AI monitoring agent — live in production

DevOps solutions,
not consultants.

We package your DevOps scope into fixed-price, outcome-driven engagements — delivered in weeks, not quarters. You define the problem. We deliver a working system.

2–8w
project delivery
↓40%
deployment lead time
clear
scope per engagement
A
ops-agent APP #platform-alerts
⚠ InferenceHighLatency | WARNING | production
p95 inference latency: 113s (threshold 90s) — 4th firing today
Status: Likely self-resolving. Heavy job completed 13:10 UTC. Fleet idle. Alert clears ~13:20 UTC.
Root cause
Pod worker-pod-a3f9 ran a long-duration job (160s). Heavy jobs occupying 1–3/6 pods pushes short-job queue p95 above threshold.
Recommended
1. Scale to 8 replicas · 2. Raise threshold · 3. Separate routing for long vs short jobs
Not escalating — fleet healthy · self-audit: 10 claims, 2 hypothesis, 1 did-not-check
Works across
Any cloud stack Greenfield & legacy Startups to enterprise AWS · Azure · GCP On-prem & hybrid
The problem

Your DevOps stack is costing
more than it should.

Engineering teams waste cycles assembling tooling from scratch every project. Consultants leave. Docs drift. Releases break.

🔁

"We redo setup every environment"

Every new project means re-assembling CI/CD, IaC templates, and monitoring from scratch. No golden path, no standards.

🚨

"Deployments break every sprint"

Failed releases, manual rollbacks, engineers pulled into incident bridges instead of building product.

👥

"We can't afford a full platform team"

Hiring senior DevOps/SRE takes 6+ months and costs $180k+ per head. But the work still needs to happen.

How we work together

Three ways to engage.
Each with a clear scope.

Every engagement starts with a scoped assessment. Pricing is agreed upfront based on what's actually needed — not a menu you squeeze everything into.

Defined start & end

Project

A time-bound delivery with a specific outcome. We scope it together, agree what done looks like, and deliver. You own everything at the end.
  • Scoped in a discovery session before any work starts
  • Clear deliverables — systems, not slide decks
  • Milestones and checkpoints throughout
  • Full documentation and runbooks on handoff
  • Your team trained and in control at the end
Example scopes
CI/CD foundation · IaC setup · observability stack · Kubernetes migration · security baseline · compliance readiness
Strategic · long-term

Infra Partner

A deeper relationship where we act as your infrastructure supplier and strategic DevOps partner — across multiple teams, initiatives, or the full platform lifecycle.
  • Multi-team or full platform scope
  • Architecture decisions and vendor evaluation
  • Cloud cost strategy and FinOps
  • Hiring and team capability building
  • Quarterly roadmap planning
  • Dedicated point of contact, senior level
Right for you if
You're scaling fast, facing platform complexity across multiple teams, or want an external senior partner who owns outcomes — not just tasks.

All engagements: cloud-agnostic · you own all code & documentation · pricing agreed in discovery, not upfront

A
ops-agent APP 3:17 PM
#platform-alerts
⚠ InferenceHighLatency | WARNING | production
p95 inference latency: 113s (threshold 90s) — 4th firing today
STATUS
Likely self-resolving — primary slow job completed at 13:10:13 UTC. Fleet now idle. Alert should clear as samples age out of 10-min Prometheus window (~13:20 UTC).
Pattern today
10:43 — 1st fire (p95=109.5s): transient queue buildup, self-resolved
11:07 — 2nd fire (p95=116.4s): queue saturation, self-resolved
12:12 — 3rd fire (p95=115s): 3/6 pods on heavy jobs at ~155s
13:08 — 4th fire (p95=113s): 1 pod at 160.2s ← this one
Root cause (confirmed)
Pod worker-pod-a3f9 processed a long-duration job (160.2s). Heavy jobs occupy a pod long enough to starve the short-job queue when ≥2 land concurrently.
[job-7c4a] type=long-form, duration_est=160s, steps=8
resolution=high, mode=quality
Completed: 160.2s
RECOMMENDED FOLLOW-UP
1. Scale worker pool to 8 replicas — more headroom reduces p95 sensitivity
2. Raise threshold or add job-type-specific alert — current threshold too tight for mixed workload fleet
3. Consider separate routing for long-duration jobs — prevents heavy jobs starving short requests
Not escalating — no data loss, no pod failures, no GPU degradation. Fleet healthy and recovering.
self-audit: 10 claims cited · 2 downgraded to hypothesis · 1 entity marked did-not-check
✦ Live in production — AI Agent

Not just an alert.
A full diagnosis, posted to Slack.

That's a real message from our monitoring agent running in a client's production environment. When an alert fires, it doesn't just forward a metric. It traces the root cause, reconstructs the event timeline, confirms fleet state, and posts actionable recommendations — all before an engineer looks at the screen.

🔍
Root cause analysis, not just forwarding
Correlates logs, metrics, and pod state across your fleet to explain why an alert fired — not just that it did.
📋
Pattern detection across firing history
Tracks repeated firings, identifies whether they're structural or transient, and adjusts recommendations accordingly.
🔧
Actionable follow-up, not noise
Every report ends with specific, prioritised next steps — and a clear "no action needed now / escalate if X" decision. Engineers stop waking up to guesswork.
Want this for your stack?
We adapt and deploy the agent to your infra, alert rules, and Slack setup as part of the engagement. Book an assessment call →
Customer stories

Teams that stopped fighting their own tooling.

Details anonymised at client request.

SaaS · 150 engineers
10 weeks
From monthly releases to daily deploys

Platform team was rebuilding CI/CD from scratch for every product team. No shared templates, no standards. We delivered a golden-path pipeline library, IaC module set, and observability baseline. They went from 1 deploy/month to 12+ with zero rollback events in the first 60 days.

12×
deploy freq
↓ 85%
lead time
0
rollbacks
AI Infra · GPU fleet
Ongoing
Monitoring agent deployed across production GPU fleet

AI inference company needed deeper alerting than standard Prometheus could provide — they wanted root cause analysis, not just metric forwarding. We deployed our monitoring agent to their Kubernetes GPU fleet. It now posts structured Slack diagnoses on every alert, with pattern history, fleet state, and ranked recommendations.

↓ 70%
alert noise
< 5m
diagnosis time
24/7
coverage
FinTech · regulated
14 weeks
SOC2-ready delivery pipeline from scratch

FinTech company approaching their first SOC2 audit had no audit trail, no change management workflow, and no policy enforcement in their pipelines. We delivered policy-as-code, evidence generation, secrets management, and a compliance dashboard — scoped and priced upfront. They passed their audit with zero findings related to pipeline controls.

0
audit findings
100%
policy coverage
14w
fixed scope
How it works

From first call to live system in 4 steps.

No open-ended engagements. Every stage has a clear output you own.

1

Assessment

Free 30-min call. We map your current stack, pain points, and target outcomes.

2

Blueprint

Scoped implementation plan with milestones and agreed outcomes. Reviewed and signed off before any work starts.

3

Delivery

We build, configure, and test. Weekly check-ins. You see working systems, not decks.

4

Handoff + Retainer

Full documentation, runbooks, and team training. Handoff to your team — or continue as an ongoing engagement.

Get started

Book a free 30-minute DevOps assessment.

We'll map your current stack against your goals and tell you exactly which package gets you there fastest — no pitch, no obligation.

Or email directly: denys@opspackaged.com · Responds within 24 hours