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TL;DR * 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
AI News
How to Build a Self-Correcting RAG System That Gets Smarter With Every Query TL;DR * 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
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
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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
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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