// Use cases

What the suite does that no single product does.

Each product stands on its own. These are the workflows that only work because they share one graph — where memory, code context, routing and validation are the same state, not four systems agreeing with each other.

01 // agent onboarding

Start a session that already knows the codebase

A fresh agent session begins blind: no memory of last week, no idea why the retry loop is odd. Wire CTXone and AgentStateDeveloper into the same MCP connection and the session opens with the team’s decisions and the code’s own ledger already in hand.

$ ctx recall "payments architecture"
$ asd ledger get payments.chargeCard
CTXoneAgentStateDeveloper
02 // long-running work

Carry a plan across weeks and sessions

Multi-week work dies at the session boundary — the plan drifts, the reasoning evaporates, and the next session re-derives decisions that were already settled. A plan held in the graph is state: resumable, checkable, and branchable when you want to try a direction without losing the current one.

$ ctx plan next
$ ctx branch spike/new-schema
CTXoneAgentStateGraph
03 // regulated deployment

Put agents in a workflow that has to survive an audit

In regulated environments the question is never just "did it work" — it is who authorised it, under what policy, and can you prove the record was not edited afterwards. Provenance on every write, policy gates on sensitive paths, and a hash-chained audit log make that answerable rather than aspirational.

$ ctx why-did-we /memory/legal/retention
$ asd audit verify
AgentStateGraphAgentStateDeveloperCTXone
04 // model selection

Choose a model on your workload, not a benchmark

Public benchmarks rank models on someone else’s tasks. Branch the same conversation across several providers to compare them side by side on work you actually do, then let routing history turn that comparison into a standing decision per task type.

# fork one thread across providers,
# then route on the observed result
ThreadWeaverAgentStateRouter
05 // change validation

Prove a change before it reaches production

You changed a prompt and one run passed. That is a single sample from a distribution you have not examined. Run the scenario against multiple agents, capture each decision path as a graph, and check the blast radius of the underlying code change in the same pass.

$ asd effects_of processOrder
# then replay the scenario and diff runs
AgentStateCrucibleAgentStateDeveloper
06 // spend control

Cut agent spend without capping quality

Blanket downgrades to a cheaper model save tokens and cost more overall, because the retries land somewhere you are not measuring. Route on cost-per-unit-of-work drawn from your own history, with budget circuit breakers, and keep every routing decision as a commit you can interrogate later.

# route by task type on observed
# completion cost, not token price
AgentStateRouterAgentStateGraph
// Get started

Adopt one layer, or the graph underneath.

Nothing here requires the whole suite. Each product is self-hosted and MCP-native on its own — they compound when they share a graph.

Compare the products → Read the blog