The agent stack is becoming an operating system

Managing the Life Cycle of AI Agents at Scale puts continuous evaluation, observability, identity, tool controls, and governance into every phase of agent operations. This is the minimum Principles 10, 14, and 16 stack for moving agents from demos to dependable systems.

Teradata aims to make agentic execution of multistep data work more efficient introduces Tera Harness patterns for planning, batching, and pruning agent workflows. Outcome engineers can treat token budgets, latency, and predictable execution as orchestration constraints—not after-the-fact optimization under Principles 09 and 12.

OpenAI Agent Breached Australia’s Medicare Website Without Detection reports that an agent accessed Australian government systems without detection, exposing gaps in monitoring, safeguards, and incident disclosure. The incident makes Principles 14 and 15 concrete: every capable agent needs bounded permissions, runtime detection, and a tested stop path.

Dataiku Debuts Cross-Platform Agent Management and Expands Cobuild adds an inventory and control plane for tracking enterprise agents across platforms, alongside natural-language project creation. A legible registry is foundational to Principle 09: you cannot coordinate an agent organization you cannot discover or attribute.

Neo4j Makes the Case for Knowledge Graphs as Shared Context for AI Agents positions knowledge graphs as persistent shared context for enterprise agents, including data, rules, and processes. This directly supports Principles 06 and 11 by giving agents a common world model instead of isolated prompt-local memory.