Know Today
👀 Keep an eye on
- Connected AI agents shift automation from isolated prompts to workflows that can read and act across the systems where work happens.
- Connector write permissions and audit trails are becoming practical product-selection criteria, not implementation details.
- Reusable agent instructions can turn a successful one-off interaction into a maintained team workflow.
- Meeting transcripts can become structured operational input for follow-ups, issue updates, and customer-signal extraction when access is governed.
- Automated evaluations are increasingly necessary because agent output can scale faster than careful human review.
🧭 What changed
| Capability | Practical implication |
|---|---|
| Agents can work with connected business systems | Evaluate the allowed actions, approval steps, and identity model alongside output quality. |
| Skills capture recurring process context | Version them like lightweight operational assets: owner, examples, tests, and a revision trail. |
| Evaluation can be automated | Codify acceptance criteria before increasing the volume or autonomy of a workflow. |
🛠️ Practical workflows
- Start narrow: pilot one or two recurring, low-risk workflows with a small group and record time, quality, cost, and failures.
- Map integrations first: document each system’s read/write scope, approval requirements, retention rules, and auditability.
- Promote proven prompts: after an interactive workflow succeeds repeatedly, convert it into a reusable instruction with explicit guardrails.
- Reserve review for exceptions: use automated checks for routine output and route ambiguous or high-impact cases to people.
💭 Opinions worth testing
Opinion: Reusable skills may be the highest-leverage team capability because they preserve context and reduce repeated prompting. The decision it influences is whether to invest early in ownership and maintenance rather than treating agent use as individual experimentation.
Prediction: Human review capacity will become the bottleneck as agents handle more work. The useful response is to build measurable checks and escalation paths before expanding agent permissions.
⚠️ Caveats
- Product names, availability, connector behavior, and permissions need direct verification in the plan and environment you intend to use.
- Claims about vendor quality and time saved are anecdotal rather than controlled benchmarks.
- Moving documents or data stores for better agent access can create security, compliance, migration, and lock-in costs.
✨ Try this today
Build a connector matrix
List three tools you use and mark what an agent may read, write, approve, and log in each one. The outcome is a concrete shortlist of safe first workflows and missing controls.
Turn a repeat task into a skill
Use an LLM to perform one recurring task interactively, then save the successful instructions with two test examples and one known exception. The outcome is a reusable draft that another person can review and improve.
Make a transcript-to-action prototype
Provide a redacted meeting transcript and ask an LLM for follow-ups, decisions, and proposed ticket updates, then compare it with your own notes. The outcome is a small evaluation set for judging whether this workflow is worth connecting to real systems.
Define a lightweight evaluator
For a routine agent output, write five pass/fail checks such as required fields, unsupported claims, or missing escalation. The outcome is a review rubric that can later become automated tests while keeping humans on exception handling.