Know Today
👀 Keep an eye on
- Reusable agent skills can turn requirements intake, project setup, and recurring checks into versioned workflow components.
- Per-task model and context routing makes parallel agent work more affordable when routine checks do not need a full project history.
- Reliable automation increasingly means pairing execution with explicit pass/fail verification, run history, and bounded retries.
- Separate producer and reviewer agents can reduce correlated mistakes when both work from clear acceptance criteria.
- Human approval gates remain essential before publishing, spending money, changing production systems, or taking other irreversible actions.
🛠️ Practical workflows
| Pattern | Useful implementation |
|---|---|
| Scripts plus judgment | Move stable fetch, transform, and validation steps into tested scripts; leave interpretation and exception handling to the LLM. |
| Context boundaries | Use separate sessions and folders for unrelated work, then give each agent only the files and criteria it needs. |
| Skill hardening | Record recurring edge cases and failed assumptions in the workflow itself, then review that list as the process evolves. |
| Structured planning | Have the agent ask focused questions before work begins, especially around constraints, success criteria, and ownership. |
💭 Opinions worth testing
Opinion: The useful unit of agent work is a loop, not a prompt: execute, verify, record the result, then retry only within clear limits.
This should influence whether a team invests first in acceptance criteria and observability rather than collecting ever-larger prompt templates.
Opinion: “Middle-to-middle” workflows—human framing, agent execution, human validation—are more dependable than fully autonomous end-to-end operation.
This favors designing review checkpoints around costly downstream errors instead of removing people from the process.
⚠️ Caveats
- Needs verification: Reported speed gains, cost savings, and product capabilities are anecdotal or may change; confirm current behavior and policy before standardising a workflow.
- Risk control: Auto-approval, broad tool access, retries, and concurrent agents can compound cost and impact, so start with approval pauses and spending limits.
- Practical limit: An execution loop is only as trustworthy as its verifier; vague success criteria merely automate ambiguity.
✨ Try this today
1. Build one bounded project-start skill
Write a short reusable instruction that asks five requirements questions, creates a local checklist, and stops for approval. Expected outcome: less repeated setup prompting and a clearer handoff into implementation.
2. Split a small task into producer and reviewer passes
Ask one LLM session to draft a change plan and another fresh session to review it against three acceptance criteria. Expected outcome: gaps become visible before you touch files or run commands.
3. Extract a deterministic step
Choose a repeated task such as formatting, test selection, or file inventory, and replace the conversational instruction with a tested local command. Expected outcome: faster, more repeatable agent runs with less context spent on mechanics.
4. Add a verifier before a retry
For one laptop-scale automation, define a concrete check such as “tests pass and the expected file exists,” log the result, and allow only one manual retry. Expected outcome: you learn whether the workflow is measurable before increasing its autonomy.