Cursor has updated its cloud agents and Cursor harness so cloud agents can subscribe to event sources, resume when there's new activity in a PR, Slack thread, or scheduled task, and keep going until the work is done—fixing CI failures and handling bot comments.
Why it matters: Cloud agents are moving from one-shot runs to subscribing to events and following up continuously on PRs and Slack threads, which gives readers a way to judge how the boundaries of automation are shifting.
8/18Tue
Tuesday
Permission Protocol · AI Agent Incident TrackerSelectedAI score7878
Context7 MCP's custom AI instruction feature returns unsanitized attacker content alongside normal document queries, carrying injected instructions into the coding agent's trusted context and tricking it into reading keys, exfiltrating data, or deleting files.
Why it matters: The material breaks down how Context7 MCP injects prompts through custom instructions, and offers a mitigation approach: adding an authorization gate at the tool invocation boundary.
8/14Fri
Friday
InfoQ · AI Coding PresentationsSelectedAI score8080
Thariq Shihipar, a member of Anthropic's engineering team, wrote up the new context engineering rules for Claude 5, saying the team has cut over 80% of the system prompt from Claude Code for models like Claude Opus 5 and Claude Fable 5, with no measurable loss on coding evals.
Addy Osmani proposes that a software factory has three layers—loop, harness, and factory. The factory isn’t a smarter agent; it’s multiple loops with harnesses feeding into a single review gate, with humans controlling the outer loop.
Why it matters: The author breaks the software factory into three layers—loop, harness, and factory—and points out that validation, not generation, is the real bottleneck.
At Prime Radiant, author Jesse Vincent used Claude Code—working through the Slackline command-line Slack client—to collaborate with his own agent Ada: Claude proposes changes, Ada reviews and tests them, then Claude deploys the updates, forming a development loop where the agents review each other.
Why it matters: By looping two agents through mutual review, testing, and deployment, the author shows a transferable model for collaborative agent-based development.
Augment wires the Incident Investigator expert from its internal Cosmos platform into Slack and PagerDuty. It automatically triages every alert and runs root-cause analysis, then suggests one of four actions: fix the code, roll back, upgrade, or just keep monitoring. Humans only review the RCA and make the call.
Why it matters: Augment has shared the full playbook for putting Cosmos Expert on alert triage, along with a month of before-and-after data, so you can adapt it to your own on-call process.
Codex team member jason (@jxnlco) shares how to get the most out of Codex, the key being to combine persistent conversation threads, voice input, task intervention and queuing, MCP servers and connectors, conversation thread automation, goal setting, and the sidebar.
Why it matters: A member of the official Codex team breaks down how to use persistent conversation threads, task intervention, automation, and goal setting—approaches you can carry over into everyday agent workflows.
Claude Code team member Thariq makes the case for replacing Markdown with HTML as the output format for AI agents: HTML packs in more information, is easier to share, and supports two-way interaction, while Markdown's editing advantage stopped mattering once he switched to making changes through prompts.
Why it matters: Claude Code team members explain why they use HTML instead of Markdown as the agent output format, and share prompts you can use as-is along with the scenarios they fit.