Multi-agent AI development. The questions that come up first, answered without hedging.

Multi-agent orchestration is the practice of coordinating multiple AI coding agents — each running tasks in parallel — through a shared dispatch and state layer. The core problem it solves: individual agents are powerful in isolation, but without coordination they collide on shared files, duplicate work, and produce no coherent audit trail. An orchestration layer assigns tasks, arbitrates contention, tracks state, and routes blocked agents to a human. DevPlane is the operator cockpit that provides this layer above the agents (Cursor, Claude Code, Codex, custom agents) you already use.

An operator cockpit is the layer between you and a fleet of AI coding agents — the surface from which you dispatch tasks, monitor fleet state, and route decisions back to yourself when an agent needs a human. The cockpit view a fleet operator needs is fundamentally different from what any single agent's dashboard provides: it spans all tools, tracks all assignments, and makes the coordination work visible and measurable. DevPlane is the operator cockpit for multi-AI-agent software development.

The problem is dispatch strategy, not agent capability. When agents are dispatched without a coordination layer, they write to overlapping files, produce conflicting diffs, and compound each other's mistakes. The fix is work-tree isolation by design — tasks partitioned so agents run in isolated branches or modules, with a claim mechanism that prevents two agents from taking the same territory. DevPlane's claim semantics give each agent a TTL-bounded territory; if two agents try to claim the same work, the second one is blocked and rerouted before it starts.

Parallel agents don't automatically produce parallel output. Without a dispatch layer, you become the bottleneck: you hand a task to agent A, wait for it, review it, hand a task to agent B, repeat. That's a slow single agent with extra terminals open — the theoretical parallelism never materializes. The throughput gain requires dispatching tasks concurrently, tracking their state without watching each terminal, and routing completions back to you for review in batch. DevPlane makes the parallel dispatch real: multiple agents run simultaneously, and you review completions as they land.

Agents encounter decision points they can't resolve autonomously — ambiguous specs, missing context, architectural choices that require human authority. Without a structured escalation path, they loop, hallucinate a resolution, or stop entirely. The fix is a two-phase handoff: the agent builds and escalates; you review and decide, with full context preserved in a typed completion block, not buried in a chat history. DevPlane's completion-block protocol captures the blocker with enough context for the operator to make a real decision, rather than reconstructing intent from a terminal session.

This is the visibility gap: fleet state scattered across chat histories, terminals, and notebooks, with no single view. The solution is a shared state model — every agent reports to a common board with its current task, status, blockers, and output, persisted across sessions. DevPlane provides the operator cockpit view: one board, every assignment, across all tools. You see what's running, what's blocked, what's done, and what produced a PR — without opening each terminal.

Headless operation requires four things your prompt-and-watch workflow doesn't provide: (1) a structured task brief the agent can act on without clarifying questions, (2) work-tree isolation so it can't collide with other agents, (3) a defined escalation path for blockers rather than a stalled session, and (4) a typed completion mechanism so you know when the task is actually done. DevPlane wires all four together: dispatch via `dp dispatch`, work-tree isolation via branch claims, completion via the structured completion-block protocol, and GitHub/Vercel webhook integration that moves cards on real events.

Cursor and Claude Code are the agents. DevPlane is the cockpit above them. A single assistant handles one task in one context window for one developer. DevPlane coordinates multiple agents running multiple tasks in parallel, prevents collisions through claim semantics, tracks all fleet state in one view, and routes blocked agents back to you with context intact. It's not a replacement — DevPlane works because Cursor and Claude Code run inside it. The question is whether you're running a fleet or running a slow single agent with extra tabs open.

Use multiple agents when your tasks can be isolated — different branches, different modules, no shared state — and when the coordination overhead is bounded. The parallelism gain only materializes if: (a) tasks don't depend on each other's output, (b) agents can run without colliding, and (c) you have a dispatch layer managing the fleet so you're not the bottleneck. If tasks are tightly coupled or you're manually routing each one, a single agent with a clear queue is faster. DevPlane's dispatch layer is what makes multi-agent work in practice: it handles contention, routes completions, and keeps your attention on decisions rather than terminal management.

An AI coding strategy without an operator layer is a single-player upgrade, not a team-level transformation. Individual agents make individual developers faster; a fleet with a coordination layer makes the whole engineering organization faster. The strategy question is: where is the throughput bottleneck? If it's writing code, one agent per developer is the right move. If the bottleneck has shifted to coordination — merge conflicts nobody triggered, blockers nobody noticed, state nobody can see — you need an operator layer, not more agents. DevPlane is the operator layer that makes the fleet's throughput advantage measurable: agent-hours of parallel work reaching Done vs. blocking, conflicting, or waiting on a human who became the bottleneck.