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How to Build Transparent AI Agents: Traceable Decision-Making with Audit Trails and Human Gates
In this tutorial, we build a glass-box agentic workflow that makes every decision traceable, auditable, and explicitly governed by human approval. We design the system to log each thought, action, and observation into a tamper-evident audit ledger while enforcing dynamic permissioning for high-risk operations. By combining LangGraph’s interrupt-driven human-in-the-loop control with a hash-chained database,…

NVIDIA Releases Dynamo v0.9.0: A Massive Infrastructure Overhaul Featuring FlashIndexer, Multi-Modal Support, and Removed NATS and ETCD
NVIDIA has just released Dynamo v0.9.0. This is the most significant infrastructure upgrade for the distributed inference framework to date. This update simplifies how large-scale models are deployed and managed. The release focuses on removing heavy dependencies and improving how GPUs handle multi-modal data. The Great Simplification: Removing NATS and etcd The biggest change…

Study: AI chatbots provide less-accurate information to vulnerable users | MIT News
Large language models (LLMs) have been championed as tools that could democratize access to information worldwide, offering knowledge in a user-friendly interface regardless of a person’s background or location. However, new research from MIT’s Center for Constructive Communication (CCC) suggests these artificial intelligence systems may actually perform worse for the very users who could…
Google AI Releases Gemini 3.1 Pro with 1 Million Token Context and 77.1 Percent ARC-AGI-2 Reasoning for AI Agents
Google has officially shifted the Gemini era into high gear with the release of Gemini 3.1 Pro, the first version update in the Gemini 3 series. This release is not just a minor patch; it is a targeted strike at the ‘agentic’ AI market, focusing on reasoning stability, software engineering, and tool-use reliability. For…
A Coding Implementation to Build Bulletproof Agentic Workflows with PydanticAI Using Strict Schemas, Tool Injection, and Model-Agnostic Execution
In this tutorial, we build a production-ready agentic workflow that prioritizes reliability over best-effort generation by enforcing strict, typed outputs at every step. We use PydanticAI to define clear response schemas, wire in tools via dependency injection, and ensure the agent can safely interact with external systems, such as a database, without breaking execution.…

Exposing biases, moods, personalities, and abstract concepts hidden in large language models | MIT News
By now, ChatGPT, Claude, and other large language models have accumulated so much human knowledge that they’re far from simple answer-generators; they can also express abstract concepts, such as certain tones, personalities, biases, and moods. However, it’s not obvious exactly how these models represent abstract concepts to begin with from the knowledge they contain.Now…
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