Plan squads as operating systems, not rows in a headcount sheet
A squad has a boundary, a purpose, a mix of roles, an ownership surface, a cost, and a place in the wider reporting structure. Two squads with the same headcount can have very different delivery characteristics. One may carry a staff engineer, product manager, and dedicated quality capability. Another may depend on a shared architect, a fractional designer, and a manager who already covers three teams.
Orgonaut models units, positions, placements, actors, and allocations separately. That makes it possible to show the intended team shape as well as the current reality. A vacant senior engineering position can exist before a person is hired. A staff engineer can be placed across more than one team with explicit allocation. A contractor or AI agent can sit beside permanent employees without being flattened into the same employment concept.
Use scenarios for PI planning and structural change
Quarterly and PI planning frequently expose structural problems. A programme needs a platform capability that no team owns. A product area has grown beyond one squad. A delivery group has too many dependencies. Leadership can respond by moving work, adding a position, splitting a team, changing an allocation, or creating a new enabling group. Each response changes more than a box on a chart.
A Scenario gives that proposal a safe boundary. Leaders can create several alternatives from the same Live state and keep each one internally consistent. One option may hire. Another may move existing capacity. A third may introduce agent support and invest senior review capacity elsewhere. Cost and FTE can be compared with the structure before one option becomes the plan.
Make agent capacity a structural question
AI agent adoption is often planned as a tooling rollout or a flat productivity assumption. Engineering leaders experience it as a structural change. Agent output needs context, permissions, evaluation, review, and ownership. A squad with several coding agents may become constrained by senior review. A platform team may need to own agent tooling and guardrails. Support, research, operations, and delivery teams may adopt different human-to-agent ratios.
Orgonaut can represent AI agents as actors in the organisation instead of hiding them inside a percentage. That supports questions about where the capacity lives, who supervises it, what it costs, and how the proposed mix changes the wider operating model. The free Agentic Reorg Simulator offers a small fictional version of this loop, with editable assumptions and shareable scenarios, before you model the real organisation.
Keep cost and review bottlenecks visible together
A cheaper structure can still be a worse operating system. Removing a management layer lowers cost, but it may increase span of control and concentrate review decisions. Merging squads removes a boundary, but a larger team may create more coordination work. Adding agents can increase nominal throughput while shifting the bottleneck toward senior engineers who review and integrate the output.
Orgonaut does not claim to predict delivery from an org chart. It gives leadership a shared model where cost, FTE, capacity signals, role coverage, placements, and structural changes can be discussed together. Assumptions remain open to challenge. That is more useful than letting the financial plan and the team design reach agreement separately.
Preserve the reasoning after the reorg
The weeks after a structural change often produce a new problem: nobody can reconstruct exactly what was approved, which assumptions were used, or how the new state differs from the last accepted version. The HR system eventually reflects reporting lines, while the decision record remains in meeting notes and slide decks.
Orgonaut keeps scenario activity, snapshots, and promotion lineage around the model. When an accepted scenario becomes Live, the relationship between the proposal and the new baseline remains available. That gives later reviews a better starting point. Leadership can examine what changed and whether the expected operating improvement appeared without relying on a filename such as final-v7-revised.
Give technical teams a technical surface
Engineering organisations already use versioned data, APIs, automation, and agent-accessible tools for the systems they operate. Organisational data should be available with similar discipline. Orgonaut provides a REST API, CLI, remote MCP access, and OrgSpec while preserving tenant scope, permissions, scenario boundaries, and human review.
A leader can use the application for visual planning. An analyst can generate a report through the API. An operator can inspect the model from the CLI. An approved assistant can answer questions or prepare work inside a bounded scenario. These are different ways into the same organisational model, not separate spreadsheets that need to be reconciled later.
