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AI Agent Swarm Architecture Planner
Building a team of AI agents? Map the roles, handoffs, costs, and risks before you commit to production. This planner outputs an architecture type, run-cost estimate, complexity score, and the first slice to ship.
Design the swarm before the agents argue
Most agent swarm projects fail not because the models are weak, but because the handoffs are undefined, the retry logic is missing, and no one owns the failure path. This planner forces those decisions before a single line of integration code is written.
More agents means more handoff risk
The dominant failure mode in agent swarms is not a bad model call. It is a misrouted handoff, a missing timeout, or an agent calling another agent in a loop.
Start with a chain, not a mesh
Mesh topologies look powerful on paper. In production, they create exponential call paths. Start linear, prove the workflow, then add branching where data shows it is worth it.
Cost scales with run volume, not agent count
A 5-agent swarm running 10 times a day is cheap. A 2-agent swarm running 10,000 times a day needs cost tuning. Plan for the volume you will actually have.
What the planner gives you
Architecture type
Whether your swarm should be a chain, hub, mesh, or hierarchical team based on roles and handoff needs.
Complexity score
A 0-100 score based on agent count, tools, handoff pattern, approvals, and run volume.
Run cost estimate
Per-run, monthly, and annual cost estimates based on agent roles, API calls, and human review time.
First production slice
The smallest viable swarm to ship first, with a clear path to expand after observing real failure modes.
Want us to build the production version for your business?
Bles Software designs and deploys private AI agent systems with real workflow integration, monitoring, human approval gates, and fallback recovery. Bring us your swarm idea and we will map the production architecture worth building.
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