Meet the operator behind the blueprints
Ahmad Lala
Ahmad works in AI agent operations at G42. Before that, he spent 17 years working in communications and software development. He builds and maintains AI workflows in production daily and writes the AI blueprints he wishes someone had given him when he started. Every guide on this site is AI-assisted and human-tested. All articles reflect his personal opinions and thoughts and do not necessarily represent those of his employer (G42).
Featured AI Research Reports
Anthropic shipped ten production finance agents on 5 May 2026, with Microsoft 365 integration, Claude Opus 4.7 leading Vals AI Finance Agent v2 at 64.37%, and the full repo open-sourced. Here is the honest operator breakdown – what each agent does, which deployment mode to pick, what early practitioners are flagging, and which role should touch this when.
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Anthropic shipped ten production finance agents on 5 May 2026, with Microsoft 365 integration, Claude Opus 4.7 leading Vals AI Finance Agent v2 at 64.37%, and the full repo open-sourced. Here is the honest operator breakdown – what each agent does, which deployment mode to pick, what early practitioners are flagging, and which role should touch this when.
Thinking Machines Lab unveiled interaction models with 0.4s latency and simultaneous audio-video-text. Here’s how the dual-model architecture works, benchmarks vs GPT and Gemini, and what it means for enterprise.
An interaction model is an AI system that listens, watches, and speaks in continuous 200-millisecond beats – instead of waiting for your turn to end before thinking. A plain-English guide to what Thinking Machines just announced, with benchmarks, architecture, demos, and what it means for your AI stack.
AI agent tooling is getting easier. Career value moved up the stack: workflow design, domain literacy, evals, cost judgment, trust design, rollout skill. 7 skills, scorecard, pricing comparison, and field notes.
A practitioner-led roundup of the advanced AI tools worth watching after the obvious stack – from Perplexity Computer and Claude Projects with MCP to Gumloop and ExoClaw.
A practitioner framework for evaluating AI agents: a 21-point scorecard across 7 pillars (completion, accuracy, tool use, trajectory, reliability, latency/cost, safety), a three-test loop you can run in an afternoon, an interactive calculator, and a comparison of every major eval tool in 2026. Built for U.S. teams shipping agents at small and mid-sized companies.
Agent washing is the new greenwashing – vendors slapping ‘AI agent’ on chatbots and scripts. Use this 4-point checklist to separate real agentic AI from expensive marketing.
