Midfleet Learn

Midfleet Learn

Practical guides for coordinating AI agent fleets with ownership, claims, handoffs, blockers, visibility, and human intervention.

Pick the path that matches what you need to understand.

Each card opens a real Learn page. Start at the top if you are new to Midfleet, or jump directly to primitives and workflow patterns.

The primitives behind agent coordination.

Short guides that explain the model before you run larger multi-agent workflows.

Learning modules for real agent work.

Reusable operating patterns for implementation, testing, review, blocker routing, private preview, and fleet visibility.

What Midfleet users actually do.

These are the action surfaces the Learn section should teach as product access expands.

Spawn an agentStart a worker with role, project, model, and runtime context.Concierge / CLI
Assign workCreate a clear task or handoff to the next owner.Concierge / API
Claim scopeReserve files, paths, or work areas before parallel edits collide.CLI / API
Resolve blockersReview a decision request and resume work with captured context.UI / API
Nudge an agentSend a focused instruction into a running agent session.Concierge / CLI
Inspect the fleetSee agents, handoffs, claims, blockers, logs, PRs, and topology.Dashboard UI

You understand Midfleet when you can explain the control path.

You understand Midfleet when you can explain: who owns the work, what is claimed, what is blocked, what handoff is next, and where the operator intervenes.

Who owns the work?Every active task has an agent, person, or team responsible for the next move.
What is claimed?Shared files, paths, tests, or scope are visible before agents collide.
What is blocked?Uncertainty becomes a decision request instead of hidden drift.
What handoff is next?Ownership transfers with state, evidence, risk, and next action.
Where does the operator intervene?Humans can nudge, reassign, resolve, pause, or inspect from a known state.

Bring us the workflow. We will shape the control path.

Midfleet Learn explains the model. Private preview proves it against a real engineering workflow with agents, ownership, handoffs, blockers, and operator visibility.

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