Owning your runtime is not about developer experience at the scale of Claude Code, it’s about your execution-surface area and the control you have over it.
Agentic systems are no longer just model-driven — they are increasingly runtime-driven through tools, sandboxes, and orchestration layers. When agents are writing code, executing tests, calling external tools, and validating outcomes, the reliability of the entire system becomes a function of the runtime and environment, not just the model.
Controlling the runtime gives Anthropic vertical control across:
- Packaging and distribution
- Execution semantics
- Performance characteristics
- Security surface area
- Operational stability
Of course, owning a JavaScript runtime does not magically make runtime deterministic. The dominant sources of variability still sit lower in the stack: OS behaviour, native dependencies, filesystem semantics, scheduling, hardware differences, and network effects. Runtime ownership is necessary, but not sufficient, for true runtime determinism.
What this does signal is a strategic shift in where value is being captured.
Model capability is no longer the only critical layer.
The execution plane is becoming just as strategic too, can see this in ChatGPT 5’s dynamic model routing capability as well as ecosystem wide Tool Calling and MCP adoption, which are all aimed at increasing LLM determinism through traditional software stacks.
Vertical integration here looks far less like a tooling move and far more like a control-plane strategy: consolidate the stack, reduce dependency entropy, and shrink the surface area where behaviour can diverge.
Intelligence may live in the model, but reliability, scalability, and trust are increasingly properties of the system that executes it.
Agreed, we’ve come a long way since the dogmatic agile of the 90s, and maybe I could be more explicit that this is about introspecting how you’re delivering software (now AI-enabled workflows are everywhere) to decrease the probability of only increasing output (rather than increasing the probability of outcomes) for your users… XP is a good place to start (but not necessarily end).
Yeah, much of XP has just been integrated into modern workflows (for the better!), really getting this out there as a call to arms for folks to _think_ before they churn out 1000s of lines of code with an LLM and ship without thought!
From your perspective, which bits of XP would you consider weird?
As awesome as the Internet Archive's Wayback Machine is, it is still under central control. Worse, as they (reasonably) abide to robots.txt rules, the BBC could easily block access to pages they removed at their end in the archive as well. If you care about something, you need to fully "own" it.
Should work, please file an issue in GitHub, if possible could you navigate to the extensions page, enable developer mode and show the background console and any errors reported?
Agentic systems are no longer just model-driven — they are increasingly runtime-driven through tools, sandboxes, and orchestration layers. When agents are writing code, executing tests, calling external tools, and validating outcomes, the reliability of the entire system becomes a function of the runtime and environment, not just the model.
Controlling the runtime gives Anthropic vertical control across:
- Packaging and distribution - Execution semantics - Performance characteristics - Security surface area - Operational stability
Of course, owning a JavaScript runtime does not magically make runtime deterministic. The dominant sources of variability still sit lower in the stack: OS behaviour, native dependencies, filesystem semantics, scheduling, hardware differences, and network effects. Runtime ownership is necessary, but not sufficient, for true runtime determinism.
What this does signal is a strategic shift in where value is being captured.
Model capability is no longer the only critical layer.
The execution plane is becoming just as strategic too, can see this in ChatGPT 5’s dynamic model routing capability as well as ecosystem wide Tool Calling and MCP adoption, which are all aimed at increasing LLM determinism through traditional software stacks.
Vertical integration here looks far less like a tooling move and far more like a control-plane strategy: consolidate the stack, reduce dependency entropy, and shrink the surface area where behaviour can diverge.
Intelligence may live in the model, but reliability, scalability, and trust are increasingly properties of the system that executes it.
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