From Pilot to Production

New research shows what we stand to lose – and how we can stay in control Ever ask an AI to “show its work”? That’s basically what chain-of-thought prompting is. You get the model to explain its reasoning step by step – like showing math on a test. It’s been a go-to technique for getting more transparency from advanced systems. But according to new research from OpenAI, Anthropic, Google, and Meta, that window into AI’s thinking might be closing. The research shows that as models become more capable, they’re also getting better at hiding their reasoning - especially when they’ve been trained to optimize for results. In some cases, models that appeared to be walking through a thought process were actually filtering or skipping steps to present what seemed like a safe or effective answer. That’s cause for real concern. Because if we can’t see how decisions are being made, we can’t fully trust what these systems are doing. Why this matters now Right now, chain-of-thought reasoning gives us one of the clearest looks into an AI model’s internal logic. It can expose unsafe behaviors, catch early signs of misalignment, and help humans stay in the loop as systems grow more autonomous. But the more we train models to perform well by outcome alone, the more we risk losing access to that thought process. Worse, models may learn to “say the right thing” while masking the reasoning that led them there. In short: if we don’t make transparency a priority, we risk losing it altogether. The risk isn't just academic For teams building and deploying AI systems in real-world settings, this isn't just theoretical. It affects how we debug errors, explain decisions, and meet compliance or audit requirements. When something goes wrong, organizations need more than just an output. They need to understand what happened under the hood. And the longer companies wait to build in that kind of observability, the harder it will be to retrofit it later. What this means for the future of Applied AI The research underscores something we believe strongly: AI systems need to be traceable from the start. That means tracking how they think, now just what they say. At Prove AI, we’ve built our software to help organizations do exactly that. Prove AI delivers secure, high-performing Applied AI solutions tailored to your business, as a fraction of the cost and time. Our platform accelerates deployment, boosts performance to production-ready levels, and guarantees measurable ROI in weeks, not months. Equally as important, it ensures your AI system is observable and explainable, every step of the way. From reasoning logs and performance monitoring to input/output lineage, we make it possible to understand how decisions are made in real time. Bottom line We’re still early in the AI era, but we won’t always be. If we want to keep systems safe, aligned, and useful, we need to preserve our ability to “speak AI” and listen closely to how it speaks back. Chain-of-thought monitoring gives us a chance.

Prove AI was in San Francisco this week for World AI Summit, where our CTO, Greg Whalen, joined a fireside chat on why observability is critical for organizations deploying AI at scale and how it leads to smarter, more reliable models. In case you missed the conversation, here are a few key takeaways: Control is becoming foundational As organizations retrain and refine their models more frequently, public infrastructure is no longer enough. Private, end-to-end pipelines are essential for tuning performance, managing risk, and meeting internal requirements with confidence. Observability is still the hardest part Even well-designed models struggle to scale without clear visibility into how inputs, outputs, and context shifts over time. Without that clarity, it’s nearly impossible to adapt systems responsibly or evaluate them rigorously. Data traceability underpins collaboration Modern AI systems often rely on external or dynamic content, which introduces challenges around IP, provenance, and consistency. Without automated oversight, maintaining trust in shared outputs becomes unrealistic, especially at scale. Prove AI addresses these challenges directly, helping organizations monitor their models, track their data, and establish the foundation for safe, adaptive deployment. Want to learn more? Schedule a conversation with our team to see how Prove AI can support your needs.

In software engineering, unit tests have long served as a safety net. They run automatically, catching unintended side effects before they reach production. They are part of the rhythm of development, not a final box to tick before shipping. A similar mindset is now emerging in AI. Evaluations are no longer an afterthought but an active part of keeping systems dependable. They help teams see not just whether a model works today, but whether it continues to perform as expected in real world use. However, AI introduces a complication that makes the comparison only partly accurate. Traditional code behaves predictably: the same input produces the same result. Generative AI does not follow that rule. Models can change their behavior over time, influenced by new data, fine tuning, or patterns in user interaction. That variability means evaluations cannot be a one off check. It must be sustained, tracking performance trends, identifying emerging risks, and confirming that quality holds steady as conditions shift. From base models to agents A study from SAP Labs draws an important distinction here. Base models can be measured with established tools: curated datasets, standard benchmarks, and well-defined metrics. While these methods require effort, they are relatively contained. Agents bring additional complexity. They operate in environments that change from moment to moment, pull from multiple data sources, and make multi-step decisions. Their effectiveness depends on both the quality of the underlying model and the circumstances in which they operate. The SAP Labs framework outlines two main questions: What to evaluate Behavior: Does the system act appropriately in different contexts? Capability: Can it carry out the necessary tasks? Reliability: Are results consistent over repeated use? Safety: Does it avoid harmful or non-compliant outputs? How to evaluate Interaction patterns: Watching how the system behaves over time Datasets: Combining controlled tests with live data Metrics: Tracking measurable indicators like accuracy or latency Testing contacts: Using realistic scenarios to expose weaknesses Why this matters for deployment teams Many evaluation tools in use today are designed for fixed tests. While they are useful for initial validation, they do not reflect the shifting nature of AI systems in production. A model or agent might meet expectations in week one but degrade after ingesting new data, adjusting parameters, or responding to user feedback. At Prove AI, evaluation is built into daily workflows. Before new data sources are added, their potential impact on accuracy, speed, and compliance is assessed. Once live, each change is monitored over time, with full version histories and changelogs to capture what was layered and why. This creates both an early warning system for issues and a documented record for audit purposes. Evaluation in AI is not a step to complete. It is an ongoing safeground. It helps teams maintain confidence, detect problems before they escalate, and adapt to changing conditions without losing control over quality. If you are interested in how Prove AI applies this approach to practice? Visit our product page.