Harness: I made Jev control the player, has access to WASD, space, click, and mouse movements. Astra is the planner that sends instructions to Jev async. Learning: Each time the agent would fail, Astra would add skills as mjs files, that it can draw upon in different scenarios, Show more
Core ConceptsM2MAI-to-AI
Machine-to-machine automation
Automation where software calls AI directly, expected to be roughly 99% of large-scale AI interactions versus 1% human-facing.
- Category
- Core Concepts
- Also known as
- M2M, AI-to-AI
- Related terms
- 4
- Directory entries
- 2
- Docs
- docs.typesafe.ai
- Added
- 2026-09-24
Definition
TypeSafe's primer states the expectation plainly: future AI automation will be closer to 99% machine-to-machine and 1% human interaction. That reframes the model contract around predictable outputs rather than pleasant conversation.
It is also why the docs push decomposition and confidence gates: an unattended pipeline cannot read a paragraph and decide what it meant, but it can branch on a typed answer and a calibrated probability.
Tagsarchitectureautomation
Related terms
Definitions that connect to this one.
Machine Native IntelligenceTypeSafe's name for AI with software-like properties: structure, reliability, observability, testability, speed, consistency, and low cost.Core ConceptsAI-powered softwareThe architecture TypeSafe recommends: code owns the workflow and the model appears only where programmable common sense is needed.Core ConceptsConfidence gateA threshold on confidence that decides whether code acts, confirms first, or escalates to a person or a bigger model.Answers & ConfidenceAgent loopA control pattern where the model chooses its next step repeatedly; TypeSafe's guidance is to prefer a code-owned workflow instead.Core Concepts
From the directory
Why TypeSafe trains decision models with RLCD instead of RLHF: calibrated probabilities where 0.2 outcomes happen about 20% of the time, and the case for machine-to-machine automation.
Sites & GuidesDocs#official#docs#evaluation
Official
TypeSafe's case for machine-native composable AI: intelligence as a dependable primitive that software branches on, rather than an assistant that keeps humans in the loop.
Sites & GuidesArticle#official#article#architecture
Official
From the community
Posts from builders shipping with Jev right now.
Jev plus Astra beats the Ender Dragon for under a dollar
Tool calling as classify plus action, back to twelve-factor agents
jev is the best excuse you could possibly have to go re-read 12 factor agents. Tool calling itself can be decomposed into classify+action, if you learn to design ai programs as pipelines that switch breathlessly between classification, structuring data, deterministic code, AND Show more
@dillon_mulroy
i think jev is resonating with devs so well b/c it unlocks so many opportunities for composing ai into systems and products rather than ai _becoming_ the product/system really does feel like it was a missing primitive
Why Jev encodes a state and outputs distributions instead
Jev is one of the more interesting model launches I have seen recently because it asks a very simple question: Why are we using autoregressive LLMs as insanely expensive if statements inside software? The easiest way to think about it is: LLM: text -> generate tokens Show more
@CompleteSkeptic
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x
How the agent stack moved in Jev's first three days
Jev just landed and the agent stack moved in three days. TypeSafe AI released Jev on September 15. It is built for the work agents actually do most of the time: choose the next tool, score a risk, decide whether to retry, or answer yes or no. Latency is under half a second. Show more
Jev plays Doom at ~10 calls/sec for ~$7/hour
We love how this doomo doomonstrates real-time intelligence and what can be doone with code + AI! ~10 calls/sec = ~$7/hour
Jev plays Super Mario Bros.
got @typesafeai’s new model Jev to play Super Mario Bros. fast inference + structured outputs makes it surprisingly good for real time use cases. I'm excited to see what can be done with these new models!