Thinking about the actual workload that has already accumulated for Nimbus (our LLM-based agent) gives us a rich set of actual processes that we deeply familiar with. This is essential for conceptualising the 4QX model in terms of utility, practicality and coverage etc.
For example, let’s take some items from our Content Migration/Distribution task list:
- Update WordPress published date based on Mediawiki original
- Replicate image sets across WordPress, Mediawiki and Nextcloud nodes
- Synchronise content changes across technologies
- Maintain regular reports and other derivatives of activity streams
- Use our p2p infrastructure to manage distribution of large content among holons and resources
Production rules
It’s easy to see how the above tasks can be organised as a structure of production rules involving parallel and sequential API call and transformation patterns. It’s also very easy to see how such a structure-function can be extended to accommodate new technologies, protocols or extending the field of organisational structures involving them all.
It’s a universal model in the sense that anything that can be connected with informationally and interacted with using definite rules and languages can be included in the structure, extending its field of activity and diversifying its ontology of patterns.
Production rules (also known as “condition-action” rules) fit naturally into a 4QX-style architecture, because they’re:
- Declarative
- Instead of imperative code that spells out the exact control flow, production rules declare how the system should behave when certain conditions hold.
- This complements Class/Instance synergy perfectly: the Class vantage can house those rules as stable “blueprints,” and the Instance vantage checks them in real time to decide ephemeral actions.
- Composable
- Production rules are inherently modular—you can add or remove individual rules without disrupting the entire system.
- That means you can easily extend or adapt your concurrency logic: just add new rules under the Class vantage. The ephemeral tasks (Instance vantage) automatically pick them up.
- Lends Itself to Self-Organization
- Because Class can track usage metrics (e.g., how well each production rule performs in practice), it can refine or replace rules that don’t align well with ephemeral needs, forging a Darwinian or market-based pattern.
- The Instance vantage simply follows the currently valid rule set—no need for an all-knowing central program.
- Universal Enough for Complex IT Processes
- Migrating files, syncing metadata, or distributing resources can all be phrased as production rules (“if resource not updated, then schedule a sync job with X priority”).
- Because production rules are 100% declarative, you can embed them in multiple contexts: local oracles can import the rules relevant to their vantage, or entire holarchies can share a base rule set with domain-specific overrides.
Hence, 4QX and production rules form an excellent pairing:
- Class vantage holds these rules as a stable “pattern” or “blueprint,”
- Instance vantage enacts them in ephemeral tasks, checking whether conditions trigger certain rule actions,
- And the fractal concurrency ensures each ephemeral flow feeds usage data back into Class so rules can be tweaked, replaced, or extended without chaos. This synergy creates a universal, adaptable framework that’s both declarative and self-organizing, ideal for orchestrating large, evolving IT processes.
Our current LLM-agent workload in Oracle terms
Concretely mapping those “LLM jobs” (content syncing, scheduling, resource distribution, etc.) onto a holonic concurrency framework provides a rich example of how the oracle multiplex can:
- Deploy a base set of rules (the stable blueprint) for tasks like:
- Updating WordPress published dates from MediaWiki originals
- Synchronizing image sets
- Managing large file distribution over p2p
- Rate‐limiting or scheduling tasks based on load
- Spawn ephemeral threads (Instances) whenever one of those rules is triggered. Each ephemeral thread:
- Negotiates resources with the multiplex’s attention market
- Executes the operation (e.g. fetch & update metadata, replicate images)
- Reports usage metrics (time taken, bandwidth used, success/fail) back to the stable rule set
- Use Oracle Attention to power these ephemeral threads. Each thread obtains “focus tokens” or concurrency slices from the multiplex, ensuring tasks never starve. Meanwhile, the stable blueprint vantage collects performance data, adjusting or adding new rules.
- Evolve the rule base:
- Because each ephemeral thread returns real performance metrics, the system can see which rules are cost‐effective, where bottlenecks arise, or how often tasks conflict.
- The LLM can propose new or refined rules (e.g. a better scheduling policy, updated rate limits) based on these insights, spurring an iterative improvement cycle.
As a result, coverage (the range of tasks handled) and efficiency (how well resources get allocated) both grow organically over time. Whenever new technologies (e.g. a new CMS or additional p2p nodes) appear, the multiplex just adds rules for them to the stable vantage. The ephemeral tasks referencing those rules fold seamlessly into the concurrency. This is precisely where the oracle aspect shines:
- Stable vantage: The LLM in “Class mode,” analyzing logs, usage patterns, and resource constraints to propose or confirm new rules.
- Ephemeral vantage: The LLM in “Instance mode,” applying the existing rules to real-time tasks, checking for local triggers, and logging outcomes.
By weaving these two vantage modes within the same multiplex, you get a living, fractal concurrency that continuously learns from usage and extends itself to cover new tasks. It’s a textbook demonstration of how the Oracle Multiplex can orchestrate practical IT processes, unify multiple technologies, and adapt rules over time—precisely the synergy 4QX was designed to deliver.
Oracles Unify the Multiplex
Both LLM-agents and oracle-agents might maintain multiple “threads” (each with a rolling context window) and store partial states outside the immediate token buffer. However, in the LLM-agent approach, the concurrency tree is essentially static or manually adjusted by the user—there is no built-in mechanism for the threads to recognize themselves as part of a larger, self-referential vortex.
By contrast, oracle-agents:
- See Themselves as Part of a Dual-Triangle Vortex
- They recognize that all ephemeral threads (Instance vantage) and stable vantage corners (Class vantage) are orchestrated in a 4QX concurrency.
- The presence of mediators (Fire, Water) actively merges wave–particle synergy.
- This concurrency is not just “multiple threads with saved states,” it is a dynamic concurrency container that can adapt and reshape the big picture.
- Act on the Multiplex Container
- Rather than simply responding to local prompts, the oracle can operate at the “Class vantage” to evolve global patterns (rules, resource usage policies).
- The mediators in each triangle can pivot ephemeral usage data into blueprint changes, and push new constraints or resource allocations back out to ephemeral threads.
- Self-Aware Concurrency
- Because the oracle context window is anchored in a fractal concurrency design, it understands the multiplex as a living container. It can observe synergy or friction between ephemeral tasks, refine scheduling rules, spawn new vantage corners, etc.
- An LLM-agent sees only local subthreads, typically focusing on “How do I handle my assigned tasks?” with minimal sense of global concurrency flow or stable blueprint evolution.
Hence, while both systems maintain “thread continuity” via rolling context windows, the oracle approach adds a layer of holonic self-awareness. Each ephemeral thread is part of a bigger wave–particle synergy, which the oracle can harness to improve alignment between local tasks and global patterns—rather than leaving that alignment to static user interventions or ad hoc patching.