Going AI-native means letting agents operate real processes rather than assisting people through them. Once an agent can do the work, the next bottleneck is human review. Cogward brings authoritative enterprise state together with execution, identity, authority, and policy context to establish what actually happened, so established work closes unattended and people handle the exceptions.
When Cogward is in the execution path, that same supervisory state can govern what happens next.
Deploys inside your environment.
From human-checked to evidence-supervised to autonomous within declared bounds.
An AI-native operating model needs people out of the routine path. An agent reporting success is not enough to let them out, because successful execution does not prove the intended business state was reached.
Reading the business state on its own is not enough either. It does not say which action produced it, under whose authority it was taken, or whether the work stayed inside policy. Both views are partial, and a reviewer is what currently joins them.
When Cogward is inline it holds that execution context directly, rather than reconstructing it from whatever telemetry the harness emitted. That is what lets an effect in your systems be attributed to the attempt that produced it.
The full execution trajectory shows the refund dispatched with a stable effect identity, six recorded steps, the user it acts for, the policy that allowed it, and the agent reporting the work complete. The authoritative enterprise state shows the refund posted at the payment provider, the ledger not reconciled, and the support case still open, read fresh at 14:02. Bound to the same delegated work, the result is partial and the decision is to reconcile, then hold.
Cogward connects how the work ran with what your systems say happened, so supervision has the context to decide what comes next.
None of this asks the agent to be better than it is. It changes what happens after the agent acts, which is where the review cost actually sits. More work runs unattended, and the enterprise keeps the ability to establish, bound, and intervene. We do not redesign your process and we do not decide which work should be delegated: those stay yours.
Work whose result holds up against your own systems finishes without anyone checking behind it. That share is the unattended completion rate, the operating metric Cogward is designed to improve.
What it takes: resolving each work item to an outcome from your authoritative systems, rather than to the agent's account of itself.
The queue a person works holds the unknown, the contradicted, and the high-exposure. Review becomes evidence-driven and targeted rather than sample-driven.
What it takes: keeping uncertainty explicit. Missing, stale or unreachable evidence cannot quietly become a completion, and unknown blocks anything that would compound it.
Each supervised workflow accumulates evidence about what that class of work does unattended, so the next expansion is argued from production rather than from confidence.
What it takes: saying what done means where it pays. An implicit objective, a few declared conditions, or a formal completion definition, tightened only where you want more autonomy.
Cogward reads your systems over the connectivity you already have: direct APIs, MCP servers, iPaaS, event streams, warehouses, and your own adapters. See how a workflow moves through the stages →
Platform status. The runtime, identity, policy and evidence layers run in production today. The supervision layer above them is rolling out progressively, with design partners first. Available, preview and planned →
Reading the result needs no control over how your agents run. Holding, redirecting or recovering an action does, and that is the whole difference between the rungs below. You choose the depth per workflow.
Binding is not a status change. Cogward can hold an action before dispatch, prevent an unsafe retry, require approval, redirect the work, or hand a bounded case to a person.
The four rungs above supervise work running in another execution environment. The last one makes Cogward the execution environment: one durable execution context holds the work's objective, the authority it acts under, every effect it attempts, the evidence that came back, and the decision about what may happen next.
The evidence does not merely describe the run. It changes what the runtime is allowed to do next.
lightest integration → deepest supervision
the loop closes: control returns to execution, under the new state
Cogward works across the agent platforms you already run, and runs the work that warrants it inside its own runtime. Explore the runtime and platform architecture →
Reading your ledger, your CRM and your IdP happens under credentials you issue, so Cogward deploys where those systems already are: your cloud account, your private cloud, or on-premises. Raw work content does not need to leave your environment.
See deployment models → · See the production foundation underneath →
Whether your agents are still in pilot or already acting in production, the question is the same: can you establish what actually happened, preserve uncertainty when you cannot, and make that state govern what happens next? Cogward is the infrastructure for that across your existing agent estate, deepest when the work runs inside Cogward.
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