An MCP server and SDK wrapper that snapshots complete AI agent execution state at configurable checkpoints so long-running workflows can pause, recover from partial failures, and resume exactly where they stopped without re-running completed steps

Long-running agent workflows (10-30 minutes, 20-50 tool calls) have no built-in checkpoint mechanism in the major agent frameworks. When a failure occurs at step 15 of 30, there is no way to know which steps were committed, which data was stored, and which external calls succeeded. Developers report agents that claim success on an empty branch because the previous run's work was never committed and the resuming agent had no record of partial state. The compound failure math makes this critical: at 85% per-step reliability, a 10-step workflow only succeeds 20% of the time end-to-end. The Strands Agents SDK has an open feature request (issue #1138, assigned, Nov 2025) for 'Agent State Management - Snapshot, Pause, and Resume' with use cases spanning production maintenance windows, crash recovery, debugging via state capture at error points, and agent hibernation for resource optimization. AWS ADK, LangGraph, and Temporal all added checkpoint primitives separately in 2026 but no turnkey MCP-compatible layer exists that works across frameworks.