---
title: AI-powered IT support automation with AmazonBedrock & AgenticWorkflows
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# AI-powered IT support automation with Amazon Bedrock & Agentic Workflows
Queueup transformed its IT support operations with an AI-powered, agentic automation platform built on AWS. By combining Amazon Bedrock with semantic search and durable workflows, the organization now resolves 42% of tickets automatically and reduced average handling time by 88%, without increasing headcount.
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## The Challenge
As Queueup’s customer base expanded, its L1 support team faced increasing operational pressure and scalability constraints.
### Key challenges included:
- **Manual triage overload**: Over 60% of L1 support time was spent on ticket classification and routing, equivalent to more than 12 full-time employees annually
- **Slow resolution times**: Common issues required 4+ hours on average to resolve
- **Fragmented knowledge**: Critical solutions were scattered across Confluence, Slack, email, and individual engineers’ expertise
- **Minimal knowledge reuse**: Less than 5% of historical resolutions were leveraged for new tickets
- **High turnover (40% annually)**: Driven by burnout from repetitive manual tasks
- **Scalability limitations**: 80–120 tickets per day without the ability to scale without proportional headcount growth
- **Inconsistent resolution quality**: Outcomes varied depending on engineer availability and experience
Queueup required an intelligent automation solution capable of learning from historical resolutions, delivering consistent high-quality responses, and scaling efficiently without linear cost increases.
## The Solution
CloudNation implemented an enterprise-grade agentic AI platform on AWS that autonomously processes and resolves IT support tickets through a structured six-step workflow.
### Core architecture
The solution combines fully managed AWS services with durable workflow orchestration:
- **Amazon Bedrock (Claude 3.5 Sonnet)**
Multi-step reasoning engine for structured problem extraction and solution validation, using EU inference profiles to meet compliance requirements
- **Amazon Titan Text Embeddings V1**
1,536-dimensional embeddings enabling cost-efficient semantic search
- **Amazon OpenSearch Service (k-NN)**
Vector database supporting semantic similarity search across 10,000+ historical resolutions
- **Temporal.io**
Deterministic workflow orchestration with built-in human-in-the-loop capabilities
- **AWS ECS Fargate**
Serverless container platform running four microservices (Jira webhook, Jira worker, Confluence worker, data wrangling service)
- **Amazon Aurora PostgreSQL (Multi-AZ)**
Persistence layer for workflow state management
- **Secure VPC Architecture**
Private subnets with VPC endpoints for Bedrock, S3, Secrets Manager, and Textract, eliminating NAT Gateway costs
Infrastructure was deployed using layered Terraform modules (000–030) and GitOps CI/CD via GitHub Actions with AWS OIDC.
## The six-step agentic workflow
Each ticket is processed autonomously through a structured reasoning pipeline:
1. **Preprocess**
Consolidates ticket description, comments, attachments, and metadata into a unified context
2. **Extract problem**
Claude analyzes the context and extracts the core technical issue using structured reasoning
3. **Search knowledge base**
RAG-powered semantic search across historical resolutions using Titan embeddings and OpenSearch
4. **Evaluate solution**
Multi-factor confidence scoring assessing relevance, similarity, and contextual alignment
5. **Post to Jira**
Automatically generates a formatted resolution comment with citations to source documentation
6. **Transition status**
- > 70% confidence → automatically resolve
- 40–70% confidence → provide AI-assisted suggestion
- <40% confidence → escalate to human support
## Results within 90 days
The implementation exceeded all predefined KPIs and delivered measurable business value shortly after going live.
### Operational impact
- **42% fully automated ticket resolution** (target: 40%)
- **31% AI-assisted recommendations**
- **88% reduction in handling time** (4.2 hours → 28 minutes)
- **87% knowledge reuse rate** (up from <5%)
### Business impact
- Avoided additional L1 support hires despite growing ticket volume
- Increased first-contact resolution
- Expanded support capacity without increasing headcount
### Technical performance
- **Zero hallucination incidents in production** following implementation of multi-factor confidence scoring and conservative thresholds
- System handles 3× peak business-hour load with auto-scaling
- 15 CloudWatch alarms monitoring latency, error rates, and cost
## Total cost of ownership (3-year analysis)
A comprehensive three-year TCO analysis compared AWS infrastructure costs against realized business value.
### Cost optimization strategies
- **VPC Endpoints**: 50–80% reduction in data transfer costs; S3 Gateway Endpoint is free
- **ECS right-sizing**: 40% compute savings through iterative load testing
- **Bedrock serverless model**: $350–$750 per month savings compared to self-managed SageMaker endpoints
- **Scale-to-Zero services**: Idle workloads automatically scale to zero, eliminating unnecessary spend
The serverless-first architecture ensures costs scale proportionally with usage.
## Lessons learned
### Strategic technology decisions
- **Bedrock vs. Self-Hosted LLMs**
Eliminated ML infrastructure management overhead while maintaining compliance and cost flexibility
- **OpenSearch vs. Qdrant**
Reduced operational complexity with managed service, IAM integration, and combined full-text and vector search
- **ECS Fargate vs. EKS**
Approximately 50% cost savings for burst-heavy workloads due to pay-per-task-second pricing
### Critical success factors
- Conservative confidence thresholds (initially 80%, gradually lowered to 70%)
- Human-in-the-loop design to maintain quality and build internal trust
- Comprehensive observability and monitoring from day one
### Best practices demonstrated
- Responsible AI with full audit trails via Temporal
- Zero static credentials (IAM roles + OIDC)
- Private networking and least-privilege IAM policies
- 100% Infrastructure as Code (Terraform)
- Extensive automated testing (100+ unit tests)
## Recommendations for future implementations
- Begin with a pilot focused on low-risk ticket categories
- Implement cost monitoring and alerting early, LLM context expansion can significantly impact spend
- Combine AI autonomy with structured human oversight
##### **AI support automation **
## Explore what AI automation could do for your support team
[Schedule an AI automation assessment ](https://www.cloudnation.nl/en/services/cloud-migration)
## More success stories
[
Azure
## Greenwheels accelerates because of migration to Azure cloud
Read more
](https://www.cloudnation.nl/en/success-stories/greenwheels) [
aws
## Financial start-up VIVE can't do without the cloud
Read more
](https://www.cloudnation.nl/en/success-stories/vive) [
app migration
## How we migrated the CubicEyes app to AWS
Read more
](https://www.cloudnation.nl/succesverhalen/how-we-migrated-the-cubiceyes-app-to-aws/)
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