Enable Persistent Memory and Learning for Specialized Agents

Title: Enable Persistent Memory and Learning for Specialized Agents

Description:

While Claude Code has excellent agent specialization through the Task tool (ui-translator, general-purpose, etc.), these agents are currently stateless and don't retain learning between sessions. This
complements existing agent improvement requests (#4182, #1770, #3013) by addressing the memory/learning dimension.

Current Agent Limitations:

• Agents spawn fresh each time with no memory of previous successful patterns
• Domain expertise must be re-explained in every Task tool invocation
• No accumulation of specialized knowledge over time
• Main Claude instance bears cognitive load for all agent-specific knowledge

Proposed: Agent-Specific Persistent Memory
Enable each agent type to maintain persistent memory similar to how main Claude uses CLAUDE.md files for guidance and learning.

Implementation Concepts:

1. Agent Memory Files: Each agent type gets its own persistent guidance (e.g., ui-translator-memory.md, code-reviewer-memory.md)
2. Cross-Session Learning: Agents remember successful approaches and terminology choices
3. Adaptive Guidelines: Agents refine their approaches based on task outcomes and user feedback
4. Domain Knowledge Building: Translation agents build vocabulary databases; code reviewers learn project-specific patterns

Concrete Example:
Currently, ui-translator requires this in every prompt:
CRITICAL: Use Unicode escape sequences (\uXXXX) for Java properties
Check existing translations for terminology consistency

With persistent memory, ui-translator would:

• Remember Java properties always need Unicode escaping
• Build terminology databases across languages ("bookmark" → "signet" in French, "favorito" in Spanish)
• Learn which translation approaches work best for different UI contexts
• Refine quality guidelines based on successful translations

Benefits:

• Reduces prompt overhead (less context needed per Task invocation)
• Better consistency across agent sessions
• True specialized expertise accumulation
• Complements existing agent hierarchy proposals (#4182, #1770)

Relationship to Existing Issues:

• Builds on agent specialization concepts
• Orthogonal to nested task spawning (#4182) and parallel execution (#3013)
• Addresses different aspect than general memory improvements (#87, #1813)
• Would enhance agent reliability mentioned in #3081

This would transform agents from "specialized prompt templates" into true persistent specialists with accumulated expertise.