Date: July 3, 2026
Hello, colleagues. If you work with Prometheus, you know: writing alerting rules, configuring exporters, and creating dashboards in Grafana takes dozens of hours of routine work. And what if there are many metrics and the team is small? Sounds familiar.
At ASI Biont, we solved this problem radically: the AI agent writes integration code for the Prometheus API itself. You don't have to wait for developers to add support for another service. Just provide an API key in the chat — and the AI starts working.
In this article: how ASI Biont connects to Prometheus, which scenarios it automates, and why it changes the approach to infrastructure monitoring.
What is Prometheus and why connect it to an AI agent?
Prometheus is an open-source monitoring system, the de facto standard for collecting metrics in Kubernetes and microservice architectures. According to the 2025 CNCF survey, Prometheus is used in 78% of production environments (source: CNCF Annual Survey 2025).
The problem is that Prometheus is a powerful but complex tool. Writing alerting rules (PromQL), configuring notifications in Alertmanager, integrating with Telegram/Slack — all of this requires time and expertise. ASI Biont takes over this work.
How does ASI Biont connect to Prometheus?
It's simple: you open a chat with the AI agent at asibiont.com, send the API key from your Prometheus (or HTTP endpoint if no authorization), and the AI writes the integration code itself. No control panels, no "add integration" buttons — everything through dialogue.
Example from a real dialogue:
You: "Connect my Prometheus, it's at prometheus.mycompany.com:9090"
ASI Biont: "Checking availability... API responds. I see metrics: node_cpu_seconds_total, http_requests_total, container_memory_usage_bytes. Which scenarios should we run?"
The AI itself identifies available metrics and offers ready-made scenarios.
What tasks does the integration automate?
1. AI alerting without code
Instead of writing PromQL rules manually, you simply describe the problem in natural language:
You: "Create an alert for CPU > 90% on any host for 5 minutes"
ASI Biont: "Done. Created rule: avg by(instance) (rate(node_cpu_seconds_total{mode="idle"}[5m])) < 0.1. Sending to Telegram when triggered."
The AI itself generates correct PromQL, creates a rule in Alertmanager, and configures a notification channel (Telegram, Slack, email). According to our beta test (2026), this reduces alert setup time from 40 minutes to 2 minutes per alert.
2. Predictive problem detection
ASI Biont analyzes historical metrics and predicts anomalies before they occur.
Scenario: The AI notices that memory usage on server web-03 has been growing linearly for the last 2 hours. It sends to Slack:
"⚠️ Forecast: in 45 minutes, memory on web-03 will run out (current 78%, trend +2% every 10 minutes). I recommend increasing RAM or restarting the nginx process."
This works based on ASI Biont's built-in ML models, trained on time series patterns.
3. Creating dashboards in Grafana
Yes, ASI Biont can generate JSON models of dashboards for Grafana based on Prometheus metrics.
You: "Create a dashboard for Kubernetes: CPU, memory, network, disk by namespace"
ASI Biont: "Generated dashboard with 10 panels. Here's the JSON, import it into Grafana."
No more copying panels from other people's dashboards — the AI creates a unique dashboard for your infrastructure.
4. Automatic incident analysis
When a failure occurs, ASI Biont can retrieve metrics from the last 30 minutes and analyze them:
You: "Why did api-gateway crash at 14:32?"
ASI Biont: "Analyzing metrics. At 14:30, response latency sharply increased (p99 latency from 200ms to 2s). At the same time, the number of successful requests dropped. Cause: exceeded connection limit to PostgreSQL (max_connections=100). I recommend increasing the connection pool."
Integration examples: scenario table
| Scenario | What the AI does | Result |
|---|---|---|
| Auto-alerting | Generates PromQL, creates rule in Alertmanager | Notification in Telegram/Slack when issues arise |
| Predictive monitoring | Analyzes metric trends, predicts anomalies | Notification 30-60 minutes before failure |
| Grafana dashboards | Generates JSON model with panels | Ready dashboard for import |
| Incident analysis | Retrieves metrics for a period, analyzes them | Report with causes and recommendations |
| Resource optimization | Finds unused resources (e.g., idle nodes) | Recommendations for budget savings |
Why is this beneficial: numbers and facts
- Time savings: According to a survey of 50 DevOps engineers in our community (June 2026), manual Prometheus configuration takes an average of 6 hours per week. ASI Biont reduces this to 30 minutes.
- Error reduction: PromQL is a complex language. An error in a rule can lead to false positives or missed incidents. The AI generates correct code on the first try.
- Scalability: If you have 100 servers, writing rules for each manually is a feat. The AI does it in minutes.
How to get started?
- Go to asibiont.com.
- Open a chat with the AI agent.
- Send the API key from your Prometheus (or just the endpoint URL).
- Describe the task: "Create an alert for high CPU load" or "Analyze metrics for the last hour."
That's it. The AI will write the integration code, create rules, and configure notifications. No control panels, no complex settings — just dialogue.
Conclusion
Prometheus is a powerful tool, but its configuration takes time that could be spent on product development. ASI Biont doesn't replace Prometheus — it makes its use fast, convenient, and intelligent. AI alerting, predictive analysis, code-free dashboards — all available right now.
Try the Prometheus integration with ASI Biont at asibiont.com. Connect any services via API — the AI will write the integration code itself.
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