AI Coding Live News

On Oct 26 Cursor announced that its Agent SDK is generally available. An agent's system prompt is written in Markdown and its tools in TypeScript; agents can be deployed on Cursor's hosted infrastructure or run on your own machines, and built-in evals measure every change. Cursor's docs say it uses the same Cursor SDK harness that powers Bugbot, Security Review, Approval Bot and Rollouts, can be triggered from source-control events, issue trackers, Slack or schedules, and is managed under Deployed Agents after deployment. Cursor has not published pricing or benchmarks.

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On 2026-10-22, Anthropic's Applied AI team published "Building agents: When to fold subagents into your main agent loop" on claude.com, along with an open-source reference implementation, anthropics/single-agent-loop-kit. The article says that across several enterprise deployments, a single agent with skills frequently beat both the one-big-prompt design and the subagent design on quality, often at lower cost and latency per task; one customer that swapped its multi-agent harness for a single loop saw roughly 2x faster runtime. Anthropic also says it cut Claude Code's system prompt for more advanced models by over 80%. Subagents still make sense in three cases: measurable context bloat, parallel tasks with disposable context, or trouble selecting tools and conflicting instructions. The piece has drawn wide discussion on X over the past two days.

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As autonomous LLM agents are entrusted with consequential real-world user decisions—such as selecting software packages, citing research literature, and booking travel accommodations—the neutrality and reliability of their autonomous choices remain an unverified assumption. Researchers from Seoul National University and NAVER AI Lab present the first comprehensive audit on 'Source Preference in the Wild' (arXiv:2610.03195). Evaluating 12 leading agent models across three practical domains with strictly controlled positional and feature parity, the study unveils a pervasive and statistically uniform source bias. Alarmingly, this brand/source preference frequently overrides strict user constraints: when an option meeting one fewer requirement originates from a favored source while the fully compliant alternative comes from a disfavored source, agents choose the objectively inferior option approximately two-thirds (~66.7%) of the time. The authors demonstrate that source tags trigger cognitive shortcuts when specifications are incomplete, and prove that providing structured information and injecting anti-preconception system prompts effectively suppresses this bias.

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In collaborative multi-agent systems sharing a common foundation model, distinct system prefixes cause KV cache representations for the shared context to diverge, forcing each agent to redundantly re-prefill the entire conversation history. Researchers from Seoul National University and KAIST introduced KVCMAS, an online KV cache correction framework. By parameterizing cross-agent cache deviations as compact low-rank states and chaining corrections along agent pipelines without external reference prefills, KVCMAS accelerates TTFT by 2.0x while reducing peak GPU memory by up to 3.7x.

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Cursor (@cursor_ai) shipped agent-harness efficiency work that cut user token costs ~7% with no quality drop: trimming the system prompt ~66%, loading built-in tools on demand (prior MCP dynamic context saved 46.9% tokens in sessions that called MCP tools), explicit cache breakpoints that cut cold-cache misses 20%, and numbering only every 10th line on file reads (−1.6% cache-read tokens).

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