Tools That Make AI Learn

Hierarchical Agent Loop Optimization. Trace, analyze, fix, redeploy. Recursively self-improving.

Adaptive learning with trajectory tracking, verdict judgment, memory distillation.

Microsoft: turn one-shot agents into learning agents with RL and prompt optimization.

Train any agent simply by talking. Reinforcement learning from conversation.

Self-evolving skill engine. 46% fewer tokens via learned patterns.

The sensor that closes the feedback loop. Scans code quality, scores it, agent improves, rescans — recursive self-improvement. Quality goes from 6/10 to 9.5/10 automatically.

Can a stock AI agent bootstrap an autonomous bug bounty system from scratch? Hermes Alpha hands a Nous Research agent a single identity document (soul.md) and challenges it to build, deploy, and continuously improve a second AI agent.

Procedural memory for AI agents. Three-layer cognitive architecture, confidence decay, anti-pattern learning.

Graph-first cognitive daemon v8.2. Persistent knowledge graph as source of truth.

Shared memory for teams. Every agent session compounds into a Karpathy-style wiki. 49% speedup.

Auto-captures everything Claude does, compresses with AI, injects relevant context back.

Karpathy-style persistent knowledge base. Self-healing, compounds over time.