Context Engineering

Opening — Why this matters now Enterprise teams didn’t adopt RAG to win leaderboard benchmarks. They adopted it to answer boring, expensive questions buried inside spreadsheets, PDFs, and contracts—accurly, repeatably, and with citations they can defend. That’s where things quietly break. Top‑K retrieval looks competent in demos, then collapses in production. The model sees plenty of text, yet still misses conditional clauses, material constraints, or secondary scope definitions. The failure mode isn’t hallucination in the usual sense. It’s something more procedural: the right information exists, but it never makes it into the context window in the first place. ...

Opening — Why this matters now Retrieval-Augmented Generation (RAG) has become the backbone of enterprise AI: your chatbot, your search assistant, your automated analyst. Yet most of them are curiously static. Once deployed, their retrieval logic is frozen—blind to evolving intent, changing knowledge, or the subtle drift of what users actually care about. The result? Diminishing relevance, confused assistants, and frustrated users. ...

Large models are passing tests, but they’re not yet passing life. A new paper proposes Experience‑driven Lifelong Learning (ELL) and introduces StuLife, a collegiate “life sim” that forces agents to remember, reuse, and self‑start across weeks of interdependent tasks. The punchline: today’s best models stumble, not because they’re too small, but because they don’t live with their own memories, skills, and goals. Why this matters now Enterprise buyers don’t want parlor tricks; they want agents that schedule, follow through, and improve. The current stack—stateless calls, long prompts—fakes continuity. ELL reframes the problem: build agents that accumulate experience, organize it as memory + skills, and act proactively when the clock or context demands it. This aligns with what we’ve seen in real deployments: token context ≠ memory; chain‑of‑thought ≠ skill; cron jobs ≠ initiative. ...