Day 31: Why agents need outcomes, boundaries, and stop conditions
Why agents need clear outcomes, boundaries, budgets, and stop conditions before autonomy.
Jun 21, 20263 min read
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Series
Agentic AI Foundations
A practical foundation for agentic AI. This series explains why agents need outcomes, boundaries, stop conditions, loops, planning, tools, memory separation, and reflection that changes the next action.
Day 32: Why agent behavior is a loop, not a single prompt
Why reliable agent behavior comes from loop design, not a single clever prompt.
Jun 22, 20263 min read
Day 33: Why planning reduces wasted AI actions
Why planning matters when actions have cost, risk, or dependencies.
Jun 23, 20263 min read
Day 34: Why tool access is where AI becomes operational risk
Why giving AI tools means designing permissions, observability, rollback, and approval paths.
Jun 24, 20263 min read
Day 35: Why agent memory is not one database
Why useful agent memory has to be separated, scoped, and governed.
Jun 27, 20263 min read
Day 36: Why reflection should change the next action
Why reflection is valuable only when it decides whether to revise, retry, escalate, or stop.
Jun 27, 20263 min read