Over six months, the Elastic IT team ran internal AI applications that returned $2.5 million in operational time to the business.1 A conversational support assistant moved us from zero digital resolution, where anything complex became a ticket, to 30% of support interactions closing without one.
We can put those numbers in front of a finance team because we measured them from day one at the level of individual usage events. For each use case, we assigned a conservative time-saving goal and validated it with the teams doing the work. For example, a support case summary saves about five minutes. And, using a simple formula (events*minutes saved*a standard burden), the ROI of the application is now a real-time KPI rather than simply assuming that it might be valuable because the application uses generative AI. The hours came back to support engineers who had been searching for answers and went toward work on the roadmap.
Most organizations are not in that position yet. In our Landscape of Observability survey of 500 IT decision-makers, 85% said they planned to enable observability for their large language model (LLM) applications. Only 8% had done it. Teams see the value, they just don’t seem to be prioritizing it. — Read More
Recent Updates Page 2
What an Enterprise Context Layer Actually Is
I have had some version of the same conversation with a CIO almost every day this year. It usually starts the same way. Their team has read about context layers, or seen the term in a Gartner note. They know it matters. They know it sits somewhere underneath the agents they are trying to put into production.
… So here’s the question I actually want to answer, when you strip away the branding, what is a context layer made of, and how do you tell a real one from a catalog with a new coat of paint? — Read More
The enterprise AI bottleneck is about context, not capability
Nearly four years after the generative AI wave began, enterprise deployments have settled into a familiar pattern: models are capable, pilots are convincing, but production rollouts plateau on accuracy, drift on outputs, and fail to compound productivity gains the way anyone expected. The diagnosis is increasingly consistent: The bottleneck is the context layer.
… Managing Director Lonne Jaffe sat down with Prukalpa Sankar, cofounder and co-CEO of Atlan, to work through the fundamentals: What context actually means in an enterprise setting, what it takes to build a layer that AI agents can reliably consume, and why the hardest part of all may have nothing to do with technology. — Read More
Agentic brand drift: How AI-orchestrated organizations will lose their identity and how to get it back
Agentic AI is reshaping how organizations operate. As autonomous systems take over pricing, content, personalization, and supply chain decisions, the human choices that historically built brand identity are progressively displaced. We term the result agentic brand drift: the gradual, unintended divergence between a firm’s intended brand identity and the emergent brand character produced by its AI-orchestrated operations. Unlike brand inconsistency or deliberate identity change, agentic brand drift is internally generated, has no triggering event, and co-occurs with improving performance metrics, making it invisible to conventional monitoring. Critically, this failure mode falls outside the scope of existing AI governance frameworks such as NIST AI RMF and ISO/IEC 42001, which govern system behavior rather than meaning coherence. A firm executing those frameworks flawlessly will still experience agentic brand drift. We theorize three mechanisms, Decision Diffusion, Temporal Collapse, and Accountability Dissolution, operating as a causal sequence, and derive two complementary frameworks, CORE and GUARD, that give organizations capabilities existing governance does not provide: identifying which decisions carry identity stakes, supplying agents with organizational reasoning behind past brand choices, and monitoring output patterns for identity coherence over time. — Read More
Agentic Transaction: Towards ACID-Compliant Agent Systems
Large language model (LLM) agents are evolving from conversational assistants into autonomous systems that execute long-horizon tasks through reasoning, tool use, code generation, and workspace manipulation. As agents increasingly operate over persistent environments and multi-step workflows, they face challenges analogous to those addressed by transactional database systems: reliable execution, consistent outcomes, safe concurrency, and durable state management. We introduce the concept of an agentic transaction and propose an ACID-compliant agent system framework that reinterprets the classical ACID properties for agent execution through four semantic guarantees: Semantic Atomicity, Semantic Consistency, Semantic Isolation, and Semantic Durability. Together, these properties provide a principled foundation for building reliable agent systems despite model uncertainty and dynamic execution environments. To instantiate this framework, we develop an ACID-compliant data agent that realizes these guarantees through transactional exploration-execution-validation cycles, transactional skill hubs, confidence divergence-based validation, semantic dependency-aware isolation, and transaction-aware semantic state management. Experimental results on widely used benchmarks show that our system achieves a 10.6% improvement over state-of-the-art agents, including Claude Code. This work opens a broader research agenda on extending transactional principles and system architectures toward building trustworthy, scalable, and self-evolving AI agent systems. — Read More
The US-China AI Race: China’s Endgame, ASI Timelines, & the $1.7T AI Bubble
Designing Loops for Production-Grade Work
In late 2025, we ran an experiment to answer one question: “Can coding agents autonomously solve a production-grade problem from scratch on their own?”
For this, we tasked two agents with the (at that time) best publicly available coding models with a real problem and a real deadline. The result of this experiment is a tokenizer trainer called toktoktok, and is now open source on GitHub.
In this article, we share what we learned about designing effective loops that allow agents to autonomously solve production-grade problems: how to specify a goal for multi-domain experts and how to set up the verification infrastructure. — Read More
The New American AI Model Designed to be Customized
Thinking Machines released a model called Inkling on July 15, 2026. .. Before Inkling, the company shipped Tinker, a service for fine-tuning open models [4]. Inkling is the company’s first model trained from scratch [1]. The weights sit on Hugging Face under an Apache 2.0 license [2], so anyone can download them and retrain the model on their own data.
In this article, we will work through the various choices Thinking Machines made while building Inkling. — Read More
MCP Server Architecture Patterns for LLM-Integrated Applications
The Model Context Protocol (MCP), introduced by Anthropic in November 2024, defines a standardized interface for connecting large language models (LLMs) to external tools, data sources, and services. Within months of release, hundreds of community-built MCP servers appeared on GitHub, but no software-maintenance literature has yet described how the ecosystem is being structured in production. This industry experience paper catalogues five recurring MCP server architectural patterns observed across an enumerated corpus of fifteen independently developed servers (five production servers from the ANSYR voice AI platform plus ten public servers from the official MCP registry): Resource Gateway, Tool Orchestrator, Stateful Session Server, Proxy Aggregator, and Domain-Specific Adapter. Each pattern is described in the structured form established by Gamma et al. [1]: context, problem, solution, and consequences. We also document four anti-patterns and a set of cross-cutting concerns around authentication, versioning, and observability. Quantitative evaluation contributes three measurements: inter-rater reliability of the taxonomy across two independent LLM raters on 54 held-out servers (Cohen’s ), which also localizes three pattern-boundary ambiguities; transport overhead measured end-to-end on loopback (stdio: 0.01 ms ; streamable-http: 0.39 ms ) and modeled for cross-host paths from same-region network baselines (30 ms baseline plus protocol overhead); and a tool-count study showing accuracy drops below 90% between 10 and 15 tools per context for Claude Haiku 4.5 and between 20 and 30 tools for Sonnet 4. Code, corpus, and prompts are released at https://github.com/rodriguescarson/mcp-patterns-icsme2026. — Read More
One AI module faked 86% of a pipeline’s accuracy gains by feeding another the answers
A retrieval-augmented generation (RAG) system is built to answer strictly from the documents it retrieves. But when engineers optimize these AI pipelines end-to-end, the reader module can learn a shortcut: instead of relying on retrieved evidence, it starts answering from its own internal memory — while the system’s overall accuracy keeps climbing. This is the hidden challenge of “role drift,” a failure mode in compound AI systems where individual modules learn to bypass their assigned tasks even as end-to-end performance improves.
To address this, researchers at MIT and Harvard introduce Role Anchor, a technique that forces modules to stay in their lanes during training. When applied, the technique mitigates role drift. For example, it forces the RAG reader to rely on retrieved evidence instead of answering based on its internal knowledge.
The primary takeaway for practitioners is that end-to-end accuracy alone can overstate how much a compound AI system has genuinely learned. — Read More
Rick's Cafe AI 