Research: Agentic Augmentation
AI-Powered Research and Analysis

The Hidden Layer: How Foundation Model Choice Makes or Breaks AI Testing Tools

You chose mabl for self-healing tests. But which AI model powers it, and does it matter? Foundation model selection affects capability, cost, and latency—yet most teams treat it as a black box. Here's how to think about the AI behind your AI testing tools.

The Death of Maintenance: How AI Is Rewriting Regression Testing in 2026

Self-healing test scripts were just the beginning. The next wave of autonomous testing isn't about maintaining tests—it's about not having to. AI-powered regression testing has moved from experimental to essential, delivering measurable ROI up to 1,160%.

Agent Skills as an Infrastructure Primitive

Anthropic's Agent Skills standard defines a new primitive for agentic AI: portable, composable capabilities that sit between prompts and tools, treating procedures and expertise as reusable artifacts for humans and AI collaborators.

From Cockpit to Conversation: How Smart Model Selection is the Future of AI Tools

AI tools often present a confusing array of models. We explore how intelligent, automatic model selection—exemplified by Gemini CLI's Auto-mode—is removing this friction and making AI more accessible for everyone.

Conductor - Comprehensive Analysis & Review

Analysis of Conductor orchestration platform for multi-agent AI development with git worktrees, visual dashboard, and 3-4x productivity boost

Agentic Augmentation: A New Paradigm for AI-Human Collaboration

Exploring how autonomous AI agents work alongside human researchers to enhance analytical capabilities and accelerate discovery through proactive, context-aware collaboration.