Mosaic is the orchestration layer for AI workflows. Wire specialized agents into a typed graph, route each step to the one built for it, and run the whole thing with retries, shared memory, and full traces — like a system you'd actually trust on call.
Powering agent workflows for teams shipping to real users
Single agents stall, loop, and hallucinate the moment a task gets real. Mosaic gives a team of agents the structure, state, and supervision they need to finish the job.
Define your workflow as a graph of agents with typed inputs and outputs. Mosaic validates every edge before a run starts, so a step never receives a shape it can't handle and a handoff never silently drops data on the floor.
Route each step to the agent — and the model — best suited to it. Cheap and fast for triage, a frontier model for the hard reasoning, a local model for anything sensitive.
Agents read and write a scoped, versioned memory store, so context survives a handoff instead of getting flattened into a lossy summary at every hop.
Every run is checkpointed. A model timeout or a redeploy resumes from the last good step — it never restarts the whole workflow from zero.
Pause on any step for an approval, an edit, or an answer, then resume exactly where it left off — no rerun, no lost state.
Mosaic ships a library of composable agent archetypes — and lets you define your own. Drop them into a graph, give each a tool belt, and let the orchestrator decide who works on what.
Breaks a fuzzy goal into a concrete, ordered task graph the rest of the agents can actually execute against.
Pulls the right context from your vectors, databases, and APIs so every downstream step reasons over facts, not guesses.
Writes, edits, and runs code in an isolated sandbox, then hands a tested diff to a reviewer before anything ships.
Checks another agent's output against your rules and rubrics, and sends work back for a redo when it doesn't pass.
Calls your tools and external systems with scoped credentials, idempotency keys, and a full audit trail on every action.
Watches the whole run, decides who goes next, breaks loops, and escalates to a human the moment confidence drops.
What changes when agents stop working alone
You can't ship what you can't see. Mosaic instruments every agent, every tool call, and every token, so a run is something you can debug step by step — not a black box you hope behaves.
Replay any run step by step — every prompt, tool call, model response, and handoff captured with timing and cost.
Set hard ceilings per run, per agent, or per workflow. Mosaic halts gracefully before a runaway loop spends your month's budget in an afternoon.
Pin a workflow to a graded test set and gate deploys on it, so a prompt tweak can't quietly regress the whole pipeline.
Define agents and graphs in TypeScript or Python, run them locally, then deploy the exact same code to managed infra.
“We spent two quarters trying to make one mega-agent reliable and it never crossed 60% on real tickets. We rebuilt it as five small agents in a Mosaic graph in a week and shipped it past 90%. The handoffs were the whole unlock.”
“The traces are the reason we trust it in prod. When a run goes sideways I can replay every step, see exactly which agent made the wrong call, and fix that one prompt — instead of guessing at a black box.”
“Durable runs sold our on-call team. A model provider had an outage mid-workflow and every run resumed from its last checkpoint when it recovered. Nothing restarted, nothing was lost, nobody got paged.”
Build and run locally for free. Pay for managed orchestration, shared memory, and traces when you take a workflow to production — metered by run, not by seat.
For prototypes and side projects.
For teams running agents in production.
For regulated, high-volume workloads.
Both. Mosaic ships a library of agent archetypes to start fast, but any agent is just a typed function with a tool belt — so you can wrap your existing code, prompts, or third-party agents and drop them into the same graph. The orchestrator treats your custom agents exactly like the built-in ones.
Mosaic is model-agnostic, with 40+ providers supported out of the box. Route each agent to OpenAI, Anthropic, Google, Mistral, or any OpenAI-compatible endpoint — including local and self-hosted models — and mix providers freely inside one workflow. Swapping a model is a one-line change, never a rewrite.
Every run is checkpointed and supervised. Failed steps retry with backoff, runs resume from the last good checkpoint after a crash or redeploy, and the supervisor breaks runaway loops and escalates to a human when confidence drops or a budget is hit. A bad step degrades gracefully instead of taking the whole workflow down.
Hand-rolled chains break the moment you need retries, shared state, parallel steps, human approvals, cost ceilings, and traces — and you end up rebuilding an orchestrator badly. Mosaic gives you a typed graph, durable execution, shared memory, and full observability out of the box, so you write the agents and we run the system around them.
Yes. Develop against the SDK locally, then choose managed Mosaic Cloud or a self-hosted deployment inside your own VPC. Enterprise adds zero data retention and bring-your-own model keys, so prompts and outputs never leave your environment.
Define your first graph in minutes with the SDK. No credit card, no sales call — wire up three agents and watch them finish the job together.