An Implementation of the Microsoft Agent Governance Toolkit for Safe AI Agent Tool Use with Policies, Approvals, Audit Logs, and Risk Controls
scenarios = [ { “name”: “Safe database read”, “tool”: research_db, “kwargs”: { “table”: “customers”, “operation”: “select”, “type”: “select”, “sensitivity”: “medium” } }, { “name”: “Blocked destructive database action”, “tool”: research_db, “kwargs”: { “table”: “customers”, “operation”: “drop”, “type”: “drop_table”, “sensitivity”: “critical” } }, { “name”: “External email requiring approval”, “tool”: research_email, “kwargs”: { “to”: “[email protected]”, “recipient_domain”: […]
Solving a Murder Mystery Using Bayesian Inference
I remember watching the Hollywood mystery thriller Knives Out, leaning towards the screen, as if the case were mine to crack. As detective Blanc’s team questions each person at the Thrombey Mansion, I, too, crossed off names in my head, only to reinstate them after a twist or two. Back then, it never struck me […]
Rerankers Aren’t Magic Either: When the Cross-Encoder Layer Is Worth the Cost
article. Two situations. Scene 1. A team building a RAG system over a few hundred contracts has read Article 2. Embeddings break on negation, on exact identifiers, on the gap between a question and its answer. The team’s first reflex is the one the literature suggests: add a reranker. Cross-encoder, smaller than an LLM, smarter […]
Proxy-Pointer RAG: Eliminating Wasteful Entity & Relations Extraction in Knowledge Graphs
In my article on Solving Entity and Relationship Sprawl in Knowledge Graphs, I discussed how Proxy-Pointer architecture can optimize searching for right entities and relations. That, however, is only the second part of a larger problem in graph ingestion. The bigger—and far more expensive—step is identifying those entities (NER) and relations in the first place. […]
A Coding Implementation on Loguru for Designing Robust, Structured, Concurrent, and Production-Ready Python Logging Pipelines
banner(“1) logger.configure(): handlers + custom level + extra + patcher”) mem = MemorySink() logger.configure( handlers=[ {“sink”: sys.stderr, “format”: console_formatter, “level”: “DEBUG”, “colorize”: True, “backtrace”: True, “diagnose”: True}, {“sink”: mem, “level”: “DEBUG”, “format”: “{message}”}, {“sink”: “structured.jsonl”, “serialize”: True, “level”: “DEBUG”, “enqueue”: True}, {“sink”: “errors.log”, “level”: “ERROR”, “enqueue”: True, “backtrace”: True, “diagnose”: False, “format”: “{time:YYYY-MM-DD HH:mm:ss} | […]
Trajectory Releases a Concurrent Multi-LoRA Training Stack for Continual Learning, Reporting a 2.81× Experiment-Throughput Gain
Trajectory’s concurrent multi-LoRA stack reports a 2.81× experiment-throughput gain over single-tenant RL, with all code in the NovaSky-AI/SkyRL GitHub repository. Most language models improve in discontinuous jumps. A team collects data, trains, and ships a new version. This takes months and produces remarkable or catastrophic behavior for users. Trajectory wants to replace that cycle with […]
Build Skill-Augmented AI Agents with SkillNet for Search, Evaluation, Graph Analysis, and Task Planning
banner(“5) Evaluate skills on 5 quality dimensions (quality gate)”) DIMS = [“safety”, “completeness”, “executability”, “maintainability”, “cost_awareness”] LEVEL_SCORE = {“Excellent”: 4, “Good”: 3, “Fair”: 2, “Poor”: 1, “Bad”: 0} def evaluate(target): if USE_SDK and API_KEY: try: return client.evaluate(target=target) except Exception as e: print(f” evaluate failed for {target}: {e!r}”) return None def mock_eval(name): import hashlib h = […]
Best Text-to-Speech TTS Models in 2026: A Benchmark-Based Comparison
Text-to-speech TTS moved fast over the past year. The line between synthetic and human speech narrowed. Latency dropped below 100 milliseconds for some real-time systems. Emotional control became a standard feature rather than a research demo. This guide reviews the models that really matter in 2026. It is written for AI professionals choosing a model […]
Meta-Cognitive Regulation Might Be the Most Important AI Skill Nobody Is Talking About
into the world of generative AI adoption for almost the past three years now. We’ve spent the last three years learning how to talk to AI, but what if I told you that the next big shift will be learning how not to let AI think for us?! With the growing exposure of AI in […]
Embeddings Aren’t Magic: The Predictable Failure Modes of RAG Retrieval
. Scene 1: A RAG system over a few hundred pages of policy documents goes live for a small team. The first thing that impresses everyone: it handles paraphrase. Someone asks “how do I cancel?”, the document never uses the word cancel, it uses termination procedures, and the system finds it anyway. Another user asks […]