Cursor, Claude Code, and Codex are merging into one AI coding stack nobody planned OpenAI just shipped an official plugin that runs inside Anthropic's Claude Code. Not a workaround. Not a community hack. An Apache 2.0-licensed plugin from OpenAI, installed directly into a competitor's terminal. Same week, Cursor 3 launched a rebuilt interface that treats the code editor as secondary. The default view is now an Agents Window for managing fleets of coding agents across repos and environments. Google's Antigravity reached the same conclusion with its Manager Surface. I wrote about what this means for developers - https://lnkd.in/g8QgDDhh Three layers are forming. Orchestration on top, where you manage and route agents. Execution in the middle, where coding agents write, test, and commit code. Review at the bottom, where a different model from a different provider challenges the code the first one wrote. The interesting part is the review layer. When Claude writes code and Codex reviews it, you get independent scrutiny. Different training data, different blind spots. You are no longer asking someone to grade their own homework. Nobody designed this stack. Developers assembled it because no single tool covers everything. Claude for precision on complex refactors. Codex for throughput on parallel tasks. Cursor as the control plane on top. We went through the same thing with infrastructure. Terraform, Docker, Kubernetes. Not one tool to rule them all. Composable layers that got better together. Are you already running multiple coding agents in the same workflow, or still picking one and hoping it covers everything? #AIcoding #DevTools #CodingAgents #SoftwareEngineering
Alex Rogov
2w
The infrastructure parallel is perfect. Nobody "chose" Terraform + Docker + Kubernetes either — engineers assembled it because each tool solved a specific pain point better than any all-in-one solution. My daily stack proves the same pattern: Claude Code for deep architectural refactors (it reads my CLAUDE.md files and respects module boundaries), Cursor for real-time IDE work where speed matters more than depth, and Codex for background parallel tasks. Three tools, three different cognitive modes. The missing layer that ties it together: context engineering. CLAUDE.md files at the project root give every tool the same architectural context. Without that shared context layer, you're just running three disconnected AI tools. With it, they feel like one coherent system. The next platform play will be whoever builds the "Kubernetes for AI coding" — the orchestration layer that connects these tools with shared state.
Redundancy and Adverserial Review seems an interesting way to approach the Reliability issue, Janakiram MSV - but at the cost of - Cost? If multiple Agents (or is it LLMs? Please do clarify) run within the same review layer, and the idea is "adversarial redundancy" - for the output of one agent, to be reviewed by another: 1. In any workflow execution driven by the orchestration layer - are all agents required to invoke the same singular LLM - throughout that workflow? Or is there a choice? 2. If the output of Agent-A is reviewed by Agent-B, and found to be "not satisfactory" (depending on the validations & guardrails) - if Agent-C (from a different vendor) is available, do we ask Agent-C to review the output again? Or, does Agent-C get a chance to generate output afresh? 3. What about Token Usage, Limits, and Costs? This is by far, the biggest hyperparameter that decides Agent configuration and orchestration, putting a premium on workflow breakup, and extent of LLM utilisation.