AI News
The Agent Problem AI agents are everywhere now. They're handling customer support tickets, qualifying leads, processing documents, making recommendations, executing workflows. The promise is compelling: autonomous systems that handle routine decisions so humans can focus on what matters. But teams deploying agents keep hitting the same problems: Inconsistent
AI News
* Companies have laid off tens of thousands of workers citing AI productivity gains, but 95% of enterprise AI projects deliver zero measurable ROI * Entry-level tech hiring has dropped by more than 50% since 2022, creating a talent pipeline crisis that will haunt the industry for a decade * The real story
Weekly Insights
The enterprise AI gold rush just hit a wall. While vendors promised transformation, data reveals a harsh reality: only 8.6% of companies have AI agents in production. The rest? Stuck in what industry insiders call "Pilot Purgatory." But something fundamental shifted this week. NVIDIA's Rubin
AI News
* AI agents can autonomously execute tasks like sending emails, querying databases, and processing payments without human verification - and they're doing it right now in production environments with minimal safety controls * Two guardrail architectures exist: fast pattern-matching checks that catch obvious violations instantly, and smart AI-powered evaluators that
AI News
* Traditional RAG systems retrieve documents once and generate answers, but adding a feedback loop can dramatically improve accuracy * Agentic RAG introduces intelligent checkpoints where an agent grades retrieved documents and rewrites queries when needed for better results * The architecture uses LangGraph for orchestration and Redis as the vector store, creating
AI News
The pattern is always the same: an AI agent completes step one, retrieves the wrong document, and every subsequent action is built on a broken foundation. I've watched teams spend months fine tuning prompts and swapping LLMs while the real culprit hides in plain sight. Retrieval is the
AI News
CTOs are racing to accelerate development velocity, and fully autonomous AI code generation is delivering real results. Agents that write entire applications while your team focuses on higher-value work are no longer theoretical. Thoughtworks just ran a rigorous experiment that shows exactly how to make this work, and the lessons
AI News
Engineering teams are drowning in pull requests that look correct but feel off. The code passes tests and follows style guides, yet reviewers sense something's missing. AI-assisted coding has fundamentally changed what's landing in your review queue, but the review process hasn't evolved to