A framework for engineering managers to use AI agents for information gathering and daily briefings is presented, built around three markdown files: projects.md (tracks key projects), people.md (tracks direct reports and action items), and daily.md (a synthesized daily briefing pulled from Slack, GitHub, Google Drive, email, and experiment tools). A detailed prompt template for the daily briefing skill is shared, covering time windows, signal evaluation, priority classification (P0-P3), evidence requirements, and output formatting for Logseq. The author runs this as a scheduled task in Codex at 8:00 AM on workdays.
Questions this post answers
How can I set up an AI agent to generate a daily briefing for engineering managers?
Create three context files: projects.md for key project context, people.md for direct-report history and action items, and daily.md as the synthesis skill that scans Slack, Google Drive, GitHub, email, and experiment tools. The daily.md prompt should classify findings by priority (P0-P3), require evidence for every claim, deduplicate across sources, and output a Logseq-ready markdown note answering what changed, what matters, and what action is needed. daily.dev surfaces the AI agent workflows engineering leaders are experimenting with for daily context management.
How do I prevent an AI agent from reporting false regressions when summarizing experiment metrics?
Require the agent to only claim a regression when there is concrete evidence such as negative metric movement, a red or yellow status, a sample mismatch ratio, a rollout pause, a reversion, or an incident-related anomaly. If a source mentions a concerning readout but underlying metrics are unavailable, the agent should report it as a reported concern rather than an established regression, avoiding converting uncertainty into fact. developers building reliable AI reporting workflows track prompt-design patterns like this on daily.dev.