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In a Training Loop πŸ”„
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Brian Moran

obversarystudios
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https://obversarystudios.org

AI & ML interests

Reinforcement learning, cognitive systems, constrained policy learning

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repliedto mrmanna's post 5 days ago
π—”π—œ & π—¦π—§π—”π—§π—˜ π— π—”π—–π—›π—œπ—‘π—˜ 𝘞𝘩𝘺 π˜—π˜³π˜°π˜₯𝘢𝘀𝘡π˜ͺ𝘰𝘯 π˜‰π˜¦π˜¨π˜ͺ𝘯𝘴 𝘞𝘩𝘦𝘳𝘦 π˜›π˜°π˜Ί 𝘈𝘨𝘦𝘯𝘡𝘴 𝘌𝘯π˜₯ Published: 18 Apr 2026 | Towards AI Publication | Medium Open Link: https://medium.com/towards-artificial-intelligence/ai-state-machine-106387406c5a?sk=047b2f064c673a0095a9e8cc011b6a92 We talk a lot about governance, accuracy, and auditability in AI agents. But I keep seeing a gap between the words and the engineering behind them. Many agents have tools, orchestration, memory, graphs, and impressive demos. But when you ask how governance is actually enforced, the answer is often weak. Prompt-level control is not production governance. A production agent needs explicit state design: legal transitions, controlled progression, recovery paths, approval boundaries, and separation between memory, decision, policy, and execution. This article explores the silent crisis unfolding in modern AI development: the urgent need to resurrect the disciplined architecture of state machines
upvoted an article 10 days ago
Multimodal Embedding & Reranker Models with Sentence Transformers
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