Know Today

AI context Agent guardrails Model review Developer workflows Knowledge bases Prompt maintenance Approval policies

👀 Keep an eye on

  • Reusable, narrowly scoped skills are becoming a practical layer between always-on instructions and one-off prompts.
  • Source-constrained answering modes are worth testing where internal consistency matters more than broad model knowledge.
  • A second model can provide a more useful critique gate for consequential plans and changes than self-review alone.
  • Agent permissions are best designed around reversibility, with human approval required for one-way actions.
  • Instructions, skills, and retrieval content need ownership and periodic cleanup as they accumulate.

🛠️ Practical workflows

  • Keep global guidance small: use it to route work to the relevant domain skill and reference material.
  • Make skill boundaries explicit: state both when a skill applies and when it should defer to a more specific workflow.
  • Ask for dissent by contract: require an agent to surface assumptions, alternatives, and failure modes before it acts on a plan.
  • Audit context monthly: remove stale rules, merge duplicates, and split folders whose contents have become too broad.

💭 Opinions worth testing

OpinionGeneric model output often reflects missing or poorly structured context; the useful decision is to improve reusable context architecture before endlessly tuning individual prompts.

OpinionModels may default to agreeable answers, so independent critique should be designed into high-impact workflows rather than assumed.

PredictionContext will decay as an AI workflow evolves; treat its rules and knowledge as maintainable operational assets.

⚠️ Caveats

  • VerifyAutomatic skill routing and cross-model integration behavior should be validated in the current tools before production use.
  • CautionMultiple agents do not guarantee independent judgment if they receive the same incomplete context or flawed source material.
  • MeasureCompare constrained and unconstrained answers for accuracy, coverage, and user usefulness before enforcing a knowledge-base-only mode.

✨ Try this today

Build a two-skill router. Create one general writing skill and one narrowly scoped support-reply skill, with explicit exclusions in each. Test ten representative requests and note where routing remains ambiguous.
Run a source-constrained comparison. Give an LLM a small set of internal notes and ask the same operational question with and without a “use only this material” constraint. The expected outcome is a clear trade-off between breadth and traceable consistency.
Add a critique pass to a small code change. Have one model propose a change, then give the diff and acceptance criteria to another model to identify risks and missing tests. Review both outputs yourself before applying anything.
Classify an automation’s actions. List its possible actions as reversible or irreversible, then add an approval checkpoint before the irreversible group. The expected outcome is a simpler, more defensible permission boundary.