MemoHarness dynamically configures agent harnesses

Static agent harnesses are a major bottleneck for LLM applications; by turning the harness into a dynamic, learning control layer, MemoHarness offers a practical, weight-free path to specialized and self-corrective AI agents.

• –**Zero-Weight Specialization:** Optimizes agent performance dynamically at runtime without the high computational costs and overhead of fine-tuning or training LLMs.
• –**Dual-Layer Experience Bank:** Combines case-specific execution diagnostics with distilled global patterns to allow agents to learn from errors.
• –**Granular Harness Adaptation:** Decomposes control into context, tools, generation control, orchestration, memory, and output processing for targeted adjustments.
• –**Graph-Based Correction:** Facilitates complex, graph-based error correction to break loops and handle edge cases more reliably.