agentic-engineering

# Agentic Engineering Refactor a skill or workflow so the model can execute more work with less human steering, while preserving verification, provenance, security, and the professional quality ceiling. Agentic Engineering is the step after vibe coding: speed is useful only if quality does not degrade. Humans keep ownership of taste, judgment, architecture, and risk boundaries; agents execute bounded macro actions with evidence. ## Agent Understanding Model Use the Karpathy-style LLM OS mapping as the design baseline: | OS concept | Agent workflow meaning | |---|---| | LLM = CPU | The model runs the next reasoning/action loop. | | Context = RAM | Load only the state needed for the next step. | | Wiki/logs = Disk | Durable knowledge lives outside chat memory. | | Tools = System calls | Actions must have contracts, permissions, and evidence. | | Loop = Scheduler | Plan -> Act -> Observe -> Iterate controls work. | The agent is a process with state, tools, permissions, and write-back duties. Do not design skills as advice pages; design them as executable control loops. ## Full-Stack Agent Pattern Use the Google I/O '26 wiki update as the new maturity signal: useful agents are moving from chat boxes into full-stack product surfaces. | Surface | Agentic engineering requirement | |---|---| | Developer IDE or CLI | subagents, hooks, async queues, tests, diffs, task state | | Personal agent | user intent, memory boundary, tool allowlist, resumable tasks | | Agentic search | ...