Know Today
👀 Keep an eye on
- Reusable agent skills turn a successful interactive run into a repeatable procedure that can be improved with later corrections.
- Read-only aggregation across calendars, mail, and workspaces is a practical first agent deployment because it reduces action risk while exposing useful context.
- Time-zone mistakes, missed tasks, and missing links are not edge cases: capture them as regression cases before trusting a recurring briefing.
- Sharing skills or plugins makes workflow quality portable, but also makes ownership, review, versioning, and rollback operational requirements.
- Connector scopes matter more than chat polish when an agent can reach files, communications, or task systems.
🧭 What changed
| Pattern | Practical implication |
|---|---|
| Do a task, distill it into a skill, reuse it, then feed failures back. | Treat good AI runs as versioned operating procedures, with clear acceptance criteria. |
| Skills and tool connections may be packaged and distributed separately. | A shared workflow needs a named owner and a controlled release path, not just a useful prompt. |
| Agents can combine planning, tool use, and workspace access. | Review filesystem and connector permissions as carefully as you would application credentials. |
🛠️ Practical workflows
- Start read-only: assemble a daily brief or weekly status view before allowing an agent to edit records or send messages.
- Define permission tiers: read by default, explicit confirmation for writes, and no delete capability unless there is a narrow, reviewed need.
- Keep correction examples beside the procedure: prioritisation rules, preferred links, date handling, omissions, and formatting are all testable behavior.
- Measure baseline cycle time, error rate, and review burden so claimed productivity gains can be evaluated against the current process.
💭 Opinions worth testing
- Opinion: Delegating to an agent like a new hire is more productive than treating it purely as a chatbot; test this by comparing a documented recurring workflow with ad hoc prompting.
- Prediction: Teams will move from personal assistance to redesigned processes whose output grows with model usage; use this to examine bottlenecks, not to assume linear savings or reduced staffing.
- Opinion: A few recurring skills can remove substantial busywork; treat that as a hypothesis and include setup, oversight, and rework in the measurement.
⚠️ Caveats
- Verify product behavior independently: availability, portability, automatic skill selection, and version-control claims may vary by plan and environment.
- Connected data raises material access risk: confirm tenant controls, audit logs, retention, sharing boundaries, and least-privilege support before a wider rollout.
- Productivity and cost examples are anecdotal; they may omit integration work, review time, and error remediation.
✨ Try this today
Build a read-only personal brief
Give an LLM a small exported set of calendar items, tasks, and selected messages, then ask for a fixed briefing format with links and time-zone rules. The outcome should reveal which context is useful before you connect live systems.
Turn one good run into a checklist
Complete a recurring task interactively, then save the final instructions, inputs, output shape, and acceptance checks in a Markdown file. Run it again on different data and add every correction as a regression case.
Run a permission review
List every tool an agent would need for one workflow and classify each as read, write, or delete. The expected outcome is a narrower pilot scope with explicit approval points for consequential actions.
Compare ad hoc prompting with a procedure
Perform the same briefing twice: once from a fresh prompt and once from your saved checklist. Track omissions, editing time, and consistency to see whether the procedure is earning its maintenance cost.