New workflow submission

- Description (introBox.text)- should be a 300-500 word explanation of how the pattern works.
The Parallel Agent Early Stopping pattern uses AWS Step Functions and Amazon Bedrock to run multiple AI agents simultaneously on the same problem, with different approaches, and automatically terminates unnecessary processes once a high-confidence solution is discovered. The workflow coordinates Worker Agents that either retrieve information from the AWS Documentation MCP Server or generate responses using Amazon Bedrock models, while an Evaluation Agent continuously assesses confidence levels and triggers early stopping when a predetermined threshold is met. This design optimizes both performance and cost through parallel exploration, intelligent termination, and resource optimization techniques including agent tiering, token optimization, and Lambda memory tuning.

- Simplicity: `2 - Pattern`
- Diagram: This must link to an Exported PNG of the workflow that shows any service integrations, you can export this from Workflow studio.
https://serverlessland.com/workflows/early-stopping-sf-bedrock/resources/MainWorkflow.png

- Type: `Standard`
- Resources should link to AWS documentation and AWS blogs related to the post (1-5 maximum).
-https://aws.amazon.com/step-functions/
https://aws.amazon.com/bedrock/
https://docs.aws.amazon.com/prescriptive-guidance/latest/cloud-design-patterns/circuit-breaker.html

- Framework: SAM
- Author bio may include a LinkedIn and/or Twitter reference and a 1-sentence bio.
Satya Vedamtam is senior solutions architect supporting US federal customers and an avid AWS serverless technologies enthusiast. www.linkedin.com/in/svedamtam

Review existing workflow entries in the repository to find the submission location and required structure. Use the issue's workflow diagram URL and AWS reference links as the content requirements; done means the Parallel Agent Early Stopping workflow includes its explanation, diagram, resources, SAM framework, and author bio.

Written by the indexing model from the issue text.