ClawVM: Harness-Managed Virtual Memory for Stateful Tool-Using LLM Agents

ClawVM is a virtual memory management system designed for stateful LLM agents that addresses critical failures in current context window management. The system implements typed pages, multi-resolution representations, and validated writeback protocols to ensure deterministic state residency and durability, adding minimal computational overhead.

ClawVM addresses a fundamental architectural problem in LLM agent systems: the absence of reliable state management within the context window. Current agent harnesses treat memory as best-effort, leading to cascading failures including lost state during context compaction, unexecuted flush operations, and data corruption during writeback cycles. This research positions the harness layer itself—which already orchestrates prompts, mediates tool calls, and observes lifecycle events—as the natural enforcement point for memory contracts.

The innovation lies in treating agent state as typed virtual memory pages with minimum-fidelity invariants rather than unstructured context. This abstraction enables multi-resolution representations under strict token budgets, allowing agents to maintain essential state while operating within LLM constraints. The system validates writeback at every lifecycle boundary, ensuring state consistency across tool invocations and context resets.

For the AI infrastructure sector, ClawVM represents a maturation of agent reliability engineering. As LLM agents move from research prototypes to production deployments, deterministic state management becomes critical. Organizations deploying long-running agents for autonomous decision-making, database operations, or financial transactions cannot tolerate state loss or corruption. The median 50-microsecond overhead per turn is negligible relative to LLM inference latency, making adoption friction minimal.

The research validates the approach across synthetic workloads, 12 real-world session traces, and adversarial stress tests, with an offline oracle confirming fault elimination when minimum-fidelity state fits within token budgets. Future work likely explores dynamic replication, distributed state management, and integration with emerging agent frameworks. This addresses a gap between theoretical agent capabilities and production reliability requirements.

• →ClawVM eliminates policy-controllable state management faults in LLM agents by implementing virtual memory at the harness layer.
• →The system uses typed pages with minimum-fidelity invariants to maintain state within token budget constraints while ensuring durability.
• →Validation across real-world traces and stress tests confirms complete fault elimination when minimum-fidelity state fits the token budget.
• →Median computational overhead of 50 microseconds per turn makes ClawVM deployment practical for production agent systems.
• →Harness-layer enforcement makes residency and durability deterministic and auditable, critical for high-stakes agent deployments.