This report reevaluates no-code/low-code platforms for building enterprise-grade AI agents, focusing on agent authentication, sandboxed code execution, secrets management, lineage tracking, and evaluation features. It scores vendors on their native support for these security and operational capabilities, highlighting gaps in sandboxing, guardrails, and LLM hallucination checks.

This guide breaks down 30 fundamental ideas behind AI agents—from the basic think-act-observe loop and state management to multi-agent patterns, guardrails, and observability. It shows how to configure, extend, and safely run agents in any framework by focusing on underlying principles rather than tools.

Solo.io and Google teamed up to integrate Agent Substrate into kagent, enabling fast suspend/resume, scale-to-zero and secure sandboxing for AI agents on Kubernetes. It uses pre-provisioned worker pools, snapshots to storage, and lightweight isolation (gVisor or Firecracker) to cut boot times to milliseconds and minimize idle resource use.

This article argues that to get useful work from AI coding agents, teams must build a structured environment—context, tools, permissions, tests, and review loops—that guides stateless models and enforces deterministic feedback. It covers seven principles, from minimal and tested context to sandboxed credentials and self-validating work, so agents can onboard per task, prove their output, and operate safely at scale.