
Nforgent Ops coordinates specialized agents for Kubernetes operations, cloud infrastructure, and FinOps—turning operational signals into evidence-backed decisions and governed actions. Outcome: fewer escalations, faster incident closure, and more throughput per operator—without compromising change control. Self-learning continuously improves all agents via incident memory (RAG).
OPERATIONAL DRAG
Incidents rarely start with a single clear failure. They begin as scattered anomalies across Kubernetes, cloud infrastructure, and CCS/OCS platform workloads (e.g., network paths, gateways, and event-driven integrations). NOC/SRE/Platform teams spend hours correlating evidence across tools and coordinating approvals—while customer impact grows and escalation queues build.
Result: longer outages, higher on-call load, and slower throughput per operator—under strict change control.
SIGNAL FLOOD
Alerts arrive faster than context, masking the primary failure and increasing triage load across teams.
CONTEXT GAPS
Evidence is fragmented across observability, Kubernetes, and cloud control planes—slowing correlation and extending MTTR.
GOVERNANCE FRICTION
Even when teams suspect the cause, change control adds delay to every action—raising risk during high-severity incidents.
Signal
A single symptom (latency and CCS request timeouts) triggers parallel investigations across Kubernetes, cloud infrastructure, and CCS interface paths (Diameter/REST) plus event streaming dependencies. Without a reasoning path, teams spend hours correlating signals while impact and escalations grow.
Nforgent Ops reduces MTTR and operator load by turning scattered signals into an evidence-backed plan—then executing safely under policy. Built for cloud-native telecom environments with dedicated Kubernetes, Infrastructure, and FinOps agents, plus a self-learning domain memory (RAG) that improves recommendations over time.
Unify alerts, logs, traces, and events into a single incident timeline with shared domain context and consistent naming across teams.
Connect platform health to service-interface paths (Diameter, REST, Kafka) and shared dependencies to explain blast radius and likely upstream triggers.
Generate a ranked remediation plan with prerequisites, safety checks, and expected outcomes—before any change is proposed or executed.
Autopilot executes only pre-approved actions. Over-the-Shoulder pauses for operator approval at each gate. Consultant produces a guided runbook with no execution.
Capture evidence, decisions, and outcomes for audit and post-incident review—then feed the learnings into domain memory to improve future recommendations.
Outcome: fewer escalations, faster incident closure, and higher operator throughput—without compromising change control.
PLATFORM MODULES
Every module runs on the same connectors, evidence capture, and policy-gated change workflow. Start with Kubernetes incident response, then expand into Infrastructure provisioning (IaC) and FinOps—reducing MTTR, operator workload, and unnecessary cloud spend.
Detect incident patterns across clusters, correlate alerts/logs/metrics into a single timeline, and recommend safe remediations—restart/rollback, scaling, pod/node isolation, and configuration fixes—under policy gates and approvals.
Turn intent into policy-checked infrastructure plans with cost estimates, security guardrails, and Terraform-ready outputs for review and controlled execution.
Detect spend anomalies and cost drivers caused by misconfigurations and overprovisioning (e.g., load balancer exposure, node group sizing, idle capacity). Quantify impact, recommend the lowest-risk fix, and record an auditable decision trail.
Deployed in the customer environment for security, data residency, and compliance. Run with your own self-hosted LLM, or connect to an enterprise provider using your existing keys—under the same policy gates, approvals, and audit controls.
Same modes apply: Autopilot executes approved changes, Over-the-Shoulder requests approvals, Consultant provides recommendations only.
Application UI
Nforgent Ops interface highlights incident-first operations with policy gates and audit-ready actions. These screens show how operators triage signals, correlate evidence, track progress, and execute approved changes through governed workflows.
Screens reflect the current build and are shared for evaluation only. Minor UI changes may be made ahead of GA.

Incident-first operations workspace
Monitor incident volume and severity trends, track active investigations, and scope by environment and system boundaries—so triage stays fast, consistent, and auditable.
Roadmap
A staged rollout aligned to operator readiness, security reviews, and change-control requirements.
Stage 1
Stage 1 — Core platform (delivered)
Incident model, policy gates, approvals, audit trail, and baseline integrations.
Stage 2
Stage 2 — Design partners & controlled pilots (in progress)
Workflow validation with real environments, controlled rollout, and operator feedback loops.
Stage 3
Stage 3 — Hardened beta
Security hardening, scale and reliability testing, and production-grade observability.
Stage 4
Stage 4 — GA
Reference deployments, support readiness, and repeatable onboarding.
REQUEST DEMO
Customer-hosted by default (VPC / on-prem / sovereign cloud). Private demos available.
Telecom operator demo
Investor materials