The Agent Stack Moves From Demos to Governed Systems

Managing the Life Cycle of AI Agents at Scale puts continuous evaluation, observability, identity, tool controls, and governance across the agent lifecycle. That is the minimum operating model for Principles 10, 14, and 16: agents need production controls, not just prompt tests.

Teradata aims to make agentic execution of multistep data work more efficient introduces Tera Harness for planning, batching, and pruning agent workflows to reduce tokens, latency, and cost. Outcome engineers should treat orchestration as a systems problem—Principle 09—with explicit execution plans and measurable efficiency targets.

Thinking in Systems, Shipping in Loops argues that AI-native engineering shifts effort from hand-coding toward resilient system design, verification loops, constraints, and reusable agent skills. The practical takeaway is Principle 16: build feedback loops that judge shipped outcomes, not merely model output.

Neo4j Makes the Case for Knowledge Graphs as Shared Context for AI Agents positions knowledge graphs as persistent context for enterprise agents’ data, rules, and processes. Shared, legible state gives coordinated agents a common operating picture—Principles 06 and 11—instead of forcing every run to reconstruct context from scratch.

AI Agent Kill Switch Urged by Okta-Led Alliance: How Businesses Could Make It Work proposes shared controls and an emergency kill switch for containing rogue agents before anomalous behavior becomes a breach. Runtime authority, rapid revocation, and recovery paths are core Principles 10, 14, and 15 for any system that lets agents act in the real world.