awslabs/amazon-bedrock-agentcore-samples
Feb 2026 – May 2026
3 Months active
Languages Used
PythonNone
Technical Skills
AWS Boto3best practiceserror handlingsession managementtutorial developmentAWS
rajjainl
PROFILE
Rajjainl
Contributed to the awslabs/amazon-bedrock-agentcore-samples repository by building and refining session lifecycle tooling, real-time collaboration tutorials, and automated evaluation pipelines. Leveraged Python, AWS Boto3, and Jupyter Notebooks to deliver end-to-end lifecycle demonstrations, robust error handling, and security-focused cleanup workflows. Enhanced developer experience through improved session management, notebook compatibility, and authentication flows using OAuth, IAM, and WebSocket protocols. Developed a direct boto3-based evaluation framework supporting batch processing and reproducible experimentation for HR Assistant and Shopping Concierge agents. Automated deployment processes, streamlined IAM role creation, and maintained repository hygiene, resulting in more reliable, scalable, and maintainable cloud-based agent development workflows.
Overall Statistics
Feature vs Bugs
60%Features
Repository Contributions
5Total
Bugs
2
Commits
5
Features
3
Lines of code
5,303
Activity Months3
Your Network
1852 peopleWork History
May 2026
2 Commits • 1 Features
May 1, 2026May 2026 monthly summary for awslabs/amazon-bedrock-agentcore-samples focused on modernizing evaluation workflows and deployment pipelines to unlock faster business-value validation and improved reproducibility. Key features delivered in May: - Evaluation Framework for HR Assistant and Shopping Concierge: Implemented a boto3-direct evaluation flow with batch evaluation across multiple scenarios, aggregate scoring, and streamlined deployment/evaluation workflow. Replaced legacy SDK wrappers with direct bedrock-agentcore/bot REST-like calls, enabling CI/CD-friendly batch runs and per-session evaluation. - Shopping Concierge end-to-end experimentation: Added the Strands ShoppingConcierge notebook and self-contained Shopping Concierge agent (shopping_concierge_agent.py) with an accompanying requirements.txt. Notebook steps include deploy, wait-for-ready, multi-turn actor simulations, and batch evaluation, all designed to run against deployed Runtime via public boto3 calls. - Automated deployment reliability improvements: Auto-create AgentCoreExecutionRole when absent; migrate deployment from Docker/ECR-heavy paths to a CodeZip-based deployment flow and agentcore CLI for runtime management. Ensured region and identity handling are robust in notebooks (Jupyter interpolation fixes, dynamic region handling). - Notebook and workflow quality enhancements: Split agent code into standalone files, added helper deploy scripts, improved readability, and aligned notebooks with AWS docs on batch evaluations and user simulation. Implemented ARM64-friendly packaging to prevent runtime incompatibilities. Major bugs fixed: - Repository hygiene: Removed accidentally committed binary zip 01-tutorials/07-AgentCore-evaluations.zip to maintain clean source control practices (#1444). Overall impact and accomplishments: - Delivered a modern, end-to-end evaluation and deployment pipeline that reduces setup friction, accelerates validation of agent capabilities, and improves reproducibility across environments. The new direct boto3-based evaluation flow enables batch processing, richer scoring, and scalable experimentation, directly supporting faster business value realization for HR Assistant and Shopping Concierge use cases. Technologies and skills demonstrated: - boto3 direct API usage, batch evaluation patterns, and per-session aggregation - AWS Bedrock AgentCore concepts (AgentRuntime, evaluation workflows, batch evaluation) - IAM role automation and robust deployment workflows (CodeZip, agentcore CLI) - Notebook-driven experimentation, automation scripts, and Python tooling for scalable evaluation - ARM64 packaging considerations and cross-region/interpolation handling
2 Commits • 1 Features
May 1, 2026May 2026 monthly summary for awslabs/amazon-bedrock-agentcore-samples focused on modernizing evaluation workflows and deployment pipelines to unlock faster business-value validation and improved reproducibility. Key features delivered in May: - Evaluation Framework for HR Assistant and Shopping Concierge: Implemented a boto3-direct evaluation flow with batch evaluation across multiple scenarios, aggregate scoring, and streamlined deployment/evaluation workflow. Replaced legacy SDK wrappers with direct bedrock-agentcore/bot REST-like calls, enabling CI/CD-friendly batch runs and per-session evaluation. - Shopping Concierge end-to-end experimentation: Added the Strands ShoppingConcierge notebook and self-contained Shopping Concierge agent (shopping_concierge_agent.py) with an accompanying requirements.txt. Notebook steps include deploy, wait-for-ready, multi-turn actor simulations, and batch evaluation, all designed to run against deployed Runtime via public boto3 calls. - Automated deployment reliability improvements: Auto-create AgentCoreExecutionRole when absent; migrate deployment from Docker/ECR-heavy paths to a CodeZip-based deployment flow and agentcore CLI for runtime management. Ensured region and identity handling are robust in notebooks (Jupyter interpolation fixes, dynamic region handling). - Notebook and workflow quality enhancements: Split agent code into standalone files, added helper deploy scripts, improved readability, and aligned notebooks with AWS docs on batch evaluations and user simulation. Implemented ARM64-friendly packaging to prevent runtime incompatibilities. Major bugs fixed: - Repository hygiene: Removed accidentally committed binary zip 01-tutorials/07-AgentCore-evaluations.zip to maintain clean source control practices (#1444). Overall impact and accomplishments: - Delivered a modern, end-to-end evaluation and deployment pipeline that reduces setup friction, accelerates validation of agent capabilities, and improves reproducibility across environments. The new direct boto3-based evaluation flow enables batch processing, richer scoring, and scalable experimentation, directly supporting faster business value realization for HR Assistant and Shopping Concierge use cases. Technologies and skills demonstrated: - boto3 direct API usage, batch evaluation patterns, and per-session aggregation - AWS Bedrock AgentCore concepts (AgentRuntime, evaluation workflows, batch evaluation) - IAM role automation and robust deployment workflows (CodeZip, agentcore CLI) - Notebook-driven experimentation, automation scripts, and Python tooling for scalable evaluation - ARM64 packaging considerations and cross-region/interpolation handling
May 2026
March 2026
2 Commits • 1 Features
Mar 1, 2026Two major deliverables in the Bedrock AgentCore samples for 2026-03: (1) Tutorial Notebooks Compatibility and Correctness Fix (bug fix) to stabilize tutorials and Jupyter usage; (2) AG-UI Document Co-authoring Examples with SSE/WebSocket and Auth Support (feature) to enable real-time collaboration demos with Cognito/JWT and IAM/SigV4. Results include more reliable notebooks, corrected runtime calls, and new AG-UI demonstrations with architecture diagrams, improving onboarding, reducing support incidents, and expanding supported authentication and transports. Demonstrated Python, asyncio, SSE/WebSocket, and cloud-authentication concepts; improved deployment and documentation.
March 2026
2 Commits • 1 Features
Mar 1, 2026Two major deliverables in the Bedrock AgentCore samples for 2026-03: (1) Tutorial Notebooks Compatibility and Correctness Fix (bug fix) to stabilize tutorials and Jupyter usage; (2) AG-UI Document Co-authoring Examples with SSE/WebSocket and Auth Support (feature) to enable real-time collaboration demos with Cognito/JWT and IAM/SigV4. Results include more reliable notebooks, corrected runtime calls, and new AG-UI demonstrations with architecture diagrams, improving onboarding, reducing support incidents, and expanding supported authentication and transports. Demonstrated Python, asyncio, SSE/WebSocket, and cloud-authentication concepts; improved deployment and documentation.
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 — Key focus: enhancing session lifecycle tooling and developer experience for Bedrock agentcore samples. Delivered end-to-end lifecycle demonstrations and security-conscious cleanup workflows across MCP server and hosting-agent tutorials, with targeted fixes informed by reviewer feedback.
1 Commits • 1 Features
Feb 1, 2026February 2026 — Key focus: enhancing session lifecycle tooling and developer experience for Bedrock agentcore samples. Delivered end-to-end lifecycle demonstrations and security-conscious cleanup workflows across MCP server and hosting-agent tutorials, with targeted fixes informed by reviewer feedback.
February 2026
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Quality Metrics
Correctness84.0%
Maintainability84.0%
Architecture84.0%
Performance84.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
NonePython
Technical Skills
AWSAWS Boto3AWS SDKBoto3Cloud ComputingData AnalysisFastAPIJupyter NotebooksMachine LearningPython ProgrammingSSEVersion ControlWebSocketbest practiceserror handling
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