9 articles
Cursor's latest agent-swarm experiment rebuilt a SQLite-like database from documentation and passed a held-out conformance suite. The bigger story is the shift from assigning code tasks to specifying, measuring, and governing a goal.
A July 2026 paper from Tencent Hunyuan turns agent harnesses into behavior-level maps. The useful lesson for builders is simple: code search is not enough when one behavior spans prompts, tools, state, permissions, and runtime policy.
SkillHone is a July 2026 paper about evolving agent skills across sessions. The useful takeaway for developers is simple: do not save only the latest SKILL.md. Save the decisions that explain why it changed.
Long-Horizon-Terminal-Bench tests coding agents on 46 terminal tasks that can run for 90 minutes. The takeaway is not that agents are useless. It is that evals need to measure endurance, recovery, and partial progress.
Dan Luu's new agentic coding essay is not another vibe check. It is a useful reminder that coding agents only compound when the test loop, review loop, and task-selection loop are stronger than the code generator.
A Show HN project claims large agent-cost cuts by rendering bulky context as images. The useful lesson is not the trick itself. It is that compression needs evals, byte-safety rules, and per-request accounting.
OpenAI's June deprecations put Agent Builder, hosted Evals, and reusable prompts on a November 30 shutdown path. Here is the practical migration plan: Agents SDK, repo-owned prompts, and eval receipts.
The Bayer and Thoughtworks PRINCE case study is a useful reminder that reliable agentic AI comes from context routing, traces, evals, monitoring, and human review, not from a better prompt alone.
Hex's data-agent lab shows the practical eval pattern AI teams should copy: compare candidates against stable baselines, keep receipts, and judge changes by task behavior.
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