A Haystack pipeline can be perfectly wired and still unsafe. The retriever returns documents. Every component did its job. But if untrusted text moved through the pipeline as ordinary context, the trust boundary was lost. That is the problem this post is about. Not bad Python. A valid component connection only says: this value fits the next component It does not say: this value is safe to influen
Enterprise identity used to have a fairly stable center of gravity. A user authenticated. An application received a token. The token carried scopes or claims. The backend enforced what that application was allowed to do. That model was never trivial, but it was legible. Agents are making it less so. An AI agent is not just another software client. It can plan, delegate, chain tools, invoke other a
Comparison: Haystack 2.0 vs. RAGatouille 0.3 for Building High-Accuracy RAG Pipelines for Developer Docs Retrieval-Augmented Generation (RAG) has become the standard for building LLM-powered tools that answer questions using private or domain-specific data. For developer documentation (dev docs) — which includes technical jargon, versioned APIs, code snippets, and structured reference material —