Production Monitoring with Codex: Grafana, Kubernetes, & Security

# Using Codex for Production Monitoring: Grafana, Kubernetes, & Security Collaboration **TL;DR:** In a 3 AM alert scenario, Codex can leverage custom skills to automatically collect evidence from Grafana dashboards, Kubernetes clusters, and security plugins — identifying causal chains, generating patches, and allowing the engineer to serve as a "human in the loop" who simply clicks approve, closing the loop from alert to fix in minutes. ## The 3 AM Alert: Where Does the Problem Start? 3 AM, the phone rings — checkout error rates are climbing. You open Grafana, but where do you even start? "This is the stuff of engineers' nightmares." First you need to figure out what's affected and what changed. Dashboards are just one piece — you also need deployment context and the relevant code. Gathering all that information and analyzing it carefully is often hours of repetitive work. That's exactly what this demo explores using Codex. ## Scenario 1: Checkout Error Rates on the Grafana Dashboard ### The Symptom In the demo scenario, the team is pushing a new version of the repo. The latest release **V2** is currently running, but checkout error rates have climbed to around **20%**, with no clear cause identified. This is where Codex comes in with a custom skill to investigate checkout-related evidence and determine what caused the failure. ### Codex's Evidence Collection Process Codex gets to work, gathering the necessary evidence to verify whether everything is functioning as expected, including: - Successful checkout records - Response times - Service health status - And nearly every other response metric The results come back within seconds. The engineer simply types `approve`, and the newest version of the app is pushed — fixed, patched, and ready. ### The Result: Zero Errors, No Rollback After the push, checkout error rates returned to **zero**. Notably, there was **no rollback to an older version** — the system remained on V2, the latest version. Codex successfully leveraged the repo's **restricted evidence workflow** and **release spec** to investigate and fix the issue. ### Dashboards vs. Agents: A Different Way to Gather Information From the Grafana dashboard perspective, the key things you're looking at are: checkout health, the current release version, error rates, and the actual P95 metrics. In a manual workflow, an engineer might need to: 1. Check the dashboard 2. Figure out where the logs are 3. Look for other dashboards that might show more information 4. Trace what changed in the code — and which specific commit it was In the demo scenario, this information was primarily **collected by Codex**. The engineer offloaded a huge amount of manual gathering work to an agent workflow, which proposed a fix for them to approve as the "human in the loop." The entire process went from a frantic hour to **a few minutes**. ## Scenario 2: Containers & the Kubernetes Cluster "Hey Ankur, did you know this works the same way for containers and Kubernetes clusters?" — "I didn't, let's find out." ### Cluster Topology & Failure In this version, the app consists of three parts: - An **inventory API** cluster - An **orders API** cluster - An **edge gateway** The new version being rolled out is for the inventory API. This is standard developer work, and it appears to have passed the CI/CD pipeline. But here's the problem: **the container was OOM Killed no matter what**, triggering a **cascading failure** that took down the entire cluster. ### Kubernetes Rollout Investigator Skill The team used the **Kubernetes rollout investigator skill** to look into the current rollout. Codex investigated, identified the specific issue, and **mapped out the causal chain** — the presenter emphasized that this "may be the most valuable part of Codex, whether for security or production monitoring." Once the patch was ready, another `approve` click pushed the new version of the inventory API container. The service immediately returned to a healthy baseline: orders API running normally and the edge gateway back online. ### Two Levels of Automation Similar to Scenario 1, the engineer didn't collect the information themselves — they offloaded the work to Codex while remaining in the loop, allowing them to move through the fix faster than they could on their own. In both cases, the flow is the same: **receive an alert → open Codex → let it propose a fix → approve manually**. There's also an alternative: you can **self-host a runner** in Kubernetes, Grafana, or any other observability platform to monitor for out-of-baseline alerts. Once triggered, Codex can step in and fully automate the entire process from alert to proposed fix. If you want to remove yourself from the loop entirely, you can build a **multi-agent system** where other agents verify fixes and push them out without your involvement. ## Scenario 3: Where Production Monitoring Meets Codex Security An interesting aspect of production monitoring is that it also intersects with **security**. The final example combines production monitoring with **Codex Security**, working together to keep services available. ### Resource Starvation Causing Performance Issues Shortly after this particular app started up, the team hit a traffic issue: **even though nothing was recently deployed, a reporting request was starving checkout resources in the shared worker pool**. In plain English, that request was trying to grab too many resources at once and was dragging down the service. ### Hands-Off: Codex Security Generates the Patch The response? Do nothing — Codex handles it. Simply approve the patch generated by the **Codex Security plugin**, then **replay the request** — and you can see that the extremely expensive request was successfully blocked. The presenter's takeaway: "What's interesting is that a security requirement actually helped us fix a production monitoring problem." ### About the Codex Security Plugin If you haven't tried the Codex Security plugin yet, it's an incredibly powerful tool and highly recommended. There's a lot of cross-cutting potential in this direction that hasn't been fully explored yet — go check it out. ## Summary of Core Patterns The three scenarios reveal a consistent pattern: | Stage | Traditional Approach | With Codex | | --- | --- | --- | | Information gathering | Engineers manually dig through logs, hunt dashboards, trace commits | Agents automatically collect evidence and identify causal chains | | Fix proposal | Manual root-cause analysis and hand-written patches | Skills generate patches based on restricted evidence workflows and release specs | | Deployment decision | Hasty actions under pressure | Human in the loop clicks approve, or hands off to a multi-agent system | | Time cost | ~1 hour of frantic work | Done in minutes | The key takeaways: **no rollback, only roll forward** — the problem is fixed on the latest version; **no bypassing the process** — restricted evidence workflows and release specs constrain Codex's investigation scope; **no removing human decision-making** — unless you actively choose to hand it off to a multi-agent system. The demo concluded by noting that if anyone in the audience had questions or wanted to discuss further, the team was happy to talk. Source: Production Monitoring with Codex: Grafana, Kubernetes, & Security — OpenAI (https://www.youtube.com/watch?v=RQfg-zo4R-g)