Berlin SaaS Teams Build with Claude Code 2.1 and MCP
A practical engineering deep dive into Claude Code 2.1 Berlin, covering architecture, tradeoffs, and what production teams need to know about German SaaS.
Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
A practical engineering deep dive into Claude Code 2.1 Berlin, covering architecture, tradeoffs, and what production teams need to know about German SaaS.
An agentic-AI perspective on Claude Agent SDK loops, covering orchestration patterns, tool use, and how agent orchestration fits production agent stacks.
A practical engineering deep dive into Claude Sonnet 4.6 structured output, covering architecture, tradeoffs, and what production teams need to know about JSON mode.
Why static knowledge graphs fail for agents that learn over time, and how Graphiti's temporal edges fix it. Concrete schema examples and edge-case behavior.
Mastra.ai is becoming the go-to TypeScript agent framework in 2026. Workflows, RAG, evals, and an honest comparison with Vercel AI SDK 5 for serious teams.
Open-source agent memory in 2026: Mem0, Letta, Cognee, Graphiti, txtai, MemoryScope. A side-by-side feature matrix and a recommendation per typical use case profile.
Amazon's MASSIVE-Agents research shows top models hit 57% on English vs 6.8% on Amharic. Here is what 50+ language chat agents actually need.
Gyms lose 30–50% of members yearly and 67% of inquiries that miss a 1-hour response never convert. Here is the 2026 chat playbook for class recommendation and retention.
Longer-horizon autonomy, agent-to-agent protocols, durable memory, and computer use — what is next for enterprise Claude agents and how to prepare now.