AI Engineering

Agent reliability patterns fail when retries re-run side effects. Design idempotency keys and step-level tracing into the loop from day one.

Local-first MCP with llama.cpp makes offline tool-calling agents viable, and a 32k context budget forces tool discovery at runtime, not upfront.

Pinecone's Nexus Engine compiles business context into structured knowledge graphs for agents. Nemotron 3 Embed tops RTEB. Vector search alone is now insufficient. Here is the memory architecture shift every AI team needs to understand.

Agent testing infrastructure separates prototype agents from production systems. The Strix framework signals a structural shift.

Open-weight models from Meta, Mistral, and the Llama 4 ecosystem have shifted the AI debate from "open vs. closed" to a more nuanced question: what does open source actually mean when the training data remains invisible?

You do not need more GPU power to speed up LLM generation. You need a draft model. Speculative decoding uses small inexpensive models to propose multiple tokens at once, letting a large model verify them in parallel. Here is how it works, the numbers that matter, and when it actually helps.