Single-project AI tooling is a solved problem. The hard part starts when you have several projects in flight and an agent working in each. Context-switching eats your day, you forget which agent did what, and "I'll review that diff later" turns into a graveyard of half-finished branches. Here's a workflow that keeps a whole portfolio moving.
1. Model your work as workspaces, not tabs
The first mistake is treating every project as an isolated window. Instead, give yourself one structure: an organization at the top, a workspace per product line or client, and projects inside each. Now "what's the state of everything?" is one screen, not twelve.
For an agency, a workspace per client also keeps boundaries clean — each client's projects, context, and secrets stay grouped and separate.
2. Make every run disposable
The thing that makes parallelism safe is isolation. Each task should run in its own git worktree on its own branch, so five agents across five projects can work at once and none of them touch your main working tree. If a run goes sideways, you discard the branch and nothing is lost.
Rule of thumb: if you'd be nervous letting an agent run unattended, you don't have enough isolation. Worktrees plus a review step fix that.
3. Put a review queue between "done" and "merged"
Agents finish work; that doesn't mean it's right. The highest-leverage habit is a single queue of runs that are waiting on you. Read the diff, run a verify gate (your build and tests), then merge or send a follow-up. A "needs review" view across all projects means nothing falls through the cracks.
4. Batch the boring, escalate the hard
Not every task deserves your best model or your attention. Dispatch routine changes — dependency bumps, copy tweaks, test scaffolding — to a cheaper agent and let them pile up in review. Save the expensive model and your eyes for the architectural work. Choosing the agent per task is what makes this practical.
5. Let a manager handle the multi-step builds
When a project is a whole build rather than a single change, decomposing it by hand is the bottleneck. This is where an autonomous workspace manager earns its keep: describe the outcome, and it plans the task graph, dispatches each step in dependency order, retries failures, and merges as it goes — surfacing only the decisions that need you.
A day in the workflow
- Morning: open the dashboard, clear the review queue from overnight runs.
- Midday: dispatch a batch of small tasks across projects; kick off one larger build with the manager.
- Afternoon: review and merge as runs complete; send follow-ups where needed.
The shift is subtle but huge: you stop being the person typing in one repo and become the person directing work across all of them.
Run your whole portfolio from one board
Command Fleet gives you workspaces, isolated runs, a cross-project review queue, and an autonomous manager. Free for 14 days.