understand-honglong-acquisition

AI acquisition workflows automatically learn from past mistakes by identifying failure and correction signals during tasks, then applying those signals to adjust routing for future runs without manual re-programming.

A three-layer memory system stores prioritized acquisition rules in HOT, WARM, and COLD layers for fast recall and long-term retention. It automatically promotes recurring patterns to HOT rules and demotes stale ones to keep knowledge current.

Enforce iron rules during customer discovery by loading a skill that integrates with your acquisition pipeline, enforcing tool usage requirements and strict operational constraints to ensure safe, effective workflows.

Yes, you can use this self-learning approach with HOLO-AGENT by invoking the skill via `skill://HOLO-AGENT`, capturing and applying learning signals to enhance end-to-end acquisition pipelines while enforcing the eight iron rules.

You should track correction, preference, pattern, failure, and optimization signals to optimize customer outreach. Capturing these specific cues enables the system to refine workflows and improve future acquisition results.