Glean 拾遗
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11-13

Graph Engineering: Building a CI Triage Agent Graph

A how-to for replacing a single agent loop with a graph of specialists: nodes each own one verb and a scoped context, edges are deterministic routing code, a shared state file carries history across runs, and a gate decides done or not done. The worked example is a GitHub Actions failure triage system, with copy-ready agent definitions for a classifier and a bug fixer, edge rules that keep hard constraints out of prompts, a STATE.md layout, and a test-gate prompt. Autonomy is staged from manual /goal to scheduled /loop to cloud Routines. The author concedes most teams should not build one yet, and names comprehension debt as the cost of a graph that ships code nobody reads.

08-13

Grok 4.6 Field Guide: Verification Loops Beat Long Prompts

The author used Grok 4.6 as a daily driver for weeks across coding and knowledge work, running side-by-side comparisons against 4.5 with identical prompts. Key finding: short prompts plus a clear preference match two-page specs, while adding a single sentence demanding post-implementation verification and iteration had the highest leverage. 4.6 performs steadily on browser automation, visual QA, inbox triage, and editing a real Excalidraw codebase, but 3D and video work still need human oversight because a screenshot cannot confirm time-based behavior. Probably written by an xAI team member, so treat the launch framing skeptically; the methodology and prompt examples are useful for engineers working with AI coding agents.

x.com · 10 min · Agent Engineering · LLM · Prompt Engineering