Connect Chronosphere to AI Agents: Automate Team Member Lookups

Connecting your observability platform to large language models allows site reliability engineering (SRE) and DevOps teams to execute workflows autonomously. If your team uses ChatGPT, check out our guide on connecting Chronosphere to ChatGPT, and if you are integrated with Anthropic's ecosystem, read our guide on connecting Chronosphere to Claude. In this technical walkthrough, we will demonstrate how to connect Chronosphere to AI Agents using Truto's /tools endpoint to automate team member lookups.

SREs frequently need to map services, alerts, and dashboards back to the human teams responsible for them. By providing your AI agents with raw, schema-defined API access to Chronosphere's team and user management endpoints, you can completely automate routing questions like "Who owns the payment gateway, and what are their emails?" without leaving your chat or incident management interface.

Engineering Reality: The Chronosphere API

Building agentic workflows on top of enterprise observability platforms comes with distinct architectural considerations. The Chronosphere API is designed for heavy programmatic configuration, which introduces a few specific integration challenges:

1. Nested Referencing via Slugs and IDs: Observability data models are highly relational. In Chronosphere, you will often find resources referencing a team slug instead of an absolute ID, while other administrative endpoints demand the internal unique ID. Your agent must bridge these namespaces.
2. Aggressive Pagination on High-Volume Objects: While you might only have a few dozen teams, the user and team membership queries can return paginated wrappers. Agents must be equipped to handle cursor-based navigation if team sizes scale beyond a single page.
3. Strict Control-Plane Rate Limiting: Chronosphere applies rate limits to its administrative endpoints to protect the system's core metric ingestion paths. This requires careful architectural planning for error handling, as agents in a tight reasoning loop can easily exhaust quota by repeatedly querying team structures.

Chronosphere Tool Inventory

Truto dynamically maps Chronosphere's REST endpoints into proxy tools that an LLM can understand. Truto handles the underlying authentication and query processing while delivering a clean JSON schema directly to your agent framework. This process relies on LLM function calling to translate natural language prompts into precise API requests.

Hero Tools

These are the core endpoints mapped by default for automating team and user lookups in Chronosphere.

list_all_chronosphere_teams

This tool retrieves all registered teams within your Chronosphere instance. It returns an array of team objects, exposing the team's name, slug, description, user_emails, created_at, and updated_at properties.

• Contextual usage notes: Agents should use this tool when a user asks a broad question about organizational structure, or when the agent needs to resolve a team name to an internal Chronosphere ID before running subsequent operations.
• Example prompt: "List all observability teams currently registered in Chronosphere and return their associated user emails."

get_single_chronosphere_team_by_id

This tool fetches detailed information for a specific Chronosphere team by its unique id. The response includes the name, slug, description, created_at, updated_at, and user_emails.

• Contextual usage notes: Use this tool when the agent already knows the specific team ID (perhaps obtained from an alert payload or a previous list call) and needs to isolate the exact membership or description without retrieving the entire organization's data.
• Example prompt: "Get the details, including user emails, for the Chronosphere team with ID 'team_98765'."

Full Inventory

Here is the complete inventory of additional Chronosphere tools available. For full schema details, visit the Chronosphere integration page.

• (Note: Truto's customizable architecture means any additional Chronosphere endpoint can be mapped via Truto Custom Resources and instantly exposed as a tool by adding a description in the UI.)

Workflows in Action

To illustrate how these tools function within a live environment, here are two realistic, persona-specific workflows.

Workflow 1: The SRE Incident Responder

During an active incident, an SRE needs to immediately identify and page the team responsible for a failing service. They interact with an AI agent hooked into their Slack channel.

"The checkout-service monitor is firing. Which team owns the 'checkout-backend' slug in Chronosphere, and what are their contact emails?"

Agent Execution Steps:

1. The agent calls list_all_chronosphere_teams to retrieve the entire directory of teams.
2. The agent parses the returned JSON, filtering the array for the team whose slug matches checkout-backend.
3. The agent extracts the user_emails array from that specific team object and formats a direct response.

Result: The user immediately receives a concise list of email addresses for the developers on call for the checkout backend, drastically reducing the mean time to resolution (MTTR) spent clicking through UI menus.

Workflow 2: The Security Administrator

A security administrator needs to ensure all Chronosphere access aligns with current organizational compliance standards, checking for stale or undocumented teams.

"Show me all Chronosphere teams that were created before January 2024 and do not have an active description."

Agent Execution Steps:

1. The agent executes list_all_chronosphere_teams to retrieve the full organizational graph.
2. The agent locally filters the resulting payload, isolating teams where created_at is prior to 2024-01-01 and the description field is either null or an empty string.
3. The agent formats the final audit list.

Result: The security admin receives a formatted audit report of non-compliant observability teams, allowing them to initiate cleanup operations immediately.

Building Multi-Step Workflows

To build an autonomous agent, you must bind these Truto-provided tools to an LLM framework like LangChain, Vercel AI SDK, or CrewAI. This is a critical step in architecting AI agents that can manage complex SaaS data models. Truto exposes these tools via a standardized endpoint: GET https://api.truto.one/integrated-account/<id>/tools.

Using the truto-langchainjs-toolset, you can initialize a TrutoToolManager to fetch schemas dynamically and bind them to your model.

import { TrutoToolManager } from 'truto-langchainjs-toolset';
import { ChatOpenAI } from '@langchain/openai';
import { AgentExecutor, createOpenAIToolsAgent } from 'langchain/agents';
 
// Initialize the Tool Manager with your Truto API key and Chronosphere account ID
const toolManager = new TrutoToolManager({
  trutoApiKey: process.env.TRUTO_API_KEY,
  integratedAccountId: 'chronosphere-account-id',
});
 
// Retrieve the list_all_chronosphere_teams and get_single_chronosphere_team_by_id tools
const tools = await toolManager.getTools();
 
// Initialize the LLM
const llm = new ChatOpenAI({
  modelName: 'gpt-4',
  temperature: 0,
});
 
// Bind tools directly to the model
const llmWithTools = llm.bindTools(tools);

Handling Truto Rate Limits and Error States

A critical component of multi-step AI workflows is error handling. Agents often execute tools in rapid succession, which can trigger rate limits on the upstream Chronosphere API.

It is vital to understand that Truto acts as a transparent proxy layer for these limits. Truto does not retry, throttle, or apply backoff on rate limit errors. When the upstream API returns an HTTP 429 Too Many Requests, Truto passes that exact error code directly back to the caller. Understanding how to handle third-party API rate limits is essential when an AI agent is processing high volumes of observability data.

To assist your agent in handling this, Truto normalizes the upstream rate limit information into standardized IETF headers:

• ratelimit-limit
• ratelimit-remaining
• ratelimit-reset

The caller (your agent framework or custom application code) is strictly responsible for implementing retry loops or exponential backoff. Your agent execution loop should inspect these headers, pause execution until the ratelimit-reset timestamp is reached, and then re-attempt the tool call.

// Pseudocode for handling 429s in a custom tool caller loop
try {
  const response = await fetchToolData();
} catch (error) {
  if (error.status === 429) {
    const resetTime = error.headers.get('ratelimit-reset');
    const delay = calculateDelay(resetTime);
    console.warn(`Rate limited. Agent pausing for ${delay}ms before retry.`);
    await sleep(delay);
    // Re-attempt tool call
  }
}

By feeding structured API access to your LLM and handling control-plane rate limits correctly, you can dramatically improve your team's operational efficiency. Rather than manually clicking through dashboards to audit access or find an on-call engineer, your SREs can converse directly with the infrastructure.