Top 12 agentic use cases for Jev: (bookmark this) Jev handles semantic decisions that ordinary code cannot express reliably. It returns typed answers and probabilities, while code continues to cover the workflow. Here are 12 practical use cases for Jev: 1. Browser next action > Convert the current DOM state into a bounded action such as click, type, or stop. Code executes only valid operation-target pairs. There are already several open-source Jev web agents. 2. Context compaction > Decide which events from a long agent trace should remain. The selected text stays verbatim instead of being replaced with a generated summary. 3. Skill and context loading > Compare the current user turn against the available skills. Load only the instructions needed for that turn instead of filling the context window with every skill. 4. Typed tool-call compilation > Map a natural-language request to a function and fill its typed arguments. Each argument is evaluated separately before code allows execution. 5. Citation verification > Check whether a quoted passage exists and whether the surrounding evidence supports the claim. The output can be supported, unsupported, or contradicted. 6. Extraction verification > Run a cheap extractor first, then use Jev to verify questionable fields. Clean records stay on the fast path while uncertain ones reach a reasoning model. 7. Agent trace evaluation > Turn raw trajectories into queryable labels such as progress and repetition. This avoids asking another LLM to write a full review of every run. 8. Semantic regression tests > Replay a trace suite against a new agent build. Semantic checks can then pass or block prompt, model, tool, and policy changes in CI. 9. Jevgrep code search > Search a codebase by what the code does rather than its exact words. Jev scores candidate snippets and returns the most relevant code first. 10. Entity alignment > Compare two candidate records and decide whether to merge, review, or keep them separate. Candidate generation remains deterministic while Jev handles semantic identity. 11. Retrieval reranking > Let embeddings retrieve a broad candidate set, then use Jev to reorder passages by relevance. The generation model receives the most useful evidence first. 12. Memory promotion gate > Capture a completed agent trace, then judge whether its corrections contain a reusable lesson. Trace-backed lessons can be promoted while task-specific noise is discarded. If you want to see the final pattern in practice, it is already implemented in the Beacon open-source project. Beacon captures full sessions across Claude Code, Codex, Cursor, OpenCode, and 20+ agent harnesses, and then Jev identifies which workflows and corrections are worth learning from, so that a lesson discovered by one agent can become available to the others. GitHub repo: github.com/Asymptote-Labs/ag… (don’t forget to star it ⭐) If you want to dive deeper, I also wrote about a similar mechanism in a hands-on guide. It covers building a Jev-style decision path with open models, entirely locally. Read it below.

Karpathy's prediction about RL is coming true now! He argued that a single reward number tells an agent very little about why its work was good or bad. For complex tasks, he proposed a review informed by knowledge of the task. That problem shows up as soon as RL moves beyond answers that can be checked automatically. A math solution is right or wrong. Code can run against tests. But an agent's work may involve dozens of decisions, and a successful final answer can hide wasted steps or poor tool use. Engineers can write rules to score those trajectories, but the rules need to anticipate the behavior they want to reward. When the agent's workflow changes, the scoring code often needs another pass. RULER, implemented in OpenPipe ART, lets the engineer write the evaluation criteria in plain English. An LLM reviews multiple trajectories against those criteria and ranks them. ART turns that comparison into rewards for GRPO training. I used this setup to train a Qwen3 1.4B agent to play 2048. It saw the board and chose a direction at each step. Rather than scoring its moves with a collection of rules I had coded, I described what the reviewer should look for and let RULER compare complete attempts. The result is still a numerical reward, but the difference is how the reward is defined. I can revise the description when I find behavior the reviewer is missing, then inspect whether its judgments match what I intended. The full implementation is available on GitHub. ART GitHub repo: github.com/OpenPipe/ART (don't forget to star it ⭐) I also wrote a walkthrough of the RL concepts and this training run, covering RLHF, GRPO, and RULER, in the article below.