Per-Turn Evaluation: Dynamic Governance for AI Agents
The article discusses the need for dynamic governance in AI agents through per-turn evaluation. It argues that static governance is insufficient as agents change tasks throughout their operation. By implementing per-turn evaluation, governance can adapt to the current state of the agent, ensuring more precise decision-making.
- ▪Static governance fails when agents switch tasks mid-session, leading to irrelevant rules being applied.
- ▪Per-turn evaluation allows for governance to be based on the current state rather than a fixed startup configuration.
- ▪Using a declarative language like Starlark can enhance the governance conditions for AI agents.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 2155191) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Hector Flores Posted on May 25 • Originally published at htek.dev Per-Turn Evaluation: Dynamic Governance for AI Agents #aiagents #security #agenticdevelopment #platformengineering Static governance is fine right up until your agent changes modes mid-session. The same agent can spend turn 1 researching docs, turn 8 editing code, turn 14 fixing failed tests, and turn 20 preparing a production deploy.
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