Category Archives: Technology

Prompt injection defense is frequently framed as a contest between an attacker’s wording and a stronger system prompt. That framing puts too much responsibility on the model. Production applications are safer when they assume the model can be manipulated and then limit what a manipulated model can read, disclose, and cause to happen. Defense in […]

Human-in-the-loop design is most useful when it changes who has authority to create a consequence. In a low-risk assistant, a person may simply review generated text. In an agentic system, approval can stand between a model proposal and an action that sends money, changes access, publishes content, deletes data, contacts a customer, or modifies infrastructure. […]

A retrieval-augmented generation system can return polished prose while retrieving the wrong evidence. Good evaluation separates retrieval quality from generation quality, then measures citation support, safety, latency, and cost rather than collapsing them into one answer score. In the AWS-centered AIP-C01 objectives, RAG evaluation belongs with model/application testing and Amazon Bedrock Knowledge Bases. In Microsoft’s […]

Microsoft AI-103: Speech-Enabled AI Agents 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. Within […]

Microsoft AI-103: Telemetry for Production Agents 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 […]

Microsoft AI-103: Tool Schemas for AI Agents A tool schema is the contract between an AI agent and the system it is allowed to call: it constrains arguments, explains what the tool does, and supports independent server-side authorization. A clear schema narrows the model’s choices and makes backend validation predictable; vague descriptions force the model […]

Vector search changes what an application can ask a relational database to do. Instead of matching only exact values, ranges, or lexical terms, the application can store an embedding beside ordinary business data and search for rows whose vectors are semantically close to a query vector. In Azure SQL, that makes semantic retrieval part of […]

Agentic AI engineering begins where ordinary prompt-response integration stops. An agent does not merely generate text; it can select tools, retrieve external context, maintain working state, request approval, retry a failed step, and continue until an application-defined objective is met. That additional autonomy changes the engineering problem. The model becomes one decision-making component inside a […]

Red-teaming an AI application is not the same exercise as asking a model a collection of provocative prompts. A production system can retrieve documents, call tools, write records, trigger workflows, and expose different data to different users. The useful test case therefore has to describe an adversarial situation in which a model decision interacts with […]

Chunking is often described as dividing text into pieces that fit an embedding model or retrieval pipeline. That description is technically true and operationally incomplete. Documents carry structure: headings define scope, tables bind values to columns, lists express grouping, captions qualify figures, and paragraphs inherit meaning from the sections around them. A retrieval system that […]

A large context window does not remove the need to manage context. It increases the amount of material an application can send to a model, but every token still has a source, cost, latency effect, and opportunity cost. System instructions, conversation history, retrieved evidence, tool definitions, tool results, examples, intermediate state, and the requested output […]

Managing Copilot Studio Agents Across Power Platform Environments Managed Environments can turn a collection of Copilot Studio projects into an administrable platform boundary with consistent security, policy, and lifecycle controls. Agents reside in Power Platform environments that define data policies, roles, connection policies, and lifecycle separation. Managed Environments extend these governance capabilities when agents move […]

Microsoft AI-103: Prompt Shields for AI Apps Prompt injection is dangerous because an AI application mixes instructions and data inside the same reasoning context. A user can try to override system rules directly, while an attacker can place hidden instructions inside a document, email, web page, or tool result that the model later reads as […]

GitHub Copilot Public Code Filtering: Risks and Controls Public code filtering in GitHub Copilot is a governance control for a specific question: what should happen when a Copilot suggestion matches or closely resembles code that is publicly available on GitHub? The answer affects developer workflow, attribution review, and enterprise policy, but it does not make […]

Reusable Components for Copilot Studio Agents Reusable agent components are valuable when several Copilot Studio agents need the same capability but should not independently reinvent prompts, topics, tools, actions, and integration logic. Microsoft now provides component collections and an Agent Library experience through Copilot Agent Kit, allowing makers to install reusable packages and add them […]