How quickly your team identifies the issue, pulls the right data, and kicks off resolution can be the difference between a short-lived blip and a high-impact outage.
In most organizations, that still takes human effort. Engineers get paged, check multiple dashboards, hunt for the right playbook, and try to piece together what’s going on. And often, they’re doing it while half-asleep.
With LangChain and Gentoro, that response can be immediate and automated.
In this post, we walk through how to build an AI agent that monitors for incidents, analyzes the situation, alerts the right team, and creates a JIRA ticket—all in real time.
This AI agent is more than just a chatbot. It’s a full-fledged AI-powered production support system.
AI-powered incident response
The goal is simple: respond to incidents faster, with more context, and less effort from humans.
You need the AI agent to:
- Listen to Slack – It watches a dedicated channel for incident reports (manual or automated).
- Parse the incident – It retrieves the most recent runbook and matches the reported issue with known scenarios.
- Gather diagnostics – It fetches real-time monitoring data from Grafana to look for anomalies.
- Notify the team – It summarizes the issue and alerts the on-call devs via Slack.
- Create a JIRA ticket – It files a fully detailed issue with logs, diagnostics, and steps taken.
All of this is orchestrated by LangChain and powered by Gentoro.
Why this is a difficult manual process
It’s not hard to imagine what an AI agent should do in a situation like this. The hard part is getting it to work in practice.
Here’s what usually gets in the way:
- Data access: Pulling the right dashboards, parsing payloads, and turning unstructured logs into something actionable requires bespoke code.
- Tool orchestration: You need to write wrappers for each API and make them callable by your agent, with well-defined schemas and fallbacks.
- Authentication: Every tool—Slack, Grafana, JIRA—has its own auth model. You’ll need to manage API tokens, secrets, and refresh cycles.
- Security: Running all of this in a secure, auditable way across environments is a major lift.
That’s exactly what Gentoro handles for you.
How Gentoro simplifies this process
Gentoro acts as a connective layer between your agentic AI system and your enterprise systems.
With Gentoro, you define:
- Services: These are the systems you want your agent to interact with (e.g., Slack, Grafana, JIRA).
- Tools: These are the functions your agent can call (e.g., “get incident runbook,” “post to Slack,” “create JIRA ticket”). Gentoro can auto-generate these or let you define them with code or natural language.
- Bridges: These are collections of tools available to a given agent or use case. Once your tools are defined, your agent can dynamically call them using either Gentoro’s SDK or via MCP (a standard protocol we support).
That means you can update tools or add new ones without touching your agent’s core logic.
Building the Agent (step-by-step)
Let’s break down how the AI agent is built and what each component does.
1. Monitoring Slack for Incidents
The agent starts by watching a designated Slack channel (e.g., #incident-report). Any message that comes in is parsed as a potential issue.
We use a Gentoro tool to read messages from Slack, and the agent determines whether it should take action based on message content.
2. Retrieving the Runbook
Next, the agent fetches the most recent runbook—a structured document that maps common incident types to resolution steps.
This is where the reasoning starts. Using the incident message and runbook data, the LLM matches the issue to a known scenario.
In LangChain terms, this is a node that combines a tool call (get_runbook) with an LLM that uses that context to plan next steps.
3. Pulling Data from Grafana
If the runbook suggests collecting diagnostics, the agent calls another Gentoro tool to fetch data from Grafana. This might include CPU load, memory usage, or network traffic—whatever is relevant for the incident type.
The Gentoro tool is pre-configured to query specific dashboards or panels, based on what’s defined in the runbook.
The agent uses that data to confirm or rule out known issues—and to provide richer context in its alert.
4. Notifying the On-Call Team
Once it has a diagnosis or summary, the agent posts a message to the on-call team in Slack.
This message includes:
- The incident description
- Matching scenario from the runbook
- Any anomalies found in monitoring data
- Suggested next steps
Again, this is just a LangChain node calling a Gentoro tool (post_to_slack) with structured output from the LLM.
5. Creating a JIRA Ticket
Finally, the agent files a ticket in JIRA for tracking and resolution. This includes:
- A descriptive title
- Summary of the incident
- Detected anomalies
- Logs or metrics if available
- Runbook steps attempted
The ticket is tagged and assigned automatically based on rules defined in the runbook.
This is powered by another Gentoro tool (create_jira_issue) that handles the API interaction, credentials, and formatting.
Under the hood: LangChain + Gentoro architecture
This agent is built using LangGraph, a framework in the LangChain ecosystem for building multi-step agents using nodes and edges.
Each node can:
- Call a tool (via Gentoro)
- Use an LLM for reasoning
- Decide what to do next
With Gentoro, tool calls are abstracted into reusable units. You don’t need to implement the API logic inside your node—just call gentoro.runTool(...) with the name and input.
Even better: Gentoro provides introspection. Your agent can discover which tools are available at runtime, and dynamically decide what to call based on the context.
This architecture means you can add tools (e.g., send email, restart service, query logs) and your agent can start using them without code changes.
Why this matters for DevOps and SRE teams
Today, most teams rely on a human chain of response:
- Someone sees an alert
- Someone interprets it
- Someone takes action
That process is slow, inconsistent, and expensive. Even with great tooling, response times vary, context is lost, and issues escalate unnecessarily.
Using AI agents built with LangChain and Gentoro, that flow becomes:
- Agent detects incident
- Agent summarizes, diagnoses, and alerts
- Humans review and resolve if needed
That’s faster, cheaper, and more consistent.
Even if your agent doesn’t fully resolve the issue, it accelerates triage dramatically, giving your engineers the right context and next steps instantly.
And because Gentoro handles security, governance, and service access, you can safely deploy this agent in real environments without exposing credentials or building brittle code.
Build your own
Want to see it in action?
Let your AI agents reason. Let Gentoro do the rest.
