Context engineering extends prompt engineering by curating the full set of information passed to an LLM during inference, enabling steerable, multi-turn agents while addressing limitations like context rot and attention budgets. It emphasizes managing system instructions, tools, data, and history to maintain effective behavior as context length grows.
Atlassian presents the Context Quality Score (CQS) to gauge AI context quality using completeness, connectedness, and relevance. When used with the Teamwork Graph, CQS helps identify data gaps and enables permission-aware, efficient AI-driven software development across the SDLC.
The paper treats context retrieval as a core issue in world models, introducing a benchmark and an agent-harness approach to compact context. It shows context compaction can extend usable context by up to four times without retraining, and argues that when dreaming, extending model reach via compaction outperforms switching to sliding-window attention.
OpenAI explains 12-hour prompt caching in the API, enabling persistent context reuse across a day with options for long or short caches and potential cost savings.
The guide explains how to connect Linkup to DeepSeek Harness through Composio Connect CLI or MCP. It presents the CLI as the easiest personal setup and notes that dsh includes a built-in MCP client.
Translated title. Original: Dauerhaft gespeicherter KI-Kontext bringt neue Datenebene | Computer Weekly
Enterprise AI memory is expanding from session-level features to a persistent, cross-platform context layer that spans teams and workflows; governance, distributed storage, and CIO data-management concerns shape its adoption.
Online courses from Coursera feature multiple institutions teaching AI, prompt engineering, software development, and tool integration, with varying durations and beginner to intermediate tracks. The programs emphasize hands-on skills, agentic workflows, and career growth opportunities, citing institutions like Vanderbilt University and Anthropic.
ACCT enables cross-LLM context transfer by converting source KV caches into sparse target KV states, reducing reprocessing and speeding up inference while preserving performance across various benchmarks and tasks.
The guide explains how context graphs connect business data, policies, and decisions so AI agents can use relevant context in their work. It introduces the concept through company examples and a customer renewal discount scenario.
The course teaches MCP server and client implementation, covering message passing, standardized communication, and production deployment. It explains how MCP connects language models such as Claude to external tools and data sources.