Growth has a funny way of exposing weaknesses in systems that once felt perfectly adequate.

That truth became very real for one of our customers, a rapidly growing North American Activewear Company. On the surface, everything looked like a success story, but inside its contact center, a different reality was taking shape. 

Each surge in demand triggered spikes across calls, chat, and email. Human agents were stretched thin, visibility across customer interactions broke down, and routine questions became harder to resolve quickly. It became clear that the contact center was not built to scale at the same pace as the business.  

These gaps were most visible during high-pressure moments like Black Friday, which ultimately led the team to turn to Cloudelligent for help. We partnered with their leadership to transition operations to Amazon Connect, aligning the move with AWS’s latest Agentic AI direction and innovations shared at re:Invent 2025. The result was a contact center built to scale, designed for resilience, and powered by AI that could support agents rather than replace them. 

In this blog, we’ll take you through how this Activewear Company’s contact center evolved from a pain point into a system that could grow confidently alongside the business. 

Scaling Customer Support for a High-Growth Activewear Company with Amazon Connect  

The Activewear Company is a fast-growing ecommerce and retail brand that designs and sells premium athletic apparel for a global customer base. It’s known for its frequent product launches, seasonal campaigns, and a strong digital-first sales strategy that drives high volumes of customer interactions across channels. 
 
Now let’s take a closer look at what their contact center needed to achieve as the business continued to scale. 

The Challenge: Managing Peak Demand Without Losing Efficiency 

As the company grew, its contact center found itself operating across a fragmented system landscape that was never designed to work as one cohesive experience. 

On any given interaction, human agents had to move between multiple tools, which created more friction than flow: 

• Disconnected Core Systems: Order fulfillment, inventory, CRM, and customer history data all lived in separate platforms. Instead of having one clear view, human agents had to stitch together context on the fly. 
• Complex Returns and Exchange Workflows: Returns and exchanges required multiple handoffs and system checks, adding unnecessary steps to even simple customer requests. 
• Scattered Knowledge Sources: Policies, promotions, and FAQs sat in separate knowledge bases which slowed down real-time reference during live interactions. 
• Seasonal Promotion Surges: Flash sales, product drops, and seasonal campaigns triggered sudden spikes in inquiries. With agents already working across disconnected systems, the surge was hard to keep up with. 
• Operational Strain on Agents: Context switching led to slower resolutions. Higher cognitive load, inconsistent responses across channels, and heightened stress at exactly the moments customers needed clarity and reassurance most. 

At the same time, customer expectations continued to rise. Shoppers expected fast responses, seamless handoffs, and accurate information across every touchpoint. Across calls, chats, and emails, customers expected reliable service and consistent experience everywhere, regardless of interacting with an AI assistant or a human agent. 

As you can imagine, the team at this Activewear brand was exhausted. They were running a world-class fashion label, but their backend felt like it was held together with digital duct tape. By the time the team came to Cloudelligent, it was clear something had to change. That challenge is exactly the kind that energizes us.  

Our team leaned in, put our thinking caps on, and started mapping what a contact center built for growth should really look like. It quickly became clear that Amazon Connect was the perfect fit. Migrating their contact center to AWS mattered, but doing it at the right moment made all the difference. With the ink barely dry on the incredible AWS re:Invent 2025 announcements, we saw a massive opportunity to bake their new Agentic AI capabilities right into their foundation. 

Ready to Adopt Agentic AI in Your Contact Center? 

If this sounds familiar, you’re not alone. We see these challenges a lot, and we use a simple readiness checklist with our customers who are thinking about bringing Agentic AI into their own contact center operations. It gives a clear view of what’s already in place and what might need a bit more work before going all in. 
 
We thought it’d be a great idea to share it with you too. 
 
Download the Agentic AI Readiness Checklist for Contact Centers to see if your operation is ready to move beyond experimentation and adopt Agentic AI at scale. 

Our Solution: Redesigning the Contact Center for Adaptive Scale 

To help the activewear company grow without losing efficiency or hurting the customer experience, we reimagined their contact center. At the heart of the new setup was Amazon Connect, fully integrated with Jira and HubSpot, and brought to life with Agentic AI orchestration. 

Our approach focused on eliminating fragmentation, reducing agent cognitive load, and enabling the contact center to handle demand spikes without adding operational complexity. 

Below is the step-by-step process we executed.

Step 1: Migrate Legacy Contact Center Systems to Amazon Connect 

Before redesigning workflows and introducing Agentic AI, we first migrated the company’s legacy on-premises contact center systems to Amazon Connect. This created a modern, cloud-native foundation that could scale elastically and integrate seamlessly with AI services and enterprise tools. 

We handled the migration end-to-end to minimize risk and downtime, which ensures a smooth transition for both agents and customers. The migration was executed in phases to ensure continuity of customer support and minimize operational disruption. Their team gained browser-based access to Amazon Connect which enabled secure distributed and remote support operations. 

Outcome: 
A modern, cloud-native contact center platform that reduced infrastructure complexity and set the foundation for AI-driven redesign and long-term scalability.

Step 2: Unify Channels and Routing with Amazon Connect 

We started by consolidating voice, chat, and digital messaging into a single Amazon Connect environment, while aligning email workflows with the unified CX architecture. This ensured that every customer interaction (via phone, chat, or messaging) was routed through a unified contact flow. 

To accelerate foundational capabilities, we leveraged Amazon Connect’s out-of-the-box AI agents for common contact center workflows, such as self-service and agent assistance. These agents helped establish baseline automation and knowledge access, while giving us the flexibility to customize behavior for the company’s policies and operational processes. 

Figure 1: Pre-built AI Agents in Amazon Connect

By centralizing routing, queue management, and agent profiles, we created a consistent interaction model across channels. Their customers no longer faced discrepancies when switching between communication methods, and agents could work confidently within a single, unified framework. 

Outcome: 
A consistent, omnichannel experience with standardized routing logic and visibility across all customer touchpoints. 

Step 3: Build an Orchestration AI Agent for Coordinating Complex Workflows 

Next, we deployed an orchestration AI Agent within Amazon Connect to act as the first responder and real-time assistant for customer interactions. Unlike traditional scripted chatbots, this agent was built to reason, plan, and execute workflows dynamically across systems. 

Figure 2: Orchestration AI Agent in Amazon Connect

The AI Agent handled tasks such as: 

• Understanding customer intent and asking clarifying questions 
• Deciding which enterprise systems to query in real time 
• Executing actions like order lookups, ticket creation, and status updates 
• Automating order tracking, return eligibility checks, inventory queries, and knowledge base retrieval 

All queries were executed through secure API integrations and middleware layers, ensuring governed access to backend systems. Instead of manually stitching together data mid-conversation, agents received a consolidated, contextual summary with recommended next steps surfaced directly in their workflow. 

Outcome: 
Faster first-contact resolution, lower handle times, reduced agent fatigue, and more accurate, consistent responses during peak demand. Customers can get complex issues resolved from start to finish, without being transferred between departments or put on hold.

Step 4: Register MCP Servers via Amazon Bedrock AgentCore Gateway 

Once the orchestration layer was in place, the next priority was enabling secure, standardized access to their current systems. We used Model Context Protocol (MCP) through Amazon Bedrock AgentCore Gateway to give AI Agents a consistent way to discover and invoke tools across our client’s tech stack. 

Instead of building custom integrations for every system, MCP allowed us to connect once and scale automation everywhere. 

Figure 3: AI Agent Tooling

We used MCP to: 

• Standardize Tool Access: Registered MCP servers to expose APIs, AWS Lambda functions, and remote services as reusable tools for AI Agents. 
• Integrate Enterprise Platforms: Connected Jira for incident and case management and HubSpot (CRM) for customer profiles and lifecycle data. 
• Trigger Real Workflows: Enabled AI Agents to retrieve customer context, create tickets, and automate operational workflows in real time. 

We also leveraged Amazon Connect’s native MCP capabilities, which provide built-in access to Amazon Connect, Customer Profiles, Cases, and Assistant APIs. This allowed first-party contact center tools to be used immediately without additional configuration, which accelerated time to value while maintaining governance. 

Outcome: 
A standardized, secure tool orchestration layer that gave the AI Agents controlled access to the Activewear Company’s systems. This reduced integration complexity and enabled real-time automation across support workflows.

Step 5: Implement Custom Business Logic for AI-Driven Automation 

Next, we turned our focus to embedding the company’s real business processes directly into Amazon Connect Flows. These flows acted as reusable, parameterized modules that AI Agents could invoke as tools which enabled automation while preserving existing operational guardrails. 

We designed custom logic modules around the company’s biggest friction points: 

• Unified Order and Customer Context Logic: Aggregated order, inventory, CRM, and customer history data into a single real-time view for both AI and human agents. 
• Returns and Exchange Decision Logic: Encoded policy rules for returns, exchanges, refunds, and goodwill credits to enable instant eligibility checks. 
• Policy and Promotion Knowledge Logic: Structured active promotions, seasonal policies, and FAQs into queryable logic to ensure consistent, approved responses across channels. 
• Peak Demand Handling Logic: Prioritized high-impact inquiries and automated repetitive requests during flash sales and seasonal surges to reduce queue pressure and agent overload. 

By encapsulating these workflows as callable tools, the AI Agents could execute real business processes programmatically while maintaining governance and operational consistency. 

Outcome: 
Reusable, governed automation logic that reduced system fragmentation, improved resolution consistency, and lowered agent cognitive load during high-demand periods.

Step 6: Enforce Fine-Grained Security and Governance for AI Actions 

With AI agents executing real business workflows, security and governance became a critical priority. We needed automation that could scale without compromising compliance, data integrity, or operational control.  

We implemented fine-grained security controls across tools and workflows using Amazon Connect Security Profiles and Amazon Bedrock AgentCore capabilities. 

Key security controls implemented: 

• Controlled Tool Invocation: We restricted which tools each AI persona could access (e.g., support vs. operations), ensuring agents only executed actions aligned with their role and permissions. 
• Input and Output Guardrails: JSON path filters were applied to override sensitive inputs. Output filtering ensured only relevant data was returned, which prevented unnecessary exposure of customer or operational information. 
• User Confirmation for Sensitive Actions: High-risk actions such as refunds, account changes, or goodwill credits required explicit confirmation before execution. 
• Action-Level Governance via Security Profiles: Security Profiles defined what actions agents could trigger. This created clear decision boundaries between automation and human approval workflows. 

These guardrails ensured AI Agents operated safely and consistently, even during flash sales and seasonal demand spikes. 

Outcome: 
Enterprise-grade governance that protected customer data, prevented unauthorized actions, and enabled secure AI-driven automation at scale.

Step 7: Scale for Seasonal Peaks with Real-Time AI-Powered Voice and Chat 

We enabled real-time chat experiences and improved voice interactions to feel more responsive and natural. This helped the Activewear Company’s customers receive faster and more responsive chat interactions. 

During flash sales and product drops, AI handled the surge of repetitive inquiries, while human agents focused on complex, high-value interactions. 

Outcome: 
Elastic scaling while reducing reliance on temporary staffing during peak periods.

Step 8: Implement Observability and Continuous Optimization 

Using Amazon Connect’s in-built monitoring features, we instrumented the system to track: 

• AI resolution rate vs. escalation rate 
• Average handle time and first-contact resolution 
• Tool failure rates and fallback scenarios 
• Customer satisfaction signals 

These insights allowed continuous tuning of AI prompts, workflows, and escalation policies. 

Outcome: 
A continuously improving contact center that evolves with customer behavior and business growth. 

Impact: Scaling with Confidence, Not Complexity 

Redesigning the contact center was only the first step. Proving it could perform under real-world pressure was the real test. With Amazon Connect live, the Activewear Company finally saw how a unified, AI-powered contact center performed at scale. 

Figure 4: Contact Center Operations Before vs. After Amazon Connect Integration

What Changed in Practice 

Once everything went live, the difference was clear as day. Their team finally had a single, AI-assisted workflow that made interactions faster, easier, and more consistent, even during high-pressure scenarios. The impact showed up across every part of their support operations: 

• AI-Guided Resolution and Faster Outcomes: The assistant recommended policy-aligned actions such as refunds, goodwill credits, and billing adjustments. As a result, guesswork decreased, escalations dropped, and customer resolutions accelerated. 

Figure 5: AI Agent Assistance for Shipping Delays

• Instant Customer and Order Context: AI Agents surfaced customer history, order details, and policy references at the start of every interaction. Agents no longer had to switch systems, which significantly reduced search time. 
• Automated Ticket Creation and Routing: Cases were created and routed in Jira with structured summaries. This ensured faster, standardized issue handling and smoother handoffs across teams. 
• Consistent Answers Across Voice and Chat: The same policies and context powered every channel. Customers received uniform, reliable responses even during peak periods. 
• Scalable Operations Without Overstaffing: AI absorbed repetitive inquiries and surfaced recurring issue patterns in real time. These insights aligned support, operations, and leadership around shared, data-driven decisions. 

Turn Contact Center Complexity into Measurable CX Outcomes with Cloudelligent 

This was one of those projects that reminded us why we love building with AWS. The results were exciting, and the broader takeaway was even more compelling. Contact centers are quickly becoming intelligent systems that connect every conversation to real operational workflows. 
 
Watching Amazon Connect and its Agentic AI capabilities perform at scale made this transformation feel real. Orchestration agents, secure integrations, and real-time insights turned support interactions into coordinated, data-driven operations. 

If you are thinking about bringing Agentic AI into your contact center, we are here to help. At Cloudelligent, we enable you to build AI-powered contact centers on AWS that actually work in production. From architecture and integrations to governance and optimization, we help teams move from experimentation to real-world scale with confidence.  

Ready to move beyond pilots? Book a FREE Agentic AI Assessment with our experts to map high-impact use cases, define guardrails, and create a clear roadmap to production.

Your Frequently Asked Questions Answered!

1. How can Agentic AI improve contact center operations? 

Agentic AI makes life easier for contact center teams. It handles repetitive, time-consuming tasks like pulling data from different systems or creating tickets so agents can focus on helping customers. It guides them step by step through complex workflows, reduces context switching, and ensures responses are accurate and consistent. That means less stress, fewer mistakes, faster resolutions, and a smoother day-to-day for agents even during the busiest times. 

2. Which AWS services can modernize a contact center? 

Amazon Connect is the core AWS service for cloud contact centers. Combined with Amazon Bedrock AgentCore, it enables AI-driven automation and smarter customer support workflows. Cloudelligent helps organizations plan, migrate, and implement these services to build scalable, AI-powered contact centers on AWS. 

3. What are the latest Agentic AI features in Amazon Connect? 

Here are the eight new features:  

• AI Agent Assistance: Real-time help for human agents during customer calls.  
• AI Agent Observability: Tools to monitor agent performance and behavior.  
• AI Case Summarization: Automated generation of concise interaction summaries.  
• Flow Modules as LLM Tools: Using existing contact center logic as tools for AI Agents.  
• MCP Tool Support: New support for Management Console Portals (MCP) tools.  
• Nova Sonic Speech-to-Speech Integration: Natural, adaptive voice interactions that match customer tone.  
• Real-time Agentic Recommendations: Automatic suggestions for the agent’s next best step.  
• Self-Service Evaluation: Tools to systematically measure the quality of AI-driven self-service. 

4. What are the benefits of adopting Agentic AI in contact centers? 

Agentic AI helps contact center teams work smarter, not harder. It speeds up resolutions, reduces agent workload, and keeps responses consistent across channels. It can automate backend workflows and handle surges in demand without needing extra temporary staff. Teams get less stress, smoother days, and more time to focus on customers instead of juggling systems and repetitive tasks. 

5. Is Agentic AI secure for customer support environments? 

Yes, when implemented with proper governance. AWS offers role-based access controls, security profiles, audit logs, and approval workflows to keep AI actions in check. These safeguards protect customer data, prevent unauthorized actions, and ensure AI Agents operate within defined enterprise boundaries.