How Endava builds an agentic organization with Codex. Endava codifies senior engineering expertise into Codex agents, accelerating delivery and enabling junior teams to produce senior-level outputs. Outcome engineers can use this as a practical pattern for turning institutional know-how into versioned, testable agent skills that scale across teams — Principle 03/09.

Ktx — Open-source executable context layer for data agents. ktx provides an executable, self-improving context layer that maps warehouses, unifies company knowledge, and serves agents with approved metric definitions. If you build agents that act on business data, adopting a runnable context layer like ktx makes decisions auditable and reduces drift between metrics and agent behavior — Principle 02/11.

Securing and Governing AI Agents At Scale Through A Unified AI Gateway. Palo Alto Networks integrates Portkey’s AI Gateway into Prisma AIRS to create a central control plane for agent identity, policy enforcement, and observability. Outcome engineers need a gate that enforces permissions, traffic policies, and telemetry before agents touch production data — Principle 10/09.

AI agents enter rebuild era as enterprises confront reliability problem. The piece argues enterprises must rebuild agent architectures around durable orchestration, state management, observability, and recovery to fix production reliability. That means prioritizing durable state, replayable traces, and automated recovery paths in your agent stacks, not just prototype-level pipelines — Principle 06/09.

DNS-AID will make AI agents easier to discover, says Linux Foundation. The Linux Foundation backs DNS-AID to register and discover AI agents via DNS, keeping discovery open, secure, and vendor-neutral. Treat agent discovery as core infrastructure and integrate registries and naming into your orchestration, security, and audit surfaces — Principle 11/10.