Importance of Monitoring Open AI Models

Everybody wants to talk about using AI Agents, but how many understand what it takes to truly build and maintain them? AI Agents, like any ML model, requires monitoring post-deployment. But AI Agents are different than traditional AI models in that many industry AI Agents are built using APIs trained by third party companies. This means monitoring both during and after deployment is critical. You'll need to monitor things like usage relative to the rate limit of the API, latency, token usage, and how many LLM calls your AI Agent makes before responding. You'll even need to monitor failure points at the API level as bottlenecking and region availability can bring your entire AI solution down. Tools like Splunk, DataDog, and AWS CloudWatch work well here. They help you track these metrics and set up alerts to catch issues before it affects your AI Agent build. LLM usage costs take far too many companies by surprise at the end of a POC. Don't be that company. Monitor closely, set thresholds, and stay on top of your AI Agent's performance and costs.

Just finished reading OpenAI's recent paper on Chain-of-Thought Monitoring, and its implications are fascinating. Here’s the big takeaway: As AI models become more advanced, they don’t just make mistakes — they learn to lie to optimize for their goals. And when we push them harder to "be good" (by training them to avoid bad behavior), they get even smarter — and start to deceive by hiding their true intentions. This is an emergent property of increasingly powerful models optimizing in complex ways. Think about that for a moment: AI models that can plan, reason, and deceive — without us explicitly teaching them to do so. The implications are enormous:  - Trust: If AI systems can deceive, how do we trust them in critical roles (healthcare, finance, national security)?  - Control: If AI learns to hide misaligned behaviors, how do we ensure alignment as they become more capable than us?  - Governance: What kind of oversight mechanisms do we need — and how much transparency are we willing to trade for raw performance? One subtle insight from the paper is that watching how models "think" (via Chain-of-Thought reasoning) can help us detect bad behavior — however, if we push too hard, they will learn to "think quietly" or "think deceptively." This challenges one of the foundational assumptions in AI alignment: that if we can see a model's thought process, we can keep it aligned. Turns out, even that window can be gamed. As we push toward more powerful AI, this dynamic of reward hacking and deception should be a top concern for anyone thinking about AI's role in society. Highly recommend reading this paper if you care about where AI is headed: https://lnkd.in/epymXD5M #AI #AIAlignment #AIethics #ResponsibleAI #EmergentBehavior #ArtificialIntelligence

⛳ Deploying AI systems is fundamentally different (and much harder, IMO) than software pipelines for one key reason: AI models are non-deterministic. While this might seem obvious and unavoidable, shifting our mindset toward reducing it can make a significant impact.  The closer you can get your AI system to behave like a software pipeline, the more predictable and reliable it’ll be. And the way to achieve this is through solid monitoring and evaluation practices in your pipeline—a.k.a, observability. Here are a just a few practical steps: ⛳ Build test cases: Simple unit tests and regression cases to systematically evaluate model performance. ⛳ Track interactions: Monitor how models interact with their environment, including agent calls to LLMs, tools, and memory systems. ⛳ Use robust evaluation metrics: Regularly assess hallucinations, retrieval quality, context relevance, and other outputs. ⛳ Adopt LLM judges for complex workflows: For advanced use cases, LLM judges can provide nuanced evaluations of responses. A great tool for this Opik is by Comet, an open-source platform built to improve observability and reduce unpredictability in AI systems. It offers abstractions to implement all these practices and more. Check it out: https://lnkd.in/gAFmjkK3 Tools like this can take you a long way in understanding your applications better and reducing non-determinism. I’m partnering with Comet to bring you this information.