Tagged articles

Multi-Agent Scheduling

2 articles · Page 1 of 1

Sep 7, 2026 · Artificial Intelligence

Agent Harness Evolves from Loop to Semantic Task Runtime: Six Core Abstractions

This weekly analysis reveals how agent harnesses are maturing from simple execution loops into full semantic task runtimes, detailing six core abstractions—Scope, Context, Capability, State, Execution, Policy—and a five-layer architecture that moves deterministic system concerns out of LLM prompts into runtime primitives.

Agent RuntimeContext SecurityMemory Service

0 likes · 29 min read

Agent Harness Evolves from Loop to Semantic Task Runtime: Six Core Abstractions

JD Tech Talk

Oct 9, 2020 · Artificial Intelligence

Dynamic Public Resource Allocation Based on Human Mobility Prediction

This article presents a method for dynamically deploying public resources such as trash bins using human mobility prediction, describing the problem background, challenges, a multi‑agent long‑term maximal coverage scheduling model, an energy‑adaptive heuristic, and experimental results showing up to 80% resource savings.

Dynamic Resource AllocationMulti-Agent SchedulingUrban Computing

0 likes · 13 min read

Dynamic Public Resource Allocation Based on Human Mobility Prediction