this isn’t an “agent” or chatbot. It’s a cognitive substrate I’ve been building for the last year that behaves more like an operating system for model orchestration.
A few useful details for people who asked for specifics:
• It has memory (hot/cold tiers, reflection, doctrine learning)
• It self-heals (auto-heal cycles, failure circuit breakers, shadow deployment)
• It mutates and upgrades itself via a component called the Modernizer
• It proposes patches and tests them in shadow before production
• It has a telemetry layer (vision) that treats cognition like observability
• It has adapters for SAP/Workday/Databricks/etc. so it can operate in enterprise environments
• Dream cycles run background learning when the system is idle
The logs in the post are real runtime output from v4.2.0. This build is running on top of Postgres + Redis + RabbitMQ + S3 + an LLM router (20+ providers). It currently has 12 modules, 160+ commands, and a 100% health score on this cycle.
Current research question is:
what’s the right abstraction for turning model capabilities into durable software infrastructure? My hypothesis is that you don’t need bigger models for autonomy, you need better orchestration.
Happy to answer technical questions here. No sales motion, nothing to buy, not trying to funnel traffic — genuinely interested in feedback from people who have built distributed systems, orchestration layers, and observability pipelines.