Welcome to the 288th edition of Coding Jag brought to you by TestMu AI!👐
Agentic AI is no longer a proof of concept. Teams are deploying it, and the 80% reductions in manual work are real. But so are the security gaps, the silent failures, and the infrastructure that was never built to handle it.
This edition covers what is actually happening right now. Microsoft shipped an open-source framework that pinpoints the exact step where an agent fails. Anthropic introduced a safer way to run Claude Code autonomously. And OpenAI shut down Sora six months after launch, the compute costs, the deepfake controversies, and the $1 billion Disney deal all went with it.
The theme is execution. Not what agents can theoretically do, but what happens when you deploy them. This edition also covers quantum-resilient AI infrastructure, LLM observability tooling, and why India is emerging as the world's largest real-world sandbox for agentic experimentation.
📬 Found something useful or interesting? Hit reply and let's share perspectives.
News
12 min
microsoft.com
🔬 Shraddha Barke, Arnav Goyal, Alind Khare, and Chetan Bansal from Microsoft Research introduce AgentRx, an open-source framework that pinpoints the exact step where an agent trajectory becomes unrecoverable. It synthesizes executable constraints from tool schemas and policies, producing an auditable violation log with a 23.6% improvement in failure localization.
08 mintechcrunch.com
📉 Amanda Silberling at TechCrunch covers OpenAI's decision to shut down Sora, the TikTok-style AI video app that peaked at 3.3 million downloads in November before declining to 1.1 million by February. Weak moderation, deepfake controversies, and high compute costs made it a liability, and the $1 billion investment and licensing deal allowing Sora to generate videos with Disney, Marvel, Pixar, and Star Wars characters collapsed with it.
12 minartificialintelligence-news.com
🔐 AI News covers Utimaco's report on quantum resilience for AI, arguing organisations must prepare now for cryptographic vulnerabilities that quantum systems could exploit within the decade. It recommends crypto-agility, hardware-based data enclaves, and a chain of trust from hardware to application across the full AI lifecycle.
AI
10 minmlflow.org
🧪 MLflow maintainers teams share a methodology for testing Claude Code skills using MLflow tracing and LLM-based judges that evaluate actual tool call sequences rather than fixed outputs. When a judge fails, the trace and rationale feed back to Claude, which rewrites the skill itself, creating a fully automated refinement loop.
12 minclaude.com
⚙️ Anthropic introduces auto mode in Claude Code, a middle path between manual approval prompts and the risky - - dangerously-skip-permissions flag. A classifier reviews every tool call before it runs, blocking destructive actions like mass file deletion or data exfiltration while letting safe ones proceed automatically.
09 minlangwatch.ai
🔍 Manouk Draisma introduces LangWatch Skills, installable skill files that give coding agents like Claude Code and Cursor native knowledge of instrumentation, observability, and evaluation workflows without repeated context setup. Three skills cover the full loop: tracing agent behavior, observing production performance, and fixing regressions with auto-generated eval criteria.
11 mindevassure.io
🎭 Divya Manohar, Co-Founder and CEO of DevAssure, walks through building a Python-based testing agent that takes plain-English YAML test specs and uses Claude's tool-use API to autonomously navigate a live browser, reason about page content, and report structured results. The shift from mechanism to intent means UI changes no longer break the suite.
08 minunite.ai
🛡️ Steve Povolny, VP of AI Strategy at Exabeam, argues that rising AI security budgets are applied to fragmented task-level tools rather than the integrated workflows where AI drives business decisions. Securing disconnected experiments expands complexity without reducing real risk. The fix is aligning investment to systems that influence planning and outcomes.
10 mintestmuai.com
🤖 Salman Khan at TestMu AI covers the full arc of AI chatbots, from transformer architecture and RAG-based retrieval to four deployment types: autonomous agents, generative chatbots, small language models, and hybrid systems. The guide closes with why testing agentic workflows matters, covering hallucination rates and MCP permission boundary validation.
Automation
09 minlyzr.ai
⚙️ The Lyzr Team details how a global insurance advisory firm replaced manual cross-system billing validation with an agent-orchestrated pipeline. Agents now retrieve and validate data across six enterprise systems, detect exceptions in real time, and create structured tickets automatically, delivering an 80% reduction in manual validation steps.
08 mintestfort.com
🛠️ Olexandra Baglai at TestFort maps out how hyperautomation moves enterprise QA beyond brittle scripts by combining AI agents, RPA, and low-code platforms into a system that self-heals, prioritizes by risk, and diagnoses failures autonomously. The guide includes a five-level maturity model and benchmarks showing 60-80% reductions in test maintenance effort.
12 mintestmuai.com
⚙️ Saniya Gazala at TestMu AI explains how n8n functions as an orchestration layer around existing test frameworks, coordinating triggers, result processing, conditional routing, and AI-driven failure analysis. The guide includes a webhook-based CI/CD integration, a working API health check workflow, and a no-code to code-based breakdown.
Tools
10 minopenobserve.ai
📊 Simran Kumari at OpenObserve breaks down ten open source LLM observability tools across tracing, evaluation, prompt management, and cost tracking, explaining why traditional monitoring falls short for catching hallucinations and silent quality regressions. The guide includes a decision framework for matching the right tool to your primary bottleneck.
Video & Podcast
12 miney.com
🎧 In this episode of EY.AI Unplugged, Rohit Pandharkar, Partner at EY India Technology Consulting, explains why India's 1.4 billion population, digital public infrastructure, and mobile-first adoption make it a natural sandbox for agentic AI at scale. He covers RBAC governance for AI agents as non-human workers and the emerging threat of prompt injection and leakage attacks.
06 minyoutube.com
🎥 In this video, Execute Automation addresses why running OpenClaw locally exposes agent credentials and workflows to unnecessary security risk, and shows step by step how to move to a Hostinger VPS for a production-ready, locked-down environment. A practical setup guide for teams whose infrastructure needs to match the reliability of their agents.
Events
11 minstareast.techwell.com
🎟️ STAREAST 2026 runs April 26 through May 1 in Orlando, FL, with a virtual attendee option available. The program covers AI/ML testing, test automation, security, and responsible AI governance, with keynotes from Kristel Kruustük of Testlio on quality standards for AI systems and speakers from Meta, Amazon, Microsoft, Red Hat, and more.