Agents silently drop long-term sub-objectives when context window retention fails across sessions, and have no persistent mechanism to reconstruct goal state, behavioral history, or experiential continuity. Current memory architectures address declarative knowledge storage but lack a principled way to compile experience into persistent behavioral identity or carry goal hierarchies across sessions. This creates invisible task failure that neither agents nor operators can detect without expensive manual auditing.

Agents silently drop long-term goals across sessions because no infrastructure exists to persist goal hierarchies, track sub-objective completion, or reconstruct behavioral state — causing invisible task failures that are expensive to detect.

AI agent developers and companies deploying autonomous agents for multi-session workflows (DevOps, research, customer success, sales pipelines) who are losing reliability at the edges of context windows.

Teams deploying production agents already spend significant engineering hours building bespoke memory/state systems; a standardized goal persistence layer with drift detection replaces weeks of custom infra with a drop-in SDK, and the pain compounds as agents get more autonomous and long-running.

MVP is an open-source SDK + hosted API that provides a structured goal-tree data model with state serialization, a lightweight reconciliation protocol agents call at session start, and a drift-detection webhook that alerts operators when sub-objectives are dropped or stale — works with any LLM via simple tool-call integration.

The AI agent infrastructure market is projected at $10B+ by 2027; persistent state management is a foundational primitive every production agent needs, comparable to how databases underpin web apps.