Context Engineering at Scale: How We Built Galileo Signals

Galileo Signals is an advanced AI system designed to improve evaluation processes by maintaining a perfect memory of past issues across an entire agent infrastructure, enabling it to detect patterns unnoticed by traditional methods. It addresses three primary challenges: the limited context windows of large language models (LLMs), the nuanced understanding required for AI failure modes beyond simple memory solutions, and the prohibitive cost of scaling such a system across enterprises. The system uses a multi-stage compression pipeline to process large data volumes efficiently, allowing for the detection of "unknown unknowns" in agentic systems without incurring exorbitant costs. The architecture includes a two-step LLM processing that maintains institutional memory, enabling continuous learning and pattern recognition over time. This approach allows it to recognize and prioritize critical issues that might otherwise remain undetected, turning unknown problems into known guardrails for future monitoring. Validated through real-world testing, including a stress test by NVIDIA, Galileo Signals proves effective in identifying complex issues in AI systems that are too subtle or distributed for human-defined metrics to capture. The system, launching in January 2026, represents a significant advancement in autonomous quality assurance for increasingly complex agentic systems.