Smartsheet launches AI agent interface on AWS; cuts token use 35–47%

Smartsheet's MCP server addresses a critical gap in enterprise AI adoption: most internal systems were not built with structured machine-readable interfaces that AI agents can safely and efficiently consume. By layering an AI-optimized interface on top of Smartsheet's existing APIs and central intelligence layer, Smartsheet enables agents to orchestrate complex workflows autonomously—picking up tasks, attaching test results, drafting documentation—all within the same sheets that human users access. This parity between internal Smart Assist and external AI clients like Amazon Quick and Claude Desktop means Smartsheet builds once and every agentic client benefits immediately, avoiding fragmentation.

The infrastructure choices reflect the unique demands of agentic traffic. Traditional API services handle request-response cycles; agentic traffic is bursty, with agents firing multiple tool calls in seconds as they reason through tasks. Smartsheet addressed this by running the MCP server on AWS Fargate with auto-scaling policies that combine traffic volume and compute utilization, not just proxy throughput. The deployment strategy—rolling to the smallest region first, validated by canary tests every 15 minutes—protects active agent sessions from disruption. The observability layer extends beyond traditional monitoring to capture context across tool chains, since a single user request can produce a chain of tool calls and failures often trace back several steps.

Token cost is the defining economic constraint of AI agent deployments. Smartsheet's three-level optimization strategy—progressive disclosure that caps response size, strongly typed schemas that prevent hallucinated parameters, and proprietary serialization that cuts token count by 35–47 percent—directly attack this problem. The 3 billion tokens saved since launch demonstrates that the server design prioritizes not just correctness but efficiency at scale.