#Arquitetura

TypeSafe launched Jev, the first System One model: it doesn't complete text, it takes your program's state and returns a typed decision in a single parallel pass. No parser, no retries, no broken JSON. In this post: what Jev actually is, why it can't hallucinate (and why that doesn't mean it can't be wrong), the real math on $0.042 per million tokens with free output, the three primitives with running Python code, and the 5 traps the docs themselves admit to.

Half of the "AI projects" I reviewed in 2026 could have been solved with an if and a SELECT. The other half became agents because the team wanted to say they had AI. Here's the 3-minute test: 4 questions that decide whether your problem calls for an if, a query, a prompt, a deterministic flow or an actual agent. With 5 real cases, the verdict on each one (spoiler: 1 in 5 was an agent) and the invisible cost of overshooting.

The agent decides on its own which flow to use, but nobody can explain why it picked the wrong one yesterday at 2 p.m. Deterministic router vs. LLM router, intent classification with a closed schema and evidence, an explicit fallback policy, trace attributes to audit the decision, and how to measure routing accuracy without hand-labeling ten thousand conversations.

If your code has a try/catch wrapped around a json_decode of the model's response, you don't have a contract. You have hope. How to get truly guaranteed structured output from an LLM: constrained decoding at the provider, a schema the model can satisfy, semantic validation and retry with a fixed budget, with no infinite loop and no defensive parser.

Your prototype cost R$ 12 on day one. In production, with 200 users, it costs almost R$ 4,000 a month. We open up the token bill of a real agent layer by layer (system prompt, tool definitions, RAG chunks, history, tool results) and show where almost 80% of the money disappears. In the end, prompt compression, history pruning, and context selection cut the bill by 73%, with the before-and-after spreadsheet.

Grounding isn't RAG, it's the property you want: the answer pinned to data that's yours. The techniques that tether the LLM, structured output with a strict schema, the faithfulness check that runs before delivery to the paying customer, and what will still slip through after all of that.

An honest map of the agent orchestration and memory ecosystem. What LangGraph, Mem0, LangChain and MCP actually do, the point where each one stops being overhead, and the 5-question decision tree to run before installing anything.

The decision most people get wrong by overshooting. Objective criteria for choosing between prompt, RAG, and fine tuning, what each path costs with real numbers, the three rare cases where training a model wins, and the five-minute test that separates a knowledge problem from a retrieval problem. Plus the news that changes the math: OpenAI is shutting down its fine-tuning platform.

The operational fundamentals of LLMs with no parrot analogy: what the model does at each token, why it's stateless, how the context window degrades long before the limit, and why temperature 0 isn't deterministic. Each concept closes with the architectural consequence it forces you to build, with the official Anthropic and OpenAI docs, the Lost in the Middle paper, and Chroma's context rot study in hand.