<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Applied Notes</title><description>Technical writing on software engineering, systems, and applied AI.</description><link>https://blog.ofdeme.com/</link><language>en</language><item><title>Evaluating a gpt-realtime Voice Agent with Simulation</title><link>https://blog.ofdeme.com/blog/simulation-eval-gpt-realtime-voice-agent/</link><guid isPermaLink="true">https://blog.ofdeme.com/blog/simulation-eval-gpt-realtime-voice-agent/</guid><description>How we test a speech-to-speech debriefing agent end-to-end: a persona-driven simulated technician talks to the real gpt-realtime agent over a WebSocket, and binary judges plus outcome verification grade the result.</description><pubDate>Mon, 17 Aug 2026 00:00:00 GMT</pubDate><category>Evaluation</category><category>evaluation</category><category>voice</category><category>gpt-realtime</category><category>simulation</category><category>azure</category><author>Omer Demir</author></item><item><title>Skill Evaluation with Microsoft Waza</title><link>https://blog.ofdeme.com/blog/waza-skill-evaluation/</link><guid isPermaLink="true">https://blog.ofdeme.com/blog/waza-skill-evaluation/</guid><description>A YAML-first, two-tier approach to evaluating AI skills: Waza validates trigger routing and output relevance at the skill layer, then implementation evals gate code quality — all in CI.</description><pubDate>Tue, 05 May 2026 00:00:00 GMT</pubDate><category>Evaluation</category><category>evaluation</category><category>waza</category><category>skills</category><category>ci</category><category>ai-agents</category><author>Omer Demir</author></item><item><title>Beyond 1–5 Scores: Binary Judges, Outcome Verification, and pass^k</title><link>https://blog.ofdeme.com/blog/binary-judges-outcome-scoring-pass-k/</link><guid isPermaLink="true">https://blog.ofdeme.com/blog/binary-judges-outcome-scoring-pass-k/</guid><description>Multi-point judge scales are subjective, transcript-only scoring misses system state, and a single trial can be a lucky run. Four changes that make agent evaluation trustworthy.</description><pubDate>Tue, 21 Apr 2026 00:00:00 GMT</pubDate><category>Evaluation</category><category>evaluation</category><category>llm-judge</category><category>ai-agents</category><category>reliability</category><category>calibration</category><author>Omer Demir</author></item><item><title>Hybrid Agent Evaluation: Custom Judges + Azure Foundry Evaluators</title><link>https://blog.ofdeme.com/blog/hybrid-agent-evaluation-custom-judges-azure-foundry/</link><guid isPermaLink="true">https://blog.ofdeme.com/blog/hybrid-agent-evaluation-custom-judges-azure-foundry/</guid><description>Why we combined domain-specific custom LLM judges with Azure Foundry&apos;s built-in agent evaluators over a REST API — getting behavior metrics and scenario-fact groundedness from C# without a Python dependency.</description><pubDate>Tue, 14 Apr 2026 00:00:00 GMT</pubDate><category>Evaluation</category><category>evaluation</category><category>azure-foundry</category><category>llm-judge</category><category>ai-agents</category><category>dotnet</category><author>Omer Demir</author></item><item><title>Eval-Driven Development: Red-Green-Refactor for AI Agents</title><link>https://blog.ofdeme.com/blog/eval-driven-development-for-ai-agents/</link><guid isPermaLink="true">https://blog.ofdeme.com/blog/eval-driven-development-for-ai-agents/</guid><description>Every agent change should be justified by a failing evaluation. A disciplined, TDD-style process for evolving AI agents without regressions.</description><pubDate>Fri, 10 Apr 2026 00:00:00 GMT</pubDate><category>Evaluation</category><category>evaluation</category><category>ai-agents</category><category>testing</category><category>llm-judge</category><category>process</category><author>Omer Demir</author></item><item><title>Hybrid Activity Classification: Keyword Rules First, LLM Fallback</title><link>https://blog.ofdeme.com/blog/hybrid-activity-classification-keyword-llm/</link><guid isPermaLink="true">https://blog.ofdeme.com/blog/hybrid-activity-classification-keyword-llm/</guid><description>A two-tier classifier that answers the easy cases with deterministic keyword rules and escalates only the ambiguous ones to an LLM — cutting cost and latency while keeping a confidence threshold in control.</description><pubDate>Thu, 02 Apr 2026 00:00:00 GMT</pubDate><category>AI Agents</category><category>classification</category><category>llm</category><category>azure-openai</category><category>cost-optimization</category><category>multi-agent</category><author>Omer Demir</author></item><item><title>Real-Time Voice Debriefing with the Azure Voice Live API</title><link>https://blog.ofdeme.com/blog/real-time-voice-debriefing-voice-live-api/</link><guid isPermaLink="true">https://blog.ofdeme.com/blog/real-time-voice-debriefing-voice-live-api/</guid><description>How the field service assistant runs a hands-free, 8-step voice debrief over a persistent WebSocket loop — and how persona, scenario, and environment fixtures make it testable.</description><pubDate>Tue, 24 Mar 2026 00:00:00 GMT</pubDate><category>AI Agents</category><category>voice</category><category>azure</category><category>realtime</category><category>multi-agent</category><category>testing</category><author>Omer Demir</author></item><item><title>Designing a Multi-Agent Field Service Assistant</title><link>https://blog.ofdeme.com/blog/multi-agent-field-service-assistant-architecture/</link><guid isPermaLink="true">https://blog.ofdeme.com/blog/multi-agent-field-service-assistant-architecture/</guid><description>The architecture of a field service assistant built with FastAPI and Pydantic v2: specialized briefing, debriefing, and classification agents behind an orchestrator, with swappable connectors and persistence.</description><pubDate>Tue, 10 Mar 2026 00:00:00 GMT</pubDate><category>AI Agents</category><category>multi-agent</category><category>fastapi</category><category>pydantic</category><category>azure-openai</category><category>architecture</category><author>Omer Demir</author></item><item><title>Four Chunking Strategies for Structured Documents in RAG</title><link>https://blog.ofdeme.com/blog/chunking-strategies-structured-documents/</link><guid isPermaLink="true">https://blog.ofdeme.com/blog/chunking-strategies-structured-documents/</guid><description>A pluggable chunker design — simple, markdown, section-aware, and semantic — for splitting structured documents before vector indexing, with metadata that survives retrieval.</description><pubDate>Thu, 18 Sep 2025 00:00:00 GMT</pubDate><category>RAG</category><category>rag</category><category>chunking</category><category>embeddings</category><category>retrieval</category><category>python</category><author>Omer Demir</author></item><item><title>Wiring ONNX Runtime GenAI into a LangChain RAG Pipeline</title><link>https://blog.ofdeme.com/blog/slm-rag-langchain-onnx-runtime-genai/</link><guid isPermaLink="true">https://blog.ofdeme.com/blog/slm-rag-langchain-onnx-runtime-genai/</guid><description>Part two: a custom LangChain LLM wrapper over ONNX Runtime GenAI, streaming token generation, and building an optimized Phi-3 ONNX graph for offline Windows RAG.</description><pubDate>Fri, 13 Jun 2025 00:00:00 GMT</pubDate><category>Machine Learning</category><category>slm</category><category>onnx</category><category>rag</category><category>langchain</category><category>windows</category><author>Omer Demir</author></item><item><title>Choosing an On-Prem SLM Inference Engine for Windows</title><link>https://blog.ofdeme.com/blog/slm-onnx-runtime-genai-windows-benchmark/</link><guid isPermaLink="true">https://blog.ofdeme.com/blog/slm-onnx-runtime-genai-windows-benchmark/</guid><description>How we benchmarked ONNX Runtime GenAI, LlamaCPP, Hugging Face Optimum, and Triton to run a sub-5-second RAG pipeline offline on Windows.</description><pubDate>Fri, 30 May 2025 00:00:00 GMT</pubDate><category>Machine Learning</category><category>slm</category><category>onnx</category><category>rag</category><category>inference</category><category>windows</category><author>Omer Demir</author></item><item><title>Managed Identity on Logic App Consumption with Terraform</title><link>https://blog.ofdeme.com/blog/managed-identity-logic-app-consumption-terraform/</link><guid isPermaLink="true">https://blog.ofdeme.com/blog/managed-identity-logic-app-consumption-terraform/</guid><description>Terraform does not natively wire Managed Identity API connections for Logic App Consumption. Here is how azapi_resource plus an ARM template deployment closes the gap.</description><pubDate>Fri, 31 Jan 2025 00:00:00 GMT</pubDate><category>Azure</category><category>terraform</category><category>logic-apps</category><category>managed-identity</category><category>azure</category><category>iac</category><author>Omer Demir</author></item></channel></rss>