REAP

The Problem

Networks generate more signals than any team can process, and the tools built for a previous era of IT observability were never designed to reason about them. In most incidents the answer already exists — it is just spread across six tools. When the tools cannot correlate, the human has to, and that human is usually the most experienced engineer on the team. And every incident teaches someone, while the system learns nothing.

What REAP Does

REAP works with the systems a team already runs — network management platforms, ITSM, telemetry pipelines and configuration state — and reasons across them. Its AI correlates signals across topology, telemetry and change history to identify root cause, impact scope and a confidence level. It then surfaces a recommended action and, where approved, executes it with a full audit trail, ITSM sync and engineer-in-the-loop controls. Traditional AIOps stops at alert correlation and noise reduction. REAP is agentic: it reasons about root cause, then executes remediation runbooks.

The Platform

Where It Runs

Built for service providers, large enterprises and data-center networks — for the network engineer, the network architect, the NOC lead, the VP of Infrastructure and the CTO. It speaks the vendors those networks already run: Cisco, Arista, Juniper, HPE Aruba, Palo Alto Networks, Fortinet, SONiC, AWS and Azure; and the tools those teams already live in: ServiceNow, Jira, Slack, Microsoft Teams, NetBox and CyberArk.

The Team and the Targets

REAP is being built by a founding team that helped build the networking infrastructure enterprises depend on — Andiamo, Insieme, Pensando, WiteSand (acquired by Juniper Networks), Cisco, Juniper and Aruba, with more than thirty patents between them — as the platform they wish they had had while running those networks. The outcomes it is designed for: a 78% reduction in mean time to resolution, 85% of alert noise eliminated, a 3× multiplier on engineer capacity, and under five minutes from alert to root cause. REAP states these as target outcomes based on its reference architecture, not measured customer results — a distinction I hold to on every page I write, because a claim a buyer can verify is worth ten a buyer has to take on faith.

Why It Matters to My Work

REAP is the argument of my Field Notes on the Agentic Enterprise applied to the network: an agent that reasons is allowed to act only when its authority is written down, when a human stays in the loop for consequential changes, and when every action leaves evidence an auditor can read. Selling that to network operations leaders — buyers who have heard AIOps promises before — is the same discipline I bring to every revenue engine: define the outcome, instrument it, and sell what you can prove.