Forge hits 99% agentic accuracy for 8B models

Forge solves the reliability gap for local LLMs, proving that architectural guardrails are often more effective than model scaling for agentic tasks. It effectively commoditizes high-end tool-calling capabilities for self-hosted hardware. The framework utilizes rescue parsing to fix malformed JSON tool calls that typically crash 8B models and employs VRAM-aware context management to prevent GPU overflows. A drop-in OpenAI proxy enables these guardrails for tools like Aider without code changes, supporting accuracy gains from 53% to 99% on multi-step benchmarks. This research was presented at ACM CAIS '26 by Texas Instruments' AI lead.