Navigating the Storm: Driving AI Agents (stevenosborn.com)

A developer reports that orchestrating multiple AI coding agents feels like "driving in heavy rain": highly productive but mentally taxing. Running several agents in parallel accelerates work—refactors, frontend updates, tests—but creates a sustained, hypervigilant attention state the author calls "productive anxiety." The core problem is that humans stop coding and start monitoring: watching terminals, approvals, and emergent conflicts, then making small but critical interventions before drift becomes expensive. This changes the skillset required from implementer to conductor and exposes limits (the author’s personal cap is ~3 concurrent agents) and new fatigue patterns. Practically, the author recommends deliberate tactics—frequent checkpoints (every 15–20 minutes), per-agent commits/rollbacks, and keeping a mental model of each agent’s context—because current IDEs aren’t built for this mode. Key tooling gaps include real-time diff streams across agents, visual context management to surface stale or conflicting assumptions, task-graph–first interfaces (assign agents to tasks, not files), built-in checkpoint prompts, and “communication intercepts” to vet inter-agent handoffs. The broader implication: existing workflows and code-review practices (and Git) weren’t designed for machine-generated bulk diffs or multi-developer/multi-agent orchestration. The industry needs new, purpose-built orchestration UIs that make agent state the primary artifact and enable humans to conduct safely and scalably.