avantix
Cognitive infrastructure for AI agents
Agents that persist. Question. Improve.
LLMs don't learn. Systems do.
Large language models are frozen after training. They don't update from experience. What improves is the system around them — prompts evolve, signal reliability adjusts, thresholds tune, routing refines. The agent is the current expression of accumulated system knowledge. We build the system that learns.
Learning from truth
Every thought tracks its parent evidence. When evidence expires, the thought is invalidated and re-derived. Without this, conclusions persist after their evidence is gone — ghost reasoning that compounds silently. Run the simulation to see both modes side by side.
One of five
Truth maintenance is one cognitive loop. The full architecture runs five, at different speeds. Code handles detection. LLMs handle reasoning. Signals flow upward through typed channels on a shared bus. Most get filtered. The system gets quieter over time — expertise is knowing what to ignore.
hover each loop to explore
Cognitive depth
The same agent runs at different depths. Feature flags control which mechanisms are active — not different products, different configurations of the same system. Every level is ablation-tested against every other.
Pure code. Threshold checks against real data. The frozen watchdog — fast, cheap, can't be captured because it doesn't learn. Spike interrupts bypass everything when urgency demands it.
Watch a feed. Flag anomalies. Fire alerts.
same agent — different cognitive depth — one feature flag