Category Archives: AI & Machine Learning
Indirect prompt injection occurs when an AI system encounters instructions embedded in content that was not supposed to control the system. The attacker does not need to type directly into the chat. A malicious instruction can sit inside a webpage, email, document, issue tracker, retrieved knowledge-base chunk, image, or tool response and wait for an […]
Large language model output should be treated like input from an untrusted external component. It may be useful, well-formed, and usually correct, but it is still probabilistic text generated from data the application may not fully control. When an LLM response is displayed as prose, an error may be inconvenient. When the same response becomes […]
Prompt injection defense is not the search for one perfect instruction that makes a language model immune to manipulation. Prompt injection exists because models reason over natural language that can contain both legitimate tasks and adversarial attempts to redirect behavior. Direct attacks arrive through the user’s prompt; indirect attacks arrive through documents, webpages, email, retrieval […]
Retrieval-augmented generation is often evaluated with one deceptively simple question: did the final answer look correct? That is not enough. A RAG system is a chain of components—query understanding, retrieval, filtering, chunk selection, reranking, prompt assembly, generation, and citation—and a good final answer can hide weaknesses in any one of them. Conversely, a weak answer […]
A tool schema is the contract between an AI agent and the system it is allowed to call. Good schemas make the model’s decision space smaller and the backend’s validation job clearer; poor schemas force the model to infer hidden business rules from vague descriptions and then ask production APIs to tolerate mistakes. In Microsoft […]
Vector search in Azure SQL allows applications to keep embeddings next to relational data and query semantic similarity without automatically introducing a separate vector database. That can simplify architectures where the source of truth already lives in Azure SQL and retrieval needs to respect the same transactions, metadata, tenant keys, and operational controls. In Microsoft […]
Agentic AI engineering is the work of turning a language model into a dependable software system that can gather context, choose tools, preserve state, coordinate steps, ask for human approval, and produce outcomes that can be evaluated and operated. The model is only one component. Production behavior emerges from the contracts around it: instructions, retrieval, […]
A speech-enabled AI agent is not simply a text agent with speech recognition bolted onto the front and text-to-speech added at the end. Voice creates a real-time interaction loop where latency, interruption, turn detection, audio quality, identity, tool execution, and failure recovery all affect whether the experience feels trustworthy. In Microsoft AI Agents, Microsoft now […]
Production agents need more than application logs because one user request can become a chain of model calls, retrieval operations, tool invocations, approval steps, retries, and external side effects. Telemetry has to reconstruct that chain well enough for operators to answer what happened, why it happened, how long it took, what it cost, and whether […]
The quality of an MCP integration often depends less on the transport than on the contract of each tool. Claude sees a name, description, input schema, and eventually a result. From that limited interface it must decide whether the tool is appropriate, construct valid arguments, interpret the outcome, and determine the next step. In Claude […]
A multi-agent Claude system is useful when one conversation is no longer the right unit of work. Research, code analysis, incident investigation, document review, and other broad tasks can often be decomposed into independent workstreams that benefit from separate context and then recombined. In Claude Engineering, the important architectural decision is not how many agents […]
Parallel tool use can remove a surprising amount of latency from a Claude application, but only when the operations are genuinely independent. Claude may return several tool calls in one assistant turn, allowing the application to execute them concurrently and return all results together. In Claude Engineering, parallelism should be treated as a scheduling decision […]
Model routing in Microsoft Foundry is an architectural choice about how much model selection should happen dynamically at request time. Instead of binding every prompt to one fixed deployment, model router analyzes the request and selects an eligible model according to routing behavior and configuration. In Microsoft AI Agents, that can simplify applications that need […]
Multi-agent orchestration in Microsoft Foundry is becoming less about drawing a visually impressive workflow and more about choosing an execution model that can survive production constraints. Microsoft Foundry Agent Service can host and scale agents, while Microsoft Agent Framework provides orchestration patterns such as sequential, concurrent, handoff, group chat, and manager-driven coordination. In Microsoft AI […]
Multimodal document extraction is not simply OCR with a larger model. Business documents combine text, page layout, tables, selection marks, figures, charts, handwriting, headers, and spatial relationships that change meaning when flattened into a plain string. In Microsoft AI Agents, a strong extraction pipeline preserves document structure first, then applies generative reasoning only where it […]
