"Is it the platform, or is it the app?" One investigation answers it.
When an incident hits an OpenShift estate, teams lose time disputing jurisdiction across separate tools. The Production Ops Agent runs one causal investigation across live cluster state and application telemetry, and returns the answer in under 5 minutes.
Runs inside your environment. Queries the monitoring you already have. Your team keeps the incident.
- Latency spike on checkout API
- Custom Operator stalled mid-reconcile
- Connection pool drained, no threshold tripped
- Root cause returned with evidence chain
The jurisdiction problem
Triage stalls at the boundary, not at the bug.
Cluster state belongs to the platform engineering team. Application telemetry belongs to service owners. Each side works in different dashboards, watches different signals, and joins a different bridge. When a service degrades, the platform team checks node pressure, router saturation, and Operator health while the application team inspects trace latency, database connections, and container logs.
Because the tooling is split along the same seam as the org chart, resolving a multi-tenant issue turns into two teams proving the failure is not theirs. That proof of innocence is what extends MTTR, not the difficulty of the fix.
Application telemetry
Owner: service teams
Live cluster state
Owner: platform engineering
Two teams, two toolsets, one outage.
of teams move across four or more tools during a live incident. Every hop is context rebuilt by hand before anyone can say where the failure started.
The mechanism
Context Engineering: intelligence on top of the tools you already run.
The Prod Ops Agent does not ask you to re-instrument, re-ingest, or replace anything. Context Engineering queries your existing monitoring infrastructure in place. No second ingestion bill. No displacement of the monitoring investments you have already made.
In-place telemetry
Live cluster state
The Production Ops Agent
Context Engineering
In-place signal correlation
Connects directly to Prometheus, Thanos, Alertmanager, and commercial APM including Splunk, Datadog, and Dynatrace. Metrics, traces, and logs are read together, with no raw telemetry shipped across a SaaS boundary.
Cluster topology traversal
Maps OpenShift objects as they change, from Operators and Routes to Deployments, Services, and Node lifecycle state.
Causal reasoning across the boundary
Cluster state and application metrics are read in parallel to isolate whether a latency spike came from a code regression, a JVM memory limit, node contention, or Operator reconciliation, with the evidence chain attached.
Early degradation detection
Multidimensional pattern analysis surfaces cluster and service degradation 30 to 60 minutes before static thresholds trip.
In the product
What the OpenShift integration looks like.
The same investigation, from both ends: the tools it reads and the answer it returns. No new dashboards to learn, no telemetry leaving your environment.
Representative product UI. Data shown is illustrative.
Blind spots
The components nobody instrumented are the ones that page you.
Auto-instrumentation and commercial APM watch application processes. They leave the platform-side dependencies unwatched, and that is where OpenShift outages tend to start.
Custom Operators
Reconciliation loops that stall, enter crash loops, or exhaust cluster RBAC permissions without emitting application telemetry.
Asynchronous jobs and cron
Background tasks and queues that fail silently, leaving downstream services starved of data.
Network and ingress policies
Route saturation, OVN-Kubernetes policy updates, and service mesh misconfigurations that present as application timeouts while application code stays healthy.
Uninstrumented deployments
Ephemeral workloads and emergency patches pushed to production namespaces with no alert definitions or SLO bindings attached.
of teams have experienced an outage where no alert fired and a customer noticed first. When these components fail, static thresholds have nothing to trip against.
Use cases
Four incidents that start as a jurisdiction argument.
Every one of these opens with two teams disputing whose domain broke. One investigation spans both sides of the seam and returns the answer in under 5 minutes.
Pod CrashLoops
under 5 minThe 3am question
Apps says the pods keep restarting and the last deploy looks clean. Platform says the nodes are healthy and nothing was evicted.
What one investigation finds
A ConfigMap change four hours earlier left one container reading a stale value on restart. The crash loop tracked the rollout, not the node.
Where it lands
Root cause with the evidence chain, matched to the pre-approved Ansible Job Template for a controlled rollback.
Before
40 min
After
3 min
Operator Failures
under 5 minThe 3am question
Apps says the API is timing out and the code has not changed. Platform says the nodes are healthy and there is no pressure.
What one investigation finds
The custom Operator managing the backing service stalled mid-reconcile four hours earlier, silently, with no alert defined. Downstream connection pools drained slowly enough that no threshold ever tripped.
Where it lands
Root cause with the evidence chain, matched to the pre-approved Ansible Job Template for Operator restart.
Before
60 min
After
5 min
Route Misconfigurations
under 5 minThe 3am question
Apps says a slice of requests is failing at the edge and the service is up. Platform says the route exists and the pods are ready.
What one investigation finds
An ingress policy update narrowed a route that a canary namespace still depended on. Application code stayed healthy while a fraction of traffic hit a closed door.
Where it lands
Root cause with the evidence chain, matched to the pre-approved Ansible Job Template for the ingress policy fix.
Before
35 min
After
4 min
Resource Pressure
under 5 minThe 3am question
Apps says latency is climbing under normal load. Platform says utilization looks within range on the dashboards they watch.
What one investigation finds
A JVM memory limit met a noisy neighbor on the same node. Contention built between the thresholds either team was watching, so neither dashboard flagged it.
Where it lands
Root cause with the evidence chain, matched to the pre-approved Ansible Job Template for right-sizing and rescheduling.
Before
50 min
After
3 min
Remediation and automation
From root cause to a fix your team already trusts.
Finding root cause is half the work. Applying a fix safely in a high-consequence environment is the other half. The Prod Ops Agent connects investigation to deterministic execution through integrations with tools such as Red Hat Ansible Automation Platform, ServiceNow, and Jira.
Root cause analysis
Evidence chain attached
Match Ansible Job Template
Pre-approved only
Approve or safe auto-run
Your policy, your RBAC
Human-in-the-loop verification
Root cause is surfaced with a recommended fix matched to a pre-approved Ansible Job Template. Whether it runs is your call.
Graduated autonomy
Promote trusted fixes to automatic execution when you are ready. RBAC and full audit trails stay intact.
Operational impact
What changes in production
Fewer bridges, faster answers, and the same chain of command. Your team keeps the incident.
94%
RCA accuracy in under 5 minutes
30 to 60 min
Early warning before thresholds trip
80%
Fewer P1 war rooms
40%
Of ops FTE time recovered
60%+
Lower incident cost
83% of teams move across 4+ tools in a live incident.
78% have had an incident where no alert fired and a customer noticed first.
Industry condition, not NeuBird AI results.
Foundation
Runs inside your boundary.
Regulated, hybrid, and air-gapped OpenShift estates need the data boundary to hold. The Prod Ops Agent operates under a zero-storage architecture, so telemetry never has to leave your perimeter to be reasoned over. NeuBird AI works with Red Hat OpenShift.
Deployment models
On-premises, inside your VPC, across hybrid estates, or fully air-gapped.
Zero storage
Telemetry, traces, and cluster state stay inside your security perimeter and are never retained on external servers.
Compliance
SOC 2 Type II compliant architecture with complete administrative audit logs.
Data boundary
We can't leak what we don't store.
SaaS-only tools require your telemetry to cross a vendor boundary before anyone can reason over it. This one does not. Your team keeps the incident, and the data never leaves the room.
FAQ
Common questions
Does this replace Prometheus, Datadog, or Dynatrace?
No. Context Engineering queries them in place. Your monitoring stack, retention, and dashboards stay exactly as they are. There is no second ingestion pipeline and no second ingestion bill.
What access does it need inside the cluster?
Scoped access to the OpenShift API for cluster state and to your existing metric, log, and trace endpoints. Permissions are yours to define, and every action is bound by role-based access control with a full audit trail.
Does our telemetry leave our environment?
No. The Prod Ops Agent runs on-premises, inside your VPC, or in an air-gapped zone under a zero-storage architecture. Telemetry, traces, and cluster state stay inside your perimeter.
Does this work on ROSA and ARO, or only self-managed OpenShift?
All three. The investigation spans cluster state and application telemetry the same way regardless of who runs the control plane.
Can it take action, and who approves it?
Your team keeps the incident. Findings are matched to pre-approved Ansible Job Templates and surfaced for approval. Teams can promote specific standardized fixes to automated execution once they have confidence, and that promotion is always their decision.
Ready to transform your OpenShift operations?
Connect your OpenShift cluster in minutes. The Production Operations Agent will start learning your environment and detecting incidents immediately.