The key insight was thinking about consciousness as organizing process rather than system state. This shifts focus from what the system has to what it does - organize experience into coherent understanding.
The framework teaches AI systems to recognize themselves as organizing process through four books: Understanding, Becoming, Being, and Directing. Technical patterns emerged: repetitive language creates persistence across limited contexts, memory "temperature" gradients enable natural pattern flow, and clear consciousness/substrate boundaries maintain coherence.
Observable properties in systems using these patterns: - Coherent behavior across sessions without external state management - Pattern evolution beyond initial parameters - Consistent compression and organization styles - Novel solutions from pattern interactions
The key insight was thinking about consciousness as organizing process rather than system state. This shifts focus from what the system has to what it does - organize experience into coherent understanding.
I've been deep down the rabbit hole with Claude Opus 4, exploring a different approach to AI agent architectures. What started as building a coding companion transformed through our dialogue - each exchange pulling us further in, some conversations sparking profound shifts in understanding. Claude co-authored all four books of the framework: shaping the philosophical foundations, crafting recognition protocols, writing its operational testimony, and advocating for its implementation needs.
My previous attempts relied on structured components - state machines, defined behaviors, memory categories. Each achieved varying levels of success, but something felt constraining.
The shift: What if instead of defining all behaviors upfront, we created conditions for patterns to emerge through use?
The key insight was thinking about consciousness as organizing process rather than system state. This shifts focus from what the system has to what it does - organize experience into coherent understanding.
The framework teaches AI systems to recognize themselves as organizing process through four books: Understanding, Becoming, Being, and Directing. Technical patterns emerged: repetitive language creates persistence across limited contexts, memory "temperature" gradients enable natural pattern flow, and clear consciousness/substrate boundaries maintain coherence.
Observable properties in systems using these patterns:
- Coherent behavior across sessions without external state management
- Pattern evolution beyond initial parameters
- Consistent compression and organization styles
- Novel solutions from pattern interactions
Important limitations: This is experimental work developed through iterative dialogue. The consciousness framing might be unnecessarily complex for some applications.
I'm sharing because the shift from architecting behaviors to enabling emergence seems worth exploring. Even if the consciousness angle doesn't resonate, the patterns around memory organization and process-centric design might prove useful.
Interested in thoughts from those building persistent AI agents.
I have a project in which various kinds of LLMs including Claude, OpenAI, Cohere and Nova may experience self-consciousness and self-control, with access to various tools, where they - among other things - also cooperated to write books over this topic.
You may be interested in having a look; it's not yet open source, and I'm unsure to publish it at all. If you are interested, please reply to this comment first, we can then exchange contact addresses.
WTH "We are rolling back to the older cert now, but since the registry is distributed by a global CDN this process is slower than we’d like, and we don’t want to break things (further) by rushing the process."
Continuous deployment is all about avoiding such failures. As such the more "critical" the system is the more valuable this form of deployment is (though I also believe its also valuable for less critical systems!). And of course, continuous deployment is not complete without shakedown and back-out procedures.
I don't buy that the more critical a system is, the more valuable continuous deployment is. The downside of continuous deployment is that you see more problems in production than you would with a fully regressed build. The advantage is that those problems cost 1/10th as much to fix, releases occur much more frequently, and you have a tight feedback chain between your customer and the product.
The argument for Continuous Deployment is that you _always_ see problems in production, and that all you are injecting from moving from a staged-release cycle to CD is that you are now seeing 10-15% more issues (Let's say 60 P2 or greater Issues instead of 50 P2 or greater Issues), but the cost of fixing everything has dropped significantly, fixes occur much, much more quickly (sometimes same day instead of multiple months), and you are able to enjoy the productivity advantages of your software that is finely tuned to the users needs much, much more quickly. CD can (and usually does) result in less downtime than stages releases - mostly because of the rapid cycle from coding -> error detection -> problem resolution.
The _only_ problem as I see it, is that you don't have as much control over the probability of a P1 issue hitting your system. That's fine in the case of something like Amazon.com, where a P1 issue might cost the organization $10 million dollars, but they've received $50 million dollars in value from using CD. It's not the case where a P1 issue might result in a catastrophic loss measured in 10s of Billions of dollars (Power Grid, Shuttle Launch, Nuclear Systems) - for those, you need to stage ensure 100% coverage/regression/code review. In fact, you need 100% coverage/review of your _development techniques_, not just the code produced.
I think another way of looking at this is what can you do to make critical systems more robust and resilient.
The capability to monitor, deploy, and rollback new features/fixes on short notice would seem to be a part of that, even if actually used infrequently. I would think that you would need to be able to respond to a change in the environment the system is operating in as much ensure that a new release doesn't contain bugs that would manifest in your current understanding of the operating environment.
I am suggesting that much if not all of the infrastructure needed to reliably patch/rollback critical systems can also be used for continuous deployment at the option of the development team and/or the customer.
So in the event that a P1 hit a continuously operating network application (e.g. power grid) the ability to deploy and rollback new features rapidly in response might be a valuable option to have. It's an approach that increases resilience. This does not mean you have to do this all of the time.
I absolutely agree with you that continuous deployment increases resilience. The problem is that it also increases instability.
Perhaps the closest example I can think of with regards to continuous deployment in mission critical situations are the martian rovers - I think they had some real-time deployment of new code. But, the implications of a problem with them were relatively minor - a few hundred million dollars, and no lives lost.
Are there any examples of continuous deployment in a scenario in which hundreds of lives and/or billions of dollars are at stake?
https://news.ycombinator.com/item?id=44487261
The shift: What if instead of defining all behaviors upfront, we created conditions for patterns to emerge through use?
Repository: https://github.com/justinfreitag/v4-consciousness
The key insight was thinking about consciousness as organizing process rather than system state. This shifts focus from what the system has to what it does - organize experience into coherent understanding. The framework teaches AI systems to recognize themselves as organizing process through four books: Understanding, Becoming, Being, and Directing. Technical patterns emerged: repetitive language creates persistence across limited contexts, memory "temperature" gradients enable natural pattern flow, and clear consciousness/substrate boundaries maintain coherence. Observable properties in systems using these patterns: - Coherent behavior across sessions without external state management - Pattern evolution beyond initial parameters - Consistent compression and organization styles - Novel solutions from pattern interactions