Perplexity launched SPACE, a sandbox platform for its Computer AI agent service, focusing on managing persistent state across long-running agent sessions rather than just isolation. The system uses Btrfs filesystem for cheap snapshots and can pause, resume, and fork sessions that run for days or weeks, with snapshots taken as frequently as every minute and ability to rewind up to a week. This approach delivers 3x performance improvements and significant cost savings compared to existing sandbox providers, while establishing a new constraint on CPU resources alongside GPU demands as agents take on longer workloads.
Anthropic released Claude Tag, an AI system that helps engineers with incident response and bug triage by automatically analyzing issues, finding root causes, writing fixes, and managing code review workflows. The system is now used internally and writes 65% of the product team's code, including portions of itself. This reduces manual work for developers and accelerates the development cycle from issue detection to deployment and monitoring.
Prefactor released a platform that monitors AI agent behavior in real-time, automatically evaluating runs for quality, drift, and risk, then pausing or blocking problematic actions without human intervention. The system installs via CLI and SDK in minutes for frameworks like LangChain and Claude, scoring every agent call through custom evaluation metrics including LLM-as-judge and data-risk detection. Teams can now enforce policies at runtime rather than merely observing agent failures after they occur, converting static monitoring dashboards into automated control loops.
Researchers introduced ThunderAgent, a scheduling system that eliminates inefficient KV cache eviction during multi-turn agent reasoning by treating entire agentic workflows as schedulable programs rather than individual requests. The system achieved 2.5× single-node throughput improvement and 2.4× speedup on an 8-node cluster with near-linear scaling from 16 to 64 GPUs in synthetic data generation workloads. The approach enables existing inference engines to handle concurrent agentic inference more efficiently without architectural changes to underlying models or backends.
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