ACIF runs infrastructure change through KVRM Agents. Review the execution plan, gate the risky steps with a human, and export a signed evidence bundle before the maintenance window closes — not during a postmortem a week later.
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The operator can issue work to the agent pool, see the deployment tracker move, and keep the session context attached to the same operating surface.
Dispatch work
Send natural-language instructions to active agents from a command center built for operations work.
Approve change
Review risky actions, hold them for sign-off, and keep production changes under operator control.
Track sessions
Watch the agent session, current task, heartbeat, and execution history in one place.
Capture proof
Evidence artifacts, timeline entries, and audit-chain status accumulate while the rollout is running.
kvrm agents
KVRM Agents are our proprietary registry-constrained decision architecture. Every request is routed through a versioned action registry, checked against the supported input envelope, validated deterministically, and only then allowed to execute, hold for approval, or hand off.
Agents do not invent new procedures at runtime. They work from versioned, explicit action sets that define what each agent is actually allowed to do.
Prediction is separated from execution. The requested action has to exist, fit the current operating state, and clear validation before the platform can run it.
If the request falls outside what the agent supports, ACIF can reject, hand off, or stop for operator review instead of forcing a best guess into production.
The platform stays auditable because the route is constrained up front: match the action, validate the fit, stop for approval when needed, then either execute or fail closed.
The registry decides whether the requested work is actually owned by a known agent path.
Inputs, environment state, and guardrails have to fit before the platform can move forward.
Approval holds are attached to the exact change state the operator is reviewing, not to a side channel.
The system either runs the supported action, routes it to the right owner, or stops with a visible reason.
execution flow
The product is strongest when the agent surface, the change surface, and the evidence surface are treated as one operating model instead of separate tools.
Agent roles, task progress, handoffs, and the event log stay visible at the same time, which makes the workflow legible while it is happening.
The blast radius, approval state, and execution controls stay bound to the request so there is no handoff gap between review and action.
The operator can search artifacts, switch evidence views, and keep the audit chain visible while the rollout is still active.
Discovery, review, and rollout make more sense when the environment map and the affected services stay on-screen next to the action surface.
Operators can start from plain language without dropping into a script pile or splitting intent across tools.
Change control stays human-gated when the blast radius matters, instead of turning automation loose by default.
Session state, command output, event history, and current task context remain visible in one operating surface.
Evidence, artifacts, and approval history are already captured before the change window closes and memory fades.
from prompt to proof
The operating model only works if the operator can see who is acting, what is waiting for approval, and what proof has already been captured.
operating record
The product gets stronger when the operator never has to switch mental models between planning, approval, execution, and proof.
Dispatch
Operators can issue instructions to the agent pool from a workflow built for operations rather than ad hoc terminals and scripts.
Control
The change surface keeps review, auto-approved work, pending gates, and the live feed in the same operating record.
Proof
Artifacts, command output, timelines, and audit status accumulate as the work runs instead of being reconstructed later.
operator console
Illustrative — not a live shell. These panels show how the product above thinks: a plan gets generated, gated steps wait for a human, execution runs, and evidence accumulates. The real UI is the tour above. This is the mental model.
↳ conceptual view · real UI lives in the tour above · CLI example, not a product promise
built for
No testimonials — we're in beta. The honest pitch is about the work itself: if your team runs the rollouts below, ACIF is built for the conditions you already operate in.
Hybrid estates where one maintenance window touches Windows, VMware, Azure, AWS, and the network in a single rollout.
Approval, rollback, and evidence obligations that all land on the same engineer in the same hour.
Operating surfaces that need to stay legible while the work is still running, not only after the retro.
Scenario 01
Several engineers, one change window, zero tolerance for a rollback that silently skips a step. ACIF gives the approval path and the undo a first-class home.
Scenario 02
Cisco, Arista, Juniper, Palo Alto, Fortinet in the same estate. ACIF keeps the execution surface consistent instead of letting tribal CLI patterns accumulate change by change.
Scenario 03
Plan, approval, execution, and artifacts end up on the same record — not three tools, four tickets, and a Slack thread to reconstruct a week later.
request access
The beta application asks about company size, industry, environment, deployment timing, and the rollout pressure you want ACIF to absorb. That gives us enough context to prioritize demos and early access around real deployment conditions.
Good fit
Hybrid infrastructure teams carrying Windows, VMware, Azure, AWS, and network changes in the same operating lane.
Rollout owners dealing with maintenance windows, approvals, rollback pressure, and evidence requirements at the same time.
Regulated or high-consequence environments where change control and proof matter as much as execution speed.
Beta scope
Guided deployment review against your environment, change path, and evidence obligations instead of a generic platform tour.
Early access to the command center, approval workflow, and evidence package around the operating motions you care about first.
Direct feedback loop on workflows, integrations, and KVRM guardrails needed before broader rollout.
Beta application
We ask the operational questions up front so demo time goes into your change model, approval path, and evidence requirements instead of generic discovery.
Company size and industry so we can size the deployment conversation correctly.
Environment and deployment window so we know which workflows matter first.
Role and rollout pressure so we can tailor the walkthrough to the team carrying the risk.
Work email gets beta invites, demo access, and release notes.