# AI use cases for Agent Jev (malevrigns/agent-jev) Repo: [malevrigns/agent-jev](https://github.com/malevrigns/agent-jev) | Use cases: 4 | Page: https://dedupedai.com/use-cases/malevrigns-agent-jev/ AgentJev-0.6B is a Python model server that scores a state, such as a diff, log, or trace, against questions you supply and returns calibrated probabilities in one forward pass without generating text. This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL. - Gate agent merges on test output probability: https://dedupedai.com/use-cases/malevrigns-agent-jev/gate-agent-test-results-with-probability/ - Pick the next tool call from a fixed option list: https://dedupedai.com/use-cases/malevrigns-agent-jev/route-next-agent-action-by-choice/ - Score tickets or traces on your own rubric: https://dedupedai.com/use-cases/malevrigns-agent-jev/score-work-items-on-an-ordered-rubric/ - Benchmark AgentJev serving latency on your load: https://dedupedai.com/use-cases/malevrigns-agent-jev/measure-serving-latency-on-own-workload/ ## How to gate agent merges on test output probability with Agent Jev Repo: [malevrigns/agent-jev](https://github.com/malevrigns/agent-jev) | Difficulty: Intermediate | Time: 1 hour | Basis: From the README Ask AgentJev whether a test run passed and block the merge when the probability is too low. **Who it is for:** A developer running a coding agent that opens pull requests, where a wrong green test result would let broken code move forward. **The problem:** Agents often read a test summary and declare success too early, and parsing a language model's free-text answer is fragile. **The outcome:** A probability that the tests passed, returned without decoded text, which the agent loop can threshold before it continues. ### Steps 1. Clone the repository with `git clone https://github.com/malevrigns/agent-jev` and enter the folder. (inferred, check the README) 2. Fetch the coding-gates candidate weights with `python fetch_v1_weights.py --variant coding-gates`. 3. Start the server with `python -m jev_service.server --checkpoint models/agentjev-coding-gates/model.safetensors --model-path models/agentjev-coding-gates --temperatures models/agentjev-coding-gates/temperatures.json --port 8149`. (inferred, check the README) 4. Send the captured test output as the state and the boolean question 'Did the tests pass?' to http://localhost:8149 using the request format in the repository's HTTP section. (inferred, check the README) 5. Block the merge when the returned probability of true is below your chosen threshold, and log the probability with the run. ### Prompt to paste into your agent ```text In the agent-jev repository at , start the coding-gates server on port 8149, send the test output in with the boolean question 'Did the tests pass?', and return the probability of true. Verify the response by sending a log from a known failing run and confirming the probability is low. ``` ### Caveat The README says the v1 default calls 123 of 348 failing test cases green, so use the probability as one gate and check the candidate's stated weaknesses, such as passing go test runs that print no test files. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/malevrigns-agent-jev/gate-agent-test-results-with-probability/ ## How to pick the next tool call from a fixed option list with Agent Jev Repo: [malevrigns/agent-jev](https://github.com/malevrigns/agent-jev) | Difficulty: Intermediate | Time: 1 hour | Basis: From the README Give AgentJev the current state and a list of allowed next actions, then route to the top option. **Who it is for:** An engineer building an agent workflow with a fixed set of tools who wants a cheap routing decision on every step. **The problem:** Asking a large model to name the next tool costs a full generation and can return a name outside the allowed set. **The outcome:** The top option, its probability, and the margin over the second option, so the loop can act or escalate when the margin is small. ### Steps 1. Clone the repository and start the server with the checkpoint and temperatures you have available, following the Run it and HTTP sections of the README. 2. Write the current state as text or JSON, for example the last tool result and the open ticket. 3. Define a choice question with 2 to 255 options, each written as a description of the action. 4. Send the state and question to the server and read top_probability and margin from the response. 5. Run the top option when margin is above your threshold, and hand the step to a larger model otherwise. ### Prompt to paste into your agent ```text Using the agent-jev repository at , send the state in with a choice question listing the allowed next actions in . Return the top option, top_probability, and margin. Verify that the returned option is one of the options you supplied. ``` ### Caveat The README says a choice result is a preference inside the set you passed and is not an independent probability that the action will succeed, so calibrate a boolean question on your own outcomes if you need success odds. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/malevrigns-agent-jev/route-next-agent-action-by-choice/ ## How to score tickets or traces on your own rubric with Agent Jev Repo: [malevrigns/agent-jev](https://github.com/malevrigns/agent-jev) | Difficulty: Intermediate | Time: 1 hour | Basis: From the README Place a ticket or agent trace on a 2 to 10 level rubric and get the level and expected score. **Who it is for:** An operations lead triaging support tickets or reviewing agent traces who wants a consistent ranking without reading every item. **The problem:** Hand scoring is slow and inconsistent, and a free-text model reply is hard to convert into a stable level. **The outcome:** A level from the argmax, a weighted score across the levels, and the full distribution for every item. ### Steps 1. Clone the repository and start the server following the README's Run it and HTTP sections. 2. Write 2 to 10 level descriptions for your rubric, lowest first. 3. Send each ticket or trace as the state with a score question that uses your level descriptions. 4. Read level for the argmax and score for the weighted sum, and store the full distribution with the item. 5. Review items near the boundary between two levels before acting on them. ### Prompt to paste into your agent ```text In the agent-jev repository at , score each item in with a score question built from the rubric in . Return level, score, and the full distribution for each item. Verify that every level is within the rubric range and that score matches the weighted sum of the distribution. ``` ### Caveat The README says the Typed Decisions teacher labels are the reference for accuracy, not measured success on real coding or support work, so validate scores against your own labelled examples. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/malevrigns-agent-jev/score-work-items-on-an-ordered-rubric/ ## How to benchmark AgentJev serving latency on your load with Agent Jev Repo: [malevrigns/agent-jev](https://github.com/malevrigns/agent-jev) | Difficulty: Advanced | Time: 1 hour | Basis: From the README Run the repository's serving benchmark to see case latency and throughput on your own GPU. **Who it is for:** An infrastructure engineer deciding whether an AgentJev server fits the latency budget of a planned agent loop. **The problem:** The published timings come from one RTX 5060, so you need numbers for your own hardware and request mix. **The outcome:** Case latency at P50 and P90, throughput in cases per second, and backbone token counts for your load. ### Steps 1. Clone the repository and fetch the weights you plan to serve. 2. Start the server with the checkpoint and temperatures you plan to use. 3. Run `python -m jev_service.benchmark_serving` with the same checkpoint on the same machine. 4. Compare P50 case latency, P90 case latency, and throughput against your agent loop's time budget. 5. Repeat with a wider candidate load if your questions have many options. ### Prompt to paste into your agent ```text In the agent-jev repository at , run `python -m jev_service.benchmark_serving` on this machine and report P50 case latency, P90 case latency, and throughput. Verify that the run used the same checkpoint you plan to deploy. ``` ### Caveat The README's latency table measures one load on one GPU and warns it is not a promise for every prompt, so rerun the benchmark on your own hardware and prompts. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/malevrigns-agent-jev/measure-serving-latency-on-own-workload/