No shared state
Two agents researching the same question produce two incompatible answers. Nothing in the stack decides which one is true.
AI workforce infrastructure
Your agents should work like a cadre, not a crowd.
Cadremesh coordinates a team of specialised AI agents around one plan, one shared memory and one accountable owner. Roles are bounded, handoffs are explicit, and every run leaves a ledger you can actually read. The difference shows up where it matters: work that finishes, instead of work that starts.
Pre-launch · built for teams already shipping agent features
Single agents demo well. Teams of agents fail in predictable ways.
01 / The gap
01
02
Handoffs without receipts
Work moves between steps as free text. When an output is wrong, nobody can name the step that broke it.
03
Nobody owns the outcome
Autonomy without a named owner is just an unbounded loop with a budget attached to it.
Three ideas, applied without exception.
02 / The model
01
Define the role, not the prompt
A role states what an agent may decide, what it must escalate, and which tools it can touch. Prompts change per task; roles stay stable, so behaviour stays auditable.
02
Connect the mesh
Agents share a working memory scoped to the mission, not the conversation. Every write is attributed, every read is traceable to the step that needed it.
03
Assign the mission, keep the veto
A human owns the mission and the checkpoints. Agents run inside those boundaries; anything outside them stops and asks, rather than guessing.
The unit of automation isn't the agent. It's the team, and how it hands work to itself.
What coordination infrastructure has to provide.
03 / CapabilitiesMission-scoped memory
One store per mission, with attribution on every entry. No cross-contamination between customers or projects.
Role boundaries
Declared tool access and decision rights per role. An agent that needs more asks for it explicitly.
Handoffs with receipts
Structured artifacts between steps — inputs, assumptions and outputs — so failures are located, not guessed at.
Human checkpoints
Approval gates where the cost of being wrong is high. Configurable per role and per action class.
Run ledger
A replayable record of every decision, tool call and model used. Enough to answer an auditor without exporting logs by hand.
Model-agnostic
Routing across providers per role or per step, so a cheaper model can do the boring parts and a stronger one handles the edges.
Early access
We're opening Cadremesh to a small number of teams running multi-step agent work in production. Tell us where your current setup breaks and we'll show you what the mesh does differently.
Or email hello@cadremesh.com directly.