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 ...

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 ...

There’s no denying the excitement around Model Context Protocol (MCP), an open protocol for connecting AI assistants with external data, tools, and APIs. Since its debut by Anthropic in late 2024, ...

A new framework from Stanford University and SambaNova addresses a critical challenge in building robust AI agents: context engineering. Called Agentic Context Engineering (ACE), the framework ...

2025 has seen a significant shift in the use of AI in software engineering— a loose, vibes-based approach has given way to a systematic approach to managing how AI systems process context. Provided ...

Jared Bowns is Head of Data and AI at Elyxor, helping enterprises turn emerging AI into scalable, real-world business value. Large language models (LLMs) have evolved from novelty to necessity in ...

While some consider prompting is a manual hack, context Engineering is a scalable discipline. Learn how to build AI systems that manage their own information flow using MCP and context caching.

Large language models (LLMs) make it possible to express symbolic structure directly in natural language, but most context engineering methods still assume that a human or an external pipeline already ...