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Lauren on X - How I shipped 2,500 PRs last month to prod

- 1 min read - 100 words
Brett Dargan
Brett Dargan
Principal Solutions Architect (AWS) | Tech Co-Founder | Product Strategy & Business Value

Reviews of approach

Lauren Tan, poteto Agent Shipping System

Agent information flow

- 1 min read - 200 words
Brett Dargan
Brett Dargan
Principal Solutions Architect (AWS) | Tech Co-Founder | Product Strategy & Business Value

OpenAPPA

OpenAPPA sits between an agent and its tools and answers one question before every action: is this data allowed to go to this destination?

It is powered by APPA (Agentic Permissions Policy Algebra). OpenAPPA tracks the sensitivity and trust of everything an agent reads and checks each tool call against it before the call runs, so sensitive data never reaches an unauthorized tool. Classifiers and PII detectors are probabilistic, while this check is deterministic and returns the same decision on every run.

OpenAPPA is a frontier deterministic AI guardrail that is 100% resistant to data exfiltration caused by prompt injection or model hallucination, and the first of its kind that doesn’t break agents.

Quick AI decision making

- 2 min read - 300 words
Brett Dargan
Brett Dargan
Principal Solutions Architect (AWS) | Tech Co-Founder | Product Strategy & Business Value

Nearly deterministic sub second AI decisions.

While Jev has had lots of publicity last week there are alternatives if like: Laya, Gliner and semif-rs.

Jev and alike are not LLMs.

Jev, GLiNER 2.5 (Decide) are non-generative, “System One” decision models built to output structured data and probabilities rather than conversational text

laya-rs vs jev vs gliner-rs vs semif-rs: typed-decisions comparison · GitHub

I Tested Jev vs 12 Local Decision Models. Here’s What I’d Use… - YouTube

1. Reinforcement Learning (RL) Framework

Unlike traditional LLMs that rely on RLHF (Reinforcement Learning from Human Feedback)—where a model is rewarded for writing a response that looks pleasing to a human—RLCD trains the model based on objective correctness. The model is treated as an RL agent that interacts with data and rules, earning rewards when its structured output aligns perfectly with the ground truth or specified operational constraints.

2. Probability Calibration (The “C” in RLCD)

In standard classification or generative models, a confidence score of 90% does not always mean the answer is right 90% of the time (models are notoriously overconfident). RLCD specifically shapes the loss function and optimization strategy to calibrate the probabilities. This means if the model assigns a 0.85 probability to an API routing rule, it mathematically represents an exact 85% statistical likelihood of being correct.

3. Non-Autoregressive Joint Decoding

From an architectural standpoint, instead of generating token-by-token (like a standard GPT model), it relies on an encoder-based architecture. It processes the input and evaluates the target rules or typed questions simultaneously in a single parallel pass.