I’ve been using Google Antigravity, and it’s becoming clear that things are changing. We keep calling these tools "Editors," but Antigravity is an actual Agentic Environment. Until recently, we spent hours building, clicking, and testing software, and even automations. But Antigravity solves this by closing the loop. It doesn't just "suggest" code; it acts on it. And to understand how it does so, just remember this framework. The D-O-E Framework: 1. Directives: •󠁏󠁏 This layer defines the goal and parameters of the task in high-level, natural language, acting as the guide for the agent. •󠁏󠁏 It provides the high-level intent without dictating the exact technical steps. •󠁏󠁏 This serves to provide the context, objectives, and constraints to the AI, helping to outline the goal. ​ 2. Orchestration: •󠁏󠁏 This is the intelligence layer that is powered by an LLM model. •󠁏󠁏 The orchestrator reads the directives, plans the workflow, and decides which tools or sub-agents to deploy. •󠁏󠁏 It manages the process by generating plans, code, and tests asynchronously. 3. Execution •󠁏󠁏 This layer consists of the actual code, scripts, and tools that perform the work. •󠁏󠁏 It is deterministic for the most part; however could be non-deterministic as well. •󠁏󠁏 It serves to carry out specific actions reliably, such as scraping a website, sending an email, or querying a database. It’s definitely a different way to build. Is it perfect? No. But I like that DOE gives the whole process a structured vibe without overcomplicating it. Will be observing how this evolves over the next few months. Until next time. (Credits: D-O-E framework, referenced from Nick Saraev)

Premature automation doesn’t usually fail loudly — it fails quietly. It shows up as workflows built around assumptions that were never tested, logic that no longer matches reality, and systems that are hard to change because no one fully understands them. Automating too early often locks in decisions before a process has earned its shape. Instead of creating leverage, it creates maintenance work and hidden friction. A better approach is to let a process run manually long enough to reveal its patterns. Once the trigger, decision points, and outcome are clear, automation becomes obvious — and far more durable. Automation delivers the most value when it follows clarity, not curiosity. When do you usually know a process is ready to be automated?