The rise of the World Wide Web enabled developers to build tools and platforms on top of it. Similarly, the advent of large language models (LLMs) allows for creating new AI-driven tools, such as autonomous agents that interact with LLMs, execute tasks, and make decisions. However, verifying these decisions is crucial, and critical reasoning may be a solution, according to Yam Marcovitz, tech lead at Parlant.io and CEO of emcie.co.

Marcovitz likens LLM development to the evolution of programming languages, from punch cards to modern languages like Python. Early LLMs started with small transformer models, leading to systems like BERT and GPT-3. Now, instead of mere text auto-completion, models are evolving to enable better reasoning and complex instructions.

Parlant uses "attentive reasoning queries (ARQs)" to maintain consistency in AI responses, ensuring near-perfect accuracy. Their approach balances structure and flexibility, preventing models from operating entirely autonomously. Ultimately, Marcovitz argues that subjectivity in human interpretation extends to LLMs, making perfect objectivity unrealistic.

Learn more from The New Stack about the evolution of LLMs:

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Adam Jacob, CEO of System Initiative, discusses a shift in infrastructure automation—moving from writing code to building models that enable rapid simulations and collaboration. In The New Stack Makers, he compares this approach to Formula One racing, where teams use high-fidelity models to simulate race conditions, optimizing performance before hitting the track.

System Initiative applies this concept to enterprise automation, creating a model that understands how infrastructure components interact. This enables fast, multiplayer feedback loops, simplifying complex tasks while enhancing collaboration. Engineers can extend the system by writing small, reactive JavaScript functions that automate processes, such as transforming existing architectures into new ones. The platform visually represents these transformations, making automation more intuitive and efficient.

By leveraging models instead of traditional code-based infrastructure management, System Initiative enhances agility, reduces complexity, and improves DevOps collaboration. To explore how this ties into the concept of the digital twin, listen to the fullNew Stack Makers episode.

Learn more from The New Stack about System Initiative:

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Morgan McLean, co-founder of OpenTelemetry and senior director of product management at Splunk, has long tackled the challenges of observability in large-scale systems. In a conversation with Alex Williams onThe New Stack Makers, McLean reflected on his early frustrations debugging high-scale services and the need for better observability tools.

OpenTelemetry, formed in 2019 from OpenTracing and OpenCensus, has since become a key part of modern observability strategies. As a Cloud Native Computing Foundation (CNCF) incubating project, it’s the second most active open source project after Kubernetes, with over 1,200 developers contributing monthly. McLean highlighted OpenTelemetry’s role in solving scaling challenges, particularly in Kubernetes environments, by standardizing distributed tracing, application metrics, and data extraction.

Looking ahead, profiling is set to become the fourth major observability signal alongside logs, tracing, and metrics, with general availability expected in 2025. McLean emphasized ongoing improvements, including automation and ease of adoption, predicting even faster OpenTelemetry adoption as friction points are resolved.

Learn more from The New Stack about the latest trends in Open Telemetry:

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