Grafana + ASI Biont: AI Agent for Infrastructure Monitoring Without Code and SQL

Grafana + ASI Biont: AI Agent for Infrastructure Monitoring Without Code and SQL

Introduction

Grafana is the de facto standard for metric visualization and infrastructure monitoring. According to the Cloud Native Computing Foundation's 2025 report, over 68% of organizations using containerization employ Grafana for dashboards and alerting. However, configuring Grafana is routine work: writing PromQL queries, configuring data sources, and designing dashboards. On average, a DevOps engineer spends up to 4-6 hours creating a single complex dashboard with alerts.

This is where ASI Biont comes in—an AI agent that connects to Grafana via its API and automates the entire cycle: from dashboard generation to alert configuration. In this article, we'll explore how the Grafana and ASI Biont integration reduces monitoring setup time by 70% and why it's becoming a DevOps trend in 2026.

What Is the Grafana and ASI Biont Integration?

The integration involves connecting the Grafana service to the ASI Biont AI agent via an API. Unlike traditional approaches where you wait for developers to add a ready-made connector, ASI Biont writes the integration code itself for any service's API. Users simply provide their Grafana API key in a chat with the AI agent. The entire connection process happens in a conversational mode: you describe the task, and the AI agent generates the code, tests it, and applies it.

How it works in practice:
1. The user obtains a Grafana API key (or access token).
2. In the chat with ASI Biont, they write: "Connect Grafana, create a dashboard for monitoring CPU and memory across all hosts."
3. The AI agent analyzes the Grafana API (documentation available at https://grafana.com/docs/grafana/latest/developers/http_api/), writes Python code using the requests library, sends requests to the API, creates the dashboard, and configures alerts.
4. The user receives the finished dashboard in Grafana, along with logs of what was done.

The entire process takes 2 to 5 minutes instead of 4-6 hours of manual work.

What Tasks Does This Integration Automate?

Details on each task:

1. Automated dashboard creation. You describe what you want to see: "a dashboard for monitoring PostgreSQL with metrics on active connections, query execution time, and database size." ASI Biont generates PromQL queries, creates panels (graphs, tables, stats), and arranges them.

2. Alert configuration. Manually, you have to create evaluation rules (e.g., "if CPU > 80% for 5 minutes, send a notification to Slack"). ASI Biont, based on your description, creates alerts with the required conditions, evaluation periods, and notification channels (via the Grafana API).

3. PromQL query generation. Even experienced engineers often spend time writing complex queries. ASI Biont understands natural language: "show the 95th percentile of response time over the last 7 days"—and generates correct PromQL.

4. Dashboard import and export. Need to move a dashboard from one Grafana instance to another? Just say: "export the 'Production Overview' dashboard to JSON and import it into the test environment."

Examples of Specific Use Cases

Scenario 1: DevOps Engineer Sets Up Monitoring for a New Microservice

Problem: The team is launching a new microservice on Kubernetes. They need a dashboard to track CPU, memory, request count, errors, and response time. Manually, this takes 4-6 hours of working with PromQL and configuring alerts.

Solution with ASI Biont: The engineer writes in the chat:

"Connect Grafana on the test instance (API key: ...). Create a dashboard for the 'order-service' service. Add panels: CPU usage (rate over 5 min), memory (RSS), HTTP 5xx error count, p95 response time. Configure an alert: if errors exceed 10 per minute for 2 minutes, send a notification to Slack."

After 4 minutes, the dashboard is ready. The AI agent also created the alert and tested it (logs show it sent a test notification).

Scenario 2: Automatic Panel Updates After Metric Changes

Problem: Metric names in Grafana have changed (e.g., after an exporter update). Manually updating all panels takes hours.

Solution: The user writes:

"In the 'Infrastructure Overview' dashboard, replace all references to the metric 'node_cpu_seconds_total' with 'node_cpu_seconds_total_new'."

The AI agent finds all panels using the old metric, updates the queries, and applies the changes. Execution time: 1-2 minutes.

Scenario 3: Generating Alerts Based on Log Analysis

Problem: Need to configure alerts based on data from Loki (logs) and Prometheus (metrics). Manually, this is complex and time-consuming.

Solution:

"Create an alert: if the 'payment-gateway' service logs show the message 'timeout' more than 5 times in 10 minutes, and simultaneously the HTTP 503 error count in Prometheus exceeds 1% of total requests, send a notification to PagerDuty."

ASI Biont creates two alerts (one based on Loki, the other on Prometheus) and links them via annotations.

Why Is This a Key DevOps Trend in 2026?

According to Gartner's "AI in DevOps" report (2025), by 2026, over 40% of organizations will use AI agents to automate monitoring and observability tasks. The main reasons:

1. DevOps specialist shortage. According to LinkedIn (2025), demand for DevOps engineers exceeds supply by 2.5 times. Automating routine tasks allows teams to do more with the same resources.

2. Speed of change. In 2026, the average release cycle in tech companies is 2-3 days. Manually configuring monitoring for each release becomes a bottleneck.

3. Error reduction. The AI agent generates code based on API documentation and best practices. It doesn't make typos in PromQL and doesn't forget about alerts.

How to Connect Grafana to ASI Biont?

The connection process is extremely simple and requires no technical skills:

1. Go to the settings of your Grafana instance (or the cloud version, Grafana Cloud).
2. Create an API key with admin rights (in Configuration > API Keys).
3. Open the chat with ASI Biont at asibiont.com.
4. Write: "Connect Grafana, here is my API key: . Server address: https://my-grafana.example.com."
5. The AI agent will verify the key, test the connection, and confirm successful connection.

After that, you can give any commands: from creating dashboards to configuring alerts. No control panels, "add integration" buttons, or long forms—everything is done through a chat dialogue.

Important: Connect Any Service via API

ASI Biont can connect to any service that has a REST API. You don't have to wait for developers to add a ready-made integration. Just provide the API key in the chat, and the AI agent will write the integration code for that specific service's API. This means you can automate work not only with Grafana but also with Prometheus, Datadog, New Relic, Zabbix, InfluxDB, and any other monitoring tools.

Conclusion

The Grafana and ASI Biont integration is not just a convenience but a strategic tool for DevOps teams in 2026. It reduces monitoring setup time by 70%, automates routine tasks like creating dashboards and alerts, and frees engineers for more important work—performance analysis and infrastructure optimization.

Try it yourself: connect your Grafana to ASI Biont at asibiont.com and see that monitoring can be fast, simple, and code-free.