Automating IT Operations with Amazon Bedrock Agents
By Braincuber Team
Published on February 11, 2026
IT operations teams are drowning in tickets. From routine password resets to complex infrastructure change requests, the volume of manual validation required can paralyze innovation. What if you could deploy a "swarm" of AI agents to validate, categorize, and even resolve these requests automatically?
In this guide, we'll build an intelligent IT Operations Assistant using Amazon Bedrock Agents. We'll follow the journey of "NexusStream", a logistics company that automated their Change Management process, reducing ticket resolution time by 60%.
Why Bedrock Agents for IT Ops?
- Contextual Understanding: Unlike rigid scripts, Bedrock Agents understand the intent behind a sloppy ticket description.
- Action Execution: They don't just chat; they execute API calls to check server status or query databases.
- Orchestration: You can chain multiple agents (e.g., a "Validator" agent and a "Resolver" agent).
Step 1: Designing the Agent Action Group
First, we define what our agent can do. In Bedrock, this is an Action Group backed by an OpenAPI schema and a Lambda function. Our agent needs to: 1. Validate a Change Request (CR) ID. 2. Check the status of the affected Configuration Item (CI).
{
"openapi": "3.0.0",
"info": {
"title": "ITOpsActionGroup",
"version": "1.0.0"
},
"paths": {
"/validate-cr/{crId}": {
"get": {
"summary": "Validates a Change Request ID and checks for mandatory fields",
"operationId": "validateChangeRequest",
"parameters": [{
"name": "crId",
"in": "path",
"required": true,
"schema": { "type": "string" }
}],
"responses": {
"200": { "description": "Validation result" }
}
}
}
}
}Step 2: The Lambda Executor
The schema tells the agent how to call the function; the Lambda performs the logic. Here, we simulate checking against a ServiceNow or Jira database.
import json
def lambda_handler(event, context):
agent = event['agent']
actionGroup = event['actionGroup']
function = event['function']
parameters = event.get('parameters', [])
response_body = {}
if function == 'validateChangeRequest':
cr_id = next((p['value'] for p in parameters if p['name'] == 'crId'), None)
# Simulated DB Lookup
if cr_id == 'CR-1001':
response_body = {
"status": "INVALID",
"missing_fields": ["Risk_Assessment", "Rollback_Plan"],
"message": "CR-1001 is missing critical compliance data."
}
else:
response_body = {
"status": "VALID",
"message": "Change Request is ready for approval."
}
action_response = {
'actionGroup': actionGroup,
'function': function,
'functionResponse': {
'responseBody': { 'TEXT': { 'body': json.dumps(response_body) } }
}
}
return {'response': action_response}Step 3: Creating the "Swarm" Instructions
The magic lies in the Agent Instructions. This is the system prompt that guides the LLM to behave like a seasoned IT Manager.
Agent System Prompt
"You are an expert IT Operations Manager for NexusStream. Your goal is to validate incoming Change Requests (CRs).
1. When a user provides a CR ID, ALWAYS call the 'validateChangeRequest' function.
2. If the CR is invalid, explain EXACTLY which fields are missing in a polite, professional tone.
3. If valid, suggest the next available deployment window (assume Tuesdays and Thursdays 2 AM EST)."
Conclusion
By implementing this architecture, NexusStream shifted from manual checklist validation to an AI-driven "Quality Gate." The Bedrock Agent catches 90% of incomplete requests instantly, allowing human engineers to focus on complex deployment strategies rather than form-filling.
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