AWS Machine Learning
·
4 days ago
Smartsheet built a remote Model Context Protocol server on AWS that allows AI agents to access Smartsheet data and capabilities through natural language interfaces and autonomous workflows. The infrastructure uses AWS Fargate, Kinesis, Neptune, and Bedrock, with optimizations that have saved over 3 billion tokens through techniques like progressive disclosure, schema-driven tool contracts, and proprietary serialization reducing token count by 35–47 percent. The system includes layered security, governance controls, observability through OpenTelemetry and Datadog, and automated deployment with canary testing to ensure AI agents can reliably work with enterprise data while maintaining performance and compliance.
Sakana AI
● 2 sources
Sakana AI developed an automated proposal generation application for Sumitomo Mitsui Banking Corporation to streamline wholesale banking processes. The application reduces proposal writing time from one to two weeks to several hours or tens of minutes by deploying multiple AI agents that perform data collection, analysis, hypothesis building, and fact-checking. Bank employees can now focus on strategic problem-solving while AI handles document creation and identifies insights humans might overlook.
Sifted
·
4 days ago
Dust CEO Gabriel Hubert describes a shift from individual employees using isolated AI chatbots to multiplayer AI systems where agents work collaboratively across departments, learning from company data and sharing workflows. By 2027, Hubert expects organizations will focus on managing multiple agents rather than debating whether to use them at all. Companies must address governance challenges, prevent unauthorized AI use, and maintain human oversight as agents take on more execution while human judgment becomes increasingly valuable.
