Artificial Intelligence (AI) observability refers to the ability to understand, monitor, and evaluate AI systems by tracking their unique metrics such as token usage, response quality, latency, and model drift. Unlike traditional software, large language models (LLM) and other generative AI applications are probabilistic in nature. They do not follow fixed, transparent execution paths, making their decision making difficult to trace and reason about. This “black box” behavior creates challenges for trust, especially in high-stakes or production-critical environments.
AI systems are no longer experimental demos – they are production software. And like any production system, they require observation. Traditional software engineering has long relied on logging, metrics, and distributed tracing to understand large-scale system behavior. As LLM-powered applications move into real user workflows, the same discipline is becoming necessary. To operate these systems reliably, teams need visibility into what happens at each step of the AI pipeline, from inputs and model responses to downstream actions and failures.
Let us now understand the different layers of AI observation with the help of an example.
Think of an AI resume screening system as a sequence of steps rather than a black box. A recruiter uploads a resume, the system processes it through several components, and finally returns a shortlist score or recommendation. Each step takes time, has costs associated with it, and may fail individually. Looking only at the final recommendation may not give the full picture, as finer details may be missed.
This is why mark and span are important.
Mark
A trace represents the entire lifecycle of a single resume submission – from the moment the file is uploaded until the final score is returned. You can think of it as a continuous timeline that captures everything that happens for that request. Each trace has a unique trace ID, which links all related operations together.
spread to
Each major operation inside the pipeline is captured as a span. These spans are contained within the trace and represent specific pieces of work.
In this system those spans look like this:
upload span
The resume is uploaded by the recruiter. This span records the timestamp, file size, format, and basic metadata. The trace starts from here.
parsing span
The document is converted to structured text. This captures span parsing time and errors. The problem appears here if the resume fails to parse correctly or the formatting is broken.
feature extraction period
The parsed text is analyzed to extract skills, experience and keywords. It tracks span latency and intermediate outputs. Poor extraction quality becomes visible at this stage.
scoring period
The extracted features are passed into a scoring model. It logs the span model latency, confidence score, and any fallback logic. This is often the most calculation-intensive step.
decision period
The system generates a final recommendation (shortlist, reject or review). This span records output decisions and reaction times.
Why span-level observability matters
Without span-level tracing, you just know that the final recommendation was wrong – you have no visibility into whether the resume failed to parse correctly, key skills were missed during extraction, or the scoring model behaved unexpectedly. Span-level observability makes each of these failure modes obvious and debuggable.
It also reveals where time and money are actually being spent, such as whether parsing latency is increasing or scoring calculations are dominating costs. Over time, as resume formats evolve, new skills emerge, and job requirements change, AI systems can quietly break down. Monitoring spans independently allows teams to quickly detect this drift and fix specific components without having to retrain or redesign the entire system.
AI observation provides three main benefits: cost control, compliance, and continuous model improvement. By gaining visibility into how AI components interact with the broader system, teams can quickly spot wasted resources – for example, in a resume screening bot, observations may reveal that document parsing is lightweight while candidate scoring consumes the majority of the compute, allowing teams to optimize or scale resources accordingly.
Observability tools also simplify compliance by automatically collecting and storing telemetry such as inputs, decisions, and timestamps; In Resume Bot, this makes it easy to audit how candidate data was processed and demonstrate adherence to data protection and hiring regulations.
Finally, rich telemetry captured at each stage helps model developers maintain integrity by detecting drift as resume formats and skills evolve over time, identifying which attributes really influence decisions, and uncovering potential bias or fairness issues before they become systemic problems.
Langfuse is a popular open-source LLMops and observability tool that has grown rapidly since its launch in June 2023. It is model- and framework-agnostic, supports self-hosting, and integrates easily with tools like Open Telemetry, Langchain, and the OpenAI SDK.
At a high level, Langfuse provides teams with end-to-end visibility into their AI systems. It provides tools to trace LLM calls, evaluate model outputs using human or AI feedback, centralized instant management, and dashboards for performance and cost monitoring. Because it works on a variety of models and frameworks, it can be added to existing AI workflows with minimal friction.
Arize is an ML and LLM observation platform that helps teams monitor, evaluate, and analyze models in production. It supports both traditional ML models and LLM-based systems, and integrates well with tools like Langchain, Lindex, and OpenAI-based agents, making it suitable for modern AI pipelines.
The open-source offering (licensed under ELV2), from Phoenix, Ariz., focuses on LLM overview. It includes built-in hallucination detection, detailed tracing using open telemetry standards, and tools to observe and debug model behavior. Phoenix is designed for teams that want a transparent, self-hosted overview of LLM applications without relying on managed services.
TruLens is an observational tool primarily focused on the qualitative assessment of LLM responses. Instead of emphasizing infrastructure-level metrics, TrueLens adds a feedback function to each LLM call and evaluates the feedback generated after it is generated. These feedback functions behave like models themselves, scoring or evaluating aspects such as relevance, coherence, or alignment with expectations.
TrueLens is Python-only and available as free and open-source software under the MIT license, making it easy to adopt for teams who want lightweight, react-level assessments without a full LLMOPS platform.
I am a Civil Engineering graduate (2022) from Jamia Millia Islamia, New Delhi, and I have a keen interest in Data Science, especially Neural Networks and their application in various fields.
