Frequently asked questions about thinking, epistemology, and cognitive tools. 1647 answers
Too much monitoring data overwhelms attention and leads to ignoring signals that matter. The solution is not more data — it is fewer, sharper signals routed to the right layer of attention.
Monitoring without action is observation theater — data must drive decisions.
Use monitoring data to make targeted improvements to your agents.
Improving anything other than the bottleneck is wasted effort.
Change one thing at a time so you can attribute improvements to specific changes.
A reliable agent works every time, not just when conditions are perfect.
Pick one agent (habit, routine, automated behavior) that you consider important but that fails more than 20% of the time. Map every instance in the last 30 days where it fired and where it didn't. For the failures, identify the specific condition that broke it — fatigue, travel, interruption,.
Treating reliability as willpower instead of engineering. When an agent fails, the instinct is to try harder next time — set a louder alarm, make a firmer commitment, feel guiltier about the miss. This is the equivalent of telling a server to 'just not crash.' It does not address the structural.
A reliable agent works every time, not just when conditions are perfect.
An agent that tries to do too much does nothing well. Optimize by narrowing scope to what matters.
Select one agent — a habit, routine, workflow, or recurring process — that currently feels bloated or unreliable. List every action this agent currently includes. For each action, classify it as core (directly serves the agent's primary purpose), supporting (indirectly useful but not essential),.
Narrowing scope so aggressively that the agent loses the capability it needs to accomplish its purpose. This is the inverse failure — under-scoping. A morning routine stripped to only coffee and calendar review may execute reliably, but if the workout and meditation were genuinely load-bearing for.
An agent that tries to do too much does nothing well. Optimize by narrowing scope to what matters.
Optimize how agents connect and hand off to each other, not just how each agent performs in isolation.
Record what you changed, why, and what happened — optimization without documentation is gambling.
Creating an agent is a deliberate design act — not something that just happens.
New agents are most fragile in their first month — they need extra attention and support to survive.
Track versions of your agents so you can compare, rollback, and learn from changes.
Choose one agent you actively use — a decision-making heuristic, a weekly review process, a communication protocol, a problem-solving routine. Write down its current form as v_current (assign whatever version number feels right based on how many times you think it has changed). Then reconstruct.
Versioning without actually preserving the old version. Slapping 'v2' on your current process while letting v1 fade from memory defeats the entire purpose. If you cannot retrieve the previous version and compare it side-by-side with the current one, you have version labels but not version control..
Track versions of your agents so you can compare, rollback, and learn from changes.
Periodically review and rebalance your agent portfolio — retire underperformers, invest in high-value agents.
Documentation should evolve with the agent — outdated docs are worse than no docs.
Pick one agent or automated system you currently maintain. Open its documentation — README, wiki page, inline comments, whatever exists. Read every factual claim: data sources, triggers, dependencies, output destinations, failure modes. For each claim, mark it as current, outdated, or unknown..