--- title: "Maintaining Context as a Manager and Leveraging AI Agents Part 2" url: https://daily.dev/posts/maintaining-context-as-a-manager-and-leveraging-ai-agents-part-2-tzpwjlwcx source_url: https://softwareleads.substack.com/p/maintaining-context-as-a-manager-35c type: article source: "Effective Software Leads" published: 2026-08-25T10:48:13.425Z updated: 2026-08-25T10:48:38.301Z tags: ["ai-agents", "productivity", "leadership", "prompt-engineering", "openai-codex"] reading_time: 8 upvotes: 0 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Maintaining Context as a Manager and Leveraging AI Agents Part 2 **[Effective Software Leads](https://daily.dev/sources/softwareleads)** · 8 min read · 0 upvotes · 0 comments ## Summary 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. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## 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._ ## Similar posts on daily.dev - [Maintaining Context as a Manager Part 1](https://daily.dev/posts/maintaining-context-as-a-manager-part-1-8jt2zuldd) · Effective Software Leads · 2 upvotes · 0 comments - [Give Your AI Unlimited Updated Context](https://daily.dev/posts/give-your-ai-unlimited-updated-context-nfkh5uj5t) · Towards Data Science · 1 upvotes · 0 comments --- Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#productivity](https://daily.dev/tags/productivity), [#leadership](https://daily.dev/tags/leadership), [#prompt-engineering](https://daily.dev/tags/prompt-engineering), [#openai-codex](https://daily.dev/tags/openai-codex) [View this post on daily.dev](https://daily.dev/posts/maintaining-context-as-a-manager-and-leveraging-ai-agents-part-2-tzpwjlwcx)