# AI Architecture AI architecture is the design of layered, modular AI systems, including orchestration, model inference, data retrieval, monitoring, security, and storage. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Introducing IBM and NASA's new foundation model for the Moon DevFeed: [Introducing IBM and NASA's new foundation model for the Moon]() Original publisher: [Read original article]() Author: Kim Martineau Published: 2026-09-10T12:30:00Z Content type: article Language: en Sources: [IBM Research]() Topics: [lunar foundation model](), [ibm](), [foundation-models](), [data](), [Artificial Intelligence](), [AI Architecture]() Tags: [accelerated-discovery](), [ai](), [ai-architecture](), [algorithms](), [ibm](), [lunar-foundation-model](), [mapping](), [model](), [nasa](), [release](), [science](), [space](), [us]() ### AI overview IBM and NASA are open-sourcing the NASA-IBM Lunar Foundation Model, a multimodal AI model that integrates lunar observations from US and Japanese missions across viewing angles, spatial scales, and measurement types. The model is intended to support lunar mapping, volcanic-history research, and searches for polar ice. ### Source excerpt The multi-modal model could help astronauts navigate craters, investigate ancient lava, and search for ice, as the US plans for a long-term lunar presence. ## A Unified Data Architecture For Sovereign Agentic AI With VMware Tanzu And VMware vSAN DevFeed: [A Unified Data Architecture For Sovereign Agentic AI With VMware Tanzu And VMware vSAN]() Original publisher: [Read original article]() Author: arnab chakraborty Published: 2026-09-03T23:27:50Z Content type: article Language: en Sources: [VMware Blogs]() Topics: [Agentic AI Architecture](), [AI Architecture](), [data-architecture](), [AI Agent](), [cloud-infrastructure](), [Latency](), [real-time](), [Security](), [data-processing]() Tags: [agentic-ai](), [agentic-ai-architecture](), [ai](), [ai-architecture](), [blog](), [cloud](), [data-architecture](), [efficiency](), [infrastructure](), [latency](), [modern-apps](), [real-time](), [security]() ### AI overview The article presents a unified, on-premises data architecture for sovereign agentic AI using VMware Tanzu, VMware Tanzu Greenplum, VMware Cloud Foundation, and VMware vSAN. It argues that placing AI compute close to enterprise data can improve performance and cost while reducing latency, data-transfer fees, and compliance risks. ### Source excerpt By combining VMware Tanzu Greenplum with VMware vSAN, organizations can bring their AI compute directly to their data storage layer for improved cost and latency. The post A Unified Data Architecture For Sovereign Agentic AI With VMware Tanzu And VMware vSAN appeared first on Tanzu. The post A Unified Data Architecture For Sovereign Agentic AI With VMware Tanzu And VMware vSAN appeared first on VMware Blogs. ## Why Specialization Is Inevitable DevFeed: [Why Specialization Is Inevitable]() Original publisher: [Read original article]() Author: Erick Lachmann; Francisco de Almeida Rocha Alves; Gabriel Pimenta de Freitas Cardoso Published: 2026-06-30T14:39:11Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Artificial Intelligence](), [Optimization](), [Machine Learning & Artificial Intelligence](), [AI Architecture]() Tags: [ai](), [architecture](), [article](), [machine-learning](), [optimization](), [research]() ### AI overview The article argues that specialization is an inevitable and effective principle for AI systems. Drawing on optimization theory, evolutionary biology, competitive markets, and machine learning, it connects narrow domain focus with improvements in cost, performance, reliability, and sovereignty, while discussing the implications of the no-free-lunch theorem for AI architecture. ### Source excerpt What optimization theory, evolutionary biology, competitive markets, and machine learning all predict -- and why the answer is the same --- Those who follow Dharma AI already know that we view specialization as one of the defining principles of effective AI systems, shaping everything from cost and performance to reliability and sovereignty. Few papers have articulated that case as rigorously as the 2026 work by Goldfeder, Wyder, LeCun, and Shwartz-Ziv. ## Agentic AI Architecture: How CockroachDB Supports Memory, Context, and Control DevFeed: [Agentic AI Architecture: How CockroachDB Supports Memory, Context, and Control]() Original publisher: [Read original article]() Author: Alejandro Infanzon Published: 2026-06-11T00:00:00Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [Agentic AI Architecture](), [CockroachDB](), [Artificial Intelligence](), [AI Architecture](), [context](), [Databases](), [data](), [Resilience](), [audit](), [SQL](), [Back end](), [Cockroach Labs]() Tags: [agentic](), [agentic-ai](), [agentic-ai-architecture](), [agents](), [ai](), [ai-agents](), [ai-architecture](), [architecture](), [audit](), [backend](), [cockroachdb](), [context](), [data](), [database](), [databases](), [durability](), [infrastructure](), [latency](), [low-latency](), [memory]() ### AI overview This article explains how CockroachDB can support enterprise agentic AI architectures by storing durable agent state, long-term memory, retrieval metadata, schema context, permissions, execution metadata, cost information, latency telemetry, and SQL audit trails. It presents the database as an operational layer for observing, governing, and improving autonomous agent behavior. ### Source excerpt What happens when you connect a fleet of autonomous AI agents to your enterprise data stack? You quickly discover... ## Five principles for governed autonomy with enterprise AI DevFeed: [Five principles for governed autonomy with enterprise AI]() Original publisher: [Read original article]() Author: Robert Siwicki Published: 2026-04-28T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Artificial Intelligence](), [AI Agent](), [observability](), [AI Architecture](), [Streaming](), [real-time](), [Resilience](), [data](), [systems]() Tags: [agentic](), [ai](), [ai-agent](), [ai-architecture](), [architecture](), [customer](), [enterprise](), [event](), [governance](), [logs](), [memory](), [observability](), [real-time](), [resilience](), [streaming](), [thought-leadership](), [tool](), [workflows]() ### AI overview The article presents five principles for evolving Redleader, Redpanda's customer Slackbot, into a governed multi-agent architecture. It emphasizes stable streams, event-based coordination, human oversight, explicit terminal states, and real-time observability to make agent behavior reliable, replayable, measurable, and scalable. ### Source excerpt How to turn opaque agent behavior into governed, provable workflows. Based on our own tried and true experience with Redpanda's customer Slackbot. ## LLaDA: A Diffusion-Based Language Model for Revisable Text Generation DevFeed: [LLaDA: A Diffusion-Based Language Model for Revisable Text Generation]() Original publisher: [Read original article]() Published: 2025-02-17T00:00:00Z Content type: article Language: en Sources: [Tim Kellogg]() Topics: [diffusion-transformers](), [Large Language Model](), [AI Architecture]() Tags: [ai-architecture](), [consistency](), [diffusion](), [hallucinations](), [language](), [llms](), [model]() ### AI overview The article introduces LLaDA, a language model that uses diffusion rather than autoregressive next-token prediction. It explains how this approach can generate and revise text globally, potentially helping with hallucinations, reasoning loops, and coherence in structured writing such as contracts. ### Source excerpt A new language model uses diffusion instead of next-token prediction. That means the text it can back out of a hallucination before it commits. This is a big win for areas like law & contracts, where global consistency is valued