AI Agent Evaluation and Release Gates on Amazon Bedrock by Dedicatted

Product and platform teams in Canada, the United States and Europe running AI agents, assistants or document pipelines on Amazon Bedrock, often with several prompt or model changes a month, and without a shared definition of a good answer. The buyer is the Head of AI, VP Engineering or product owner of the assistant.

Every prompt tweak or model upgrade is a guess. Without an agreed evaluation set, teams check a few examples by hand, regressions reach customers, and fabricated or low-confidence outputs flow into downstream systems. Cost per request changes silently with each model swap.

Evaluation set and quality definition. In two weeks we turn real user questions and past failures into an evaluation set, agree what a good answer is for each type, and choose graders: exact checks, rule-based checks and model-based grading with Amazon Bedrock evaluations or Amazon Bedrock AgentCore Evaluations, with custom evaluators where data must stay in Canada.

Release gate. Graded test suites run in your CI/CD pipeline on every prompt, model or tool change, with thresholds that block a release when quality drops below the previous version.

Production monitoring. A sample of live traffic is scored continuously, with alerts on quality, refusals, latency and cost per request, and low-confidence outputs routed to human review before they reach downstream systems.

Refresh. The evaluation set grows from new failures each quarter, so the gate keeps testing what users actually ask.

For security testing of agents, see AI Agent Security Hardening on Amazon Bedrock AgentCore by Dedicatted; for running releases as a monthly service, see Managed AI Platform Operations on AWS by Dedicatted.

Amazon Bedrock (model evaluation, Guardrails), Amazon Bedrock AgentCore (Evaluations, Observability), AWS Lambda, AWS Step Functions, Amazon S3, Amazon CloudWatch, AWS CodePipeline or your own CI system, and Amazon SageMaker AI where custom classifiers are needed.

• A shared definition of a good answer, written as tests.
• No release ships with a lower score than the version before it.
• Quality, latency and cost drift in production raise alerts, not complaints.
• Low-confidence outputs go to a person before they reach downstream systems.

Dedicatted holds the AWS AI Services Competency. Evaluation and quality gates run in production in agents we built: a call-outcome classifier and evaluation pipeline for the healthcare voice agents we operate for a New York healthcare technology company, and claims agents for a travel insurance administrator where every decision carries a confidence score and the policy clause it relied on, so an adjudicator can override it. Both are published as case studies on our AWS Partner Solutions Finder profile.

Dedicatted is an AWS Premier Tier Services Partner and AWS Managed Service Provider headquartered in Toronto and Montreal, Canada. It is the first Canadian partner to hold both AWS AI Competencies (Generative AI and Agentic AI) and the first and only Canadian AWS Partner to hold the AWS Digital Sovereignty Services Competency. We deliver across Canada, the United States and Europe, in English and French, and support Canadian data residency in the AWS Canada (Central) and Canada West (Calgary) Regions.

AWS bills you for model usage during evaluation runs and production sampling, AgentCore Evaluations, Lambda, Step Functions, S3 and CloudWatch. Evaluation runs on every release consume model usage at AWS prices. We work through IAM roles you create.