the same time is genuinely useful. Two pieces of work advancing simultaneously, without waiting on each other, can produce real time savings. The point where this goes wrong is predictable: two workers in the same workspace, on two unrelated jobs. One finishes first and, being helpful, gathers everything in the shared workspace, including the other worker's half-finished output, and folds all of it into its own result. Two clean work streams go in. One tangled knot comes out. Undoing the tangle takes longer than the parallelization saved.
Neither worker malfunctioned. The collision was built into the setup the moment two workers shared one workspace. Apply the root-cause question from Lesson 6: shared space plus simultaneous hands equals collision, eventually, always. The same dynamic occurs in any physical workspace. Two cooks, one cutting board, one knife: the problem is not the cooks. The structure is the problem.
The fix is structural, not behavioral. Each job run in parallel gets its own lane: its own copy of whatever it is working on, its own space, untouched by anyone else until it is finished. Finished work flows to one place, the operator, who merges results one at a time, checking each against its definition of done from Lesson 9. One merge point, under the operator's control. When one job needs information from another, the answer is: it waits. The first piece lands, gets checked, becomes part of the settled record, and the second job starts fresh from there. Work flows to the center, never sideways. Sideways is where collisions come from.
Which tasks deserve parallel lanes at all? Independent ones. Two tasks are independent when neither needs the other's output and they touch different things. Researching venues while drafting a separate communication: independent. Drafting a message while another worker rewrites the same message's tone: that is one job wearing two names, and running it in parallel produces a race, not speed. The test is simple: ask what is actually saved. Parallel lanes only pay when the jobs genuinely cannot collide.
One further habit: when a phase of work ends, end the session with it. A long conversation carries its whole messy history forward, including everything already settled or superseded, and Lesson 0 covered what happens when the context window gets crowded. A fresh start with a clear handover, what is decided and what comes next, beats a long dragging history every time. Separate stations, one counter where results come together, and a clean reset between phases. That is the structure of a workflow that holds.