Writing.

Most teams build agents one at a time and manage them the same way. The Agentic OS is the centralized layer that treats agents as a fleet, not a collection of one-offs.

Most agent systems reason over snapshots of reality that are hours stale. Event-driven architecture gives agents the live context they need to make decisions that are actually current.

The shift from passive AIOps to active Agentic SRE is the biggest operational transformation since containerization. Get the human-agent handoff wrong, and you'll create more incidents than you resolve.

Most AI teams ship without regression gates because they don't know how to test non-deterministic systems — they eyeball outputs, vibe-check a few examples, and pray. Evaluation-driven development is the missing discipline that turns 'it looks right' into an actual deployment criterion.

Content moderation was built for chatbots — agents that execute code, call APIs, and coordinate with other agents need behavioral constraints, not content filters.

A context layer is not a vector database — it's a governed tier that determines what your agent knows, remembers, and forgets. Most teams learn this distinction after production breaks.

OKF and RAG solve fundamentally different problems — one structures knowledge at rest, the other retrieves it at runtime — and treating them as competing approaches is the fastest way to build a grounding system that fails at both.

Prompts are the most critical artifact in your AI stack and the least governed. No version control, no staged rollouts, no regression testing. LLMOps is the discipline that fixes this.