SOURCE-LINKED INTELLIGENCE

AgentPProf: Semantic Profiler for Long Horizon AI Agents

arXiv · AI, language, vision and robotics · article · Sep 14, 2026 · UTC

AI agents increasingly orchestrate long-running activities with users, tools, and system resources for days and weeks. To improve agent quality, safety, and cost efficiency, developers need to determine where failures happen, what triggers unsafe effects, and which tasks consume the most budget, then optimize those tasks. In systems software, profiling answers similar questions by aggregating resource consumption and attributing it to responsible code paths to identify hotspots. Yet existing agent observability tools focus on per-execution debugging and tracing rather than cross-run, long term

Read original source ↗ Open in workspace

recordType

paper

region

Global