Author Archives: Allen Rodriguez

Microsoft AI-103: Production Monitoring for AI Apps Production AI failures rarely announce themselves as “the model is broken.” Users report slow answers, inconsistent citations, missing tool calls, rising costs, or a sudden increase in refusals. Those symptoms can originate in the model, the retrieval layer, an agent tool, identity, rate limits, safety policy, data ingestion, […]

Microsoft AI-103: Responsible AI and Content Safety at Runtime Responsible AI becomes real at runtime. Principles, review boards, and policy documents matter, but users experience the system through individual prompts, retrieved documents, generated outputs, tool calls, and escalation decisions. A production architecture therefore needs mechanisms that turn policy into observable behavior: content filtering, access control, […]

Microsoft AI-103: Prompt Orchestration and Evaluation A production generative AI application rarely has one prompt. It has system instructions, user content, retrieved evidence, tool definitions, conversation state, policy messages, templates, routing decisions, and sometimes several model calls that transform one another’s output. Prompt engineering becomes prompt orchestration: deciding which context appears where, which step owns […]

Microsoft AI-103: Multimodal AI Under Real Constraints Multimodal AI combines more than one kind of input or output—text, images, audio, video, documents, or structured data—so an application can reason across information that humans naturally consume together. The compelling demo is easy: attach an image and ask a question. The production problem is harder because each […]

Microsoft AI-103: Tool Use and Function Calling Function calling gives a model a structured way to request work from software outside the model. The model does not execute the business operation by itself; it selects a named capability and proposes arguments, while application code validates the request, invokes the real system, and returns the result. […]

Microsoft AI-103: RAG on Azure: A Practical Mental Model Retrieval-augmented generation is often summarized as “search first, then ask the model,” but that shortcut hides the decisions that determine whether the system is useful. A production RAG application has to ingest source material, segment it, represent it for retrieval, enforce access rules, interpret a user […]

Microsoft AI-103: Foundry Project Design When a Microsoft Foundry project does not behave as expected, changing the prompt is often the wrong first move. The failure may sit in resource configuration, identity, networking, model deployment, connection setup, tool permissions, quota, or the transition between older hub-based patterns and the newer Foundry resource model. A disciplined […]

Microsoft AI-103: Foundry Agent Architecture Agent diagrams are easy to draw. A box for a model, a box for tools, a box for knowledge, and arrows between them can make an architecture look complete long before the difficult decisions have been made. A useful design review challenges what the diagram hides: identity, state, failure domains, […]

Human Oversight and Escalation for AI Agents “Human in the loop” sounds like a safe design principle until a team has to specify exactly which human, at what moment, with what evidence, and with what authority. An approval inserted everywhere can destroy the value of automation. An approval inserted nowhere can let a plausible model […]

Measuring AI Agent Value and ROI An AI agent can generate impressive usage numbers while creating very little business value. Conversations, sessions, tool calls, generated tokens, and active users show that a system is being exercised; they do not prove that work is completed better, faster, safer, or more profitably. A useful measurement model starts […]

ALM for AI Business Solutions Application lifecycle management becomes harder when an AI business solution is no longer just an application package. A production agent can depend on instructions, model deployments, connectors, actions, knowledge sources, environment variables, identities, policies, evaluation datasets, and downstream systems. A release can therefore look successful while changing only part of […]

Autonomous Agent Security: Identity, Tools and Data Autonomous agents change the security question from “what can this application display?” to “what can this software decide and do without asking first?” An agent may retrieve sensitive data, call APIs, create records, send messages, trigger workflows, delegate tasks to other agents, and operate outside a user’s active […]

Governing Enterprise AI Agent Portfolios The first enterprise agents are easy to govern because everyone knows they exist. The hundredth agent is different. Teams create departmental assistants, workflow agents, custom Azure agents, vendor agents, and experimental prototypes; ownership changes; duplicate capabilities appear; permissions accumulate; and nobody can answer confidently which agents can act on sensitive […]

Copilot Studio vs. Microsoft Foundry: Architecture Trade-offs Copilot Studio and Microsoft Foundry can both participate in enterprise agent solutions, but they start from different assumptions about who is building, how much control is required, and how much platform infrastructure the team wants to own. The wrong comparison asks which product is more powerful. The useful […]

Choosing Business Processes for AI Agents The fastest way to waste an agent project is to begin with the agent. A team sees a capable model, imagines automation everywhere, and then searches for a process that might justify the technology. Strong business-process discovery works in the opposite direction: it identifies a real operational problem, understands […]