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AI without the hype: models, agents and use cases that work in production.
The idea of UI generated on the fly instead of pre-built reached production in 2025. After a year of real-world use, the balance is more nuanced than the initial enthusiasm suggested.
Skills package reusable capabilities; subagents isolate bounded-task execution. Together they form the most effective pattern for composing complex agents in 2026.
Synthetic data has moved from a precarious substitute for real data to a central component of modern model training: the most reliable pattern expands a real core of 500 examples with thousands of synthetic paraphrases, provided you validate diversity, correctness, and distribution, and keep at least 30% real data to avoid model collapse.
Claude Sonnet 4.6 is the default model for most 2026 production workloads: it covers 80% of traffic with quality indistinguishable from Opus 4.7 in blind tests, at roughly 60% of Opus per-token price. Opus is still needed for complex reasoning and agentic coding on large codebases.
The first invoice for a production agent usually runs double or triple the estimate. This article walks through five real levers, in priority order, caching, routing, context control, batching, and telemetry, to cut cost without touching perceived quality.
LLM red teaming has gone from an esoteric activity to a mandatory practice. With the OWASP Agentic Top 10 and the CSA Agentic AI Red Teaming Guide converging on shared vocabulary, this is the operational playbook any team deploying agents needs to have.
Después de año y medio llenando tableros con agentes en producción, la pregunta que separa equipos que envían fiable de los que van a ciegas sigue siendo la misma: ¿cómo mides que el agente está funcionando?
El concepto de Agent OS pasó del slide al despliegue en 2025. Seis meses en producción dejan patrones visibles: qué arquitecturas funcionan, dónde se rompe el modelo y qué aporta frente a correr agentes sobre pila existente.
A year after GraphRAG left the lab, one statistic holds: it works where corporate information has dense relational structure, fails where there are only loose documents. Patterns, ingestion costs, and architectural decisions that have survived a year of real deployment.
The Model Context Protocol has gone from proposal to de facto standard for connecting editors with external tools. This practical guide walks through standing up a local MCP server, wiring it into VS Code or your client of choice, and understanding exactly what you are exposing.
After two years of pilots and a year of agents in production, governance has moved from an aspirational committee to an operational control. What audits ask for, what broke in 2025, and which guardrails absorb most incidents.
Twenty months after the initial announcement, Model Context Protocol went from curiosity to de-facto standard among agent clients and servers. What is available, which servers are worth it, which problems remain open, and how it compares to earlier protocol maps.
La factura de IA en las empresas ha dejado de ser anecdótica. Entre tokens de modelos frontera, GPUs reservadas que nadie usa y pipelines RAG con cachés mal configuradas, muchos equipos pagan diez veces lo que deberían. Guía de FinOps específico para IA sin relatos promocionales.
Sixteen months after Anthropic first shipped computer use, with browser-use, OpenAI Operator and Gemini Computer Use all pushing in parallel, agents that drive the browser and desktop have moved from demo to real workflows. Time to review which patterns survive when you run them daily in production.
Knowledge graphs spent two decades waiting for their moment. With LLMs now bridging free text and formal ontology, and the GraphRAG pattern already mature, the technology is back in the spotlight. Time to look at why it finally fits and where it actually pays off.
Six months after A2A landed at the Linux Foundation, and after several implementation cycles from Google, Microsoft, and open projects, what version 1 of the protocol means and whether it is safe to build on yet.
European sovereign AI discourse has spent three years fueling headlines, public investment, and interstate agreements. We are starting to see which part of the promise has real technical substance and what a technical team expecting alternatives outside the US ecosystem can actually count on.
