A global organisation in the contact centre domain sought to improve the safety, reliability, and governance of its AI-powered customer support system. Using AgentRAI, the team implemented an intelligent orchestration framework with built-in responsible AI practices. The solution integrates Amazon Comprehend and Amazon SageMaker Clarify for content analysis and bias detection, and Amazon CloudWatch with AWS X-Ray for observability and traceability. The result was a safer, more transparent AI system that met compliance standards and offered enhanced user trust.
Need to implement AI-powered agents for customer interactions
Ensure reliable, consistent performance under varied input conditions
Detect and block harmful or manipulative content during AI-agent interactions
Meet safety and compliance standards for AI usage in public-facing services
Lack of observability and trust in AI model decision paths
Integrating responsible AI features without access to internal APIs
Implementing real-time content moderation with minimal latency
Observing AI model behaviour across complex decision chains
Ensuring safety at both the input and output stages of the AI pipeline
Tracking and visualizing interventions and content violations
The AgentRAI-powered architecture included three agents:
Orchestrator Agent: Handled incoming queries, identified intents, and routed requests.
Specialist Agent: Executed task plans and coordinated the GUI interface.
GUI Agent: Performed human-like actions (mouse clicks, typing) in legacy systems.
AgentRAI was integrated with Amazon Comprehend for input/output sentiment and content safety analysis, and Amazon SageMaker Clarify for bias and explainability. Amazon CloudWatch Logs and AWS X-Ray powered Observability.
Amazon SageMaker Clarify: Used to detect model bias and ensure explainability across the model lifecycle.
Amazon Comprehend: Analyzed incoming and outgoing content to identify harmful language or manipulative instructions.
Amazon CloudWatch: Monitored system events and safety interventions.
AWS X-Ray: Captured full execution traces of model decisions.
AWS Lambda: Automated filtering and escalation processes based on content severity.
Amazon S3: Stored safety logs and flagged interaction datasets.
Created Amazon Comprehend pipelines to classify and flag harmful inputs and outputs.
Integrated Amazon SageMaker Clarify for bias detection and interpretability.
Configured Lambda functions to trigger redaction or escalation based on detection severity.
Used CloudWatch to track real-time safety metrics and log safety interventions.
Integrated AWS X-Ray for tracing decision execution paths.
Created dashboards using Amazon QuickSight to visualize intervention trends and content category breakdowns.
Tested sample cases for blocked content and response validation.
Input and output stage safety checkpoints using Amazon Comprehend and Lambda automation.
Bias detection with SageMaker Clarify across the full model lifecycle.
Tracing and metrics collection using CloudWatch and X-Ray.
Visualization and insights via Amazon QuickSight dashboards.
Improved customer trust through visible safety checks and transparent AI decisions.
Lowered risk of delivering unsafe content with real-time filtering and intervention logs.
Enabled compliance monitoring through centralized dashboards.
Reduced response time without compromising safety controls.
Full traceability of AI decisions and actions using AWS X-Ray.
Automated intervention flow using Lambda and Comprehend.
Categorized logging and reporting with CloudWatch and S3.
Improved AI reliability through Clarify-based quality checks.
"The AgentRAI approach brought transparency and control into our AI operations. We now catch unsafe content before it reaches users and have real-time insights into how decisions are made."
— Responsible AI Lead, Global Contact Centre
Balancing model transparency with response performance.
Configuring scalable safety pipelines for real-time moderation.
Calibrating thresholds to reduce false positives while ensuring safety.
Embed safety filters at both pre- and post-processing stages.
Use AWS observability tools to gain operational visibility into AI decisions.
Align AI model behaviour with defined escalation protocols.
Expand responsible AI coverage across additional use cases and languages.
Integrate additional SageMaker features for advanced model diagnostics.
Extend dashboards for broader compliance and executive reporting.