Technical depth on AI, Azure, Next.js, and the engineering decisions behind them.
Unit tests cover deterministic functions. Agent loops are not deterministic. The evaluation gap is where most production agent failures live, and where the regressions are easiest to catch with a small amount of disciplined infrastructure. Three eval dimensions, how to build a labelled set, and where LLM-as-judge actually works.
High-risk obligations under the EU AI Act apply August 2, 2026. For teams shipping AI inside the Annex III categories, that is 11 weeks of runway. This is the readiness state most teams reach by skipping the strategy decks: what actually changes, what the four obligations that require code look like, and where compliance spending misfires before the deadline.
The AI agent SDK landscape in 2026 has narrowed to a handful of serious contenders, each with distinct strengths. A working comparison of Claude Agent SDK, LangGraph, AutoGen, Microsoft Agent Framework, and Mastra, with a decision matrix that lets you pick on the criteria that actually matter for your project.
Standard APM was built for HTTP requests and database queries. Agent runs produce a different signal shape: nested decisions, tool calls, hallucination signals, accumulating context. The observability stack has caught up. Here is the production setup that makes agents debuggable.
Agent loops cost 10x to 50x what a chatbot interaction costs; multi-agent systems add another order of magnitude. The cost compounding is structural, not a bug. The cost reduction is structural too. Decomposing where the tokens go and how to bring agent economics back from runaway to acceptable.
Most multi-agent demos collapse in production. The patterns that survive contact with real workloads are smaller in number and more disciplined than the demos suggest. Planner-worker, supervisor-team, critic-reviser, and hierarchical compositions, with the cost and failure tradeoffs that decide which one to pick when.
Browser-based AI agents moved from labs to early production in 2025 and 2026. Anthropic Computer Use, Microsoft Magentic, and OpenAI Operator each take a distinct architectural bet. This is the working comparison for engineers deciding which to deploy and which workloads they actually fit.
A production-shaped guide to designing AI agent tools that the model can actually use without breaking things. Schema choices, idempotency keys, error responses the model can act on, granularity tradeoffs, versioning, and the patterns that separate demo-quality tools from ones that hold up in real workloads.
A technical review of Claude Design, Anthropic's April 2026 launch that turns Claude into a visual work tool. Coverage includes the Opus 4.7 multimodal foundation, the design-system learning feature, workflow integration patterns, enterprise considerations, and where Claude Design sits alongside Figma rather than replacing it.
European industrial organisations hiring a TypeScript consultancy for manufacturing, automotive, or energy software face a market where framework familiarity is common but domain depth is rare. This buyer guide covers the evaluation criteria that matter — including the ones most consultancy pitches quietly skip.
A working-engineer walk-through of the EU AI Act beyond the risk-tier summary. Covers legal structure, Annex III classification decisions, GPAI rules, the full timeline to 2027, Article 12 logging, Article 50 transparency, conformity assessment artifacts, CE marking, penalties, and the Swedish implementation.
A deep dive into Microsoft Entra Agent ID, the control plane for AI agent identity in 2026. Covers identity blueprints, attended and unattended authentication, tool-level RBAC, conditional access, OBO flows across multi-agent systems, and the audit logging that satisfies DORA, NIS2, and AI Act obligations.