As large language models (LLMs) become increasingly sophisticated, a new discipline is emerging that goes far beyond traditional prompt engineering: context engineering. This evolving practice ...

CARE-ACE supports autonomy through bounded agentic reasoning, in which diagnostic, prognostic, planning, and risk-assessment ...

Agentic AI systems need a deep understanding of where they are, what they know, and the constraints that apply. Context engineering provides the foundation. Enterprises have spent the past two years ...

Companies are realizing that higher AI productivity does not come from using bigger models, but rather from using AIs that understand the context they operate in. Context helps AI interpret ...

What if the key to unlocking truly intelligent AI isn’t just about asking the right questions, but about building the perfect environment for those questions to thrive? While much of the conversation ...

What if the AI tools you rely on could become not just smarter, but exponentially more effective? Imagine an AI assistant that doesn’t just follow instructions but intuitively understands your needs, ...

While prompt engineering will remain vital, getting consistent, situationally aware results from AI models will require IT teams to build context ingestion processes for agentic AI. Organizations ...

Four big lessons, seven practical tips, three useful patterns, and five common antipatterns we learned from building an AI CRM. Context engineering has emerged as one of the most critical skills in ...

The hottest discussion in AI right now, at least the one not about Agentic AI, is about how "context engineering" is more important than prompt engineering, how you give AI the data and information it ...

Today's enterprises need effective retrieval-augmented generation that extends existing data architectures without replacing current investments. As organizations face challenges in scaling RAG ...