From Black Box to Glass Box: The Observability Gap in Agentic AI

Every era of technology promises the benefit first. Visibility always comes later, usually after something breaks and no one can say why. You cannot manage what you cannot monitor, and monitoring has had to evolve with each paradigm shift. Mainframes brought the need for resource measurement of CPU, memory, and job throughput. The client-server era brought the need for network management to understand whether networks were up or down. The storage array era brought the need to understand I/O throughput, capacity, and fault management. The internet era birthed the APM era, where we needed to understand the user experience and code, not just the server. And finally, the cloud microservices era introduced the metrics, events, logs, and traces (MELT) stack, which is the foundation for the term "observability" itself.

With this new era of agentic AI, observability must evolve yet again, but this time it presents a more difficult challenge. We have shifted from deterministic systems, where we can quickly understand whether they are working properly, to non-deterministic systems, where they can fail even though all the signals are showing they are operational. We have entered the black-box era, where AI agents achieve results, but we have no idea how they get there, why they choose certain actions, where actions went wrong, and whether they are providing accurate results. AI observability and reliability enable organizations to build trust in their agents by making agent decisions traceable and their failures explainable. The tools that carried us through every prior era were built for a different kind of failure.

Why Traditional Observability Tools Will Not Work

Organizations have invested heavily in their observability stack to maintain uptime, remediate issues, and improve user experience. But while these are great for existing applications and systems, they cannot tell you what went wrong with agentic AI today. The current observability stack is particularly good at giving quantifiable information such as CPU usage, network latency, page load times, error rates, and much more, but this AI era has moved us beyond quantitative analysis into qualitative analysis. Did our agentic AI provide the desired outcome? Was the outcome accurate? How much did the outcome cost? The need to answer these questions is more closely related to the business rather than maintaining uptime. The following are new challenges our existing observability stack cannot help us solve today.

Silent Failures: Unlike traditional software or systems, agentic systems can fail without an alert reporting a service crashed. Agents can be fully operational, but what can happen is they misinterpret the goal, retrieve the wrong data, hallucinate with false confidence, and even use the wrong tools. Even though the agent provided an output, because it’s the wrong output, it can fail without operators even knowing.

Reasoning Chain Problem: AI has moved beyond simple input and outputs. Each agent can prompt several LLMs, retrieve data from systems, call tools, and even call other agents. Today, we can only see their output is wrong, but not why it went wrong. Within their chain of thought, what step led to the error? Today, most organizations cannot answer this question.

Quality Drift: Separate from providing the wrong output all together, agentic systems can also slowly degrade over time. Teams can build agents, and in testing, they perform well. But once they move to production, there is no way to track their performance until a user complaint surfaces.

Cost Runaway: Outside of the quality of outputs, there is another crucial problem that can happen in agentic environments. These agents can get stuck in infinite loops, which not only creates frustration for the time it takes to get a response, but also churns through tokens in the back end. These wasted tokens can cost organizations a lot of their budget, and there is nothing productive from their spending.

Compounding Errors: Because agentic AI and LLMs are inherently probabilistic or non-deterministic, when you add many steps in a workflow naturally, the probability of getting their correct response goes down. Even if each step has a 95% chance of being correct, if there are hundreds of steps, the probability of having an accurate response goes down simply because of the math.

Moving Beyond the MELT Stack

Compounding errors, silent failures, quality drift; none of them show up as a red light on a dashboard. Solving for that means the MELT stack itself must change. Not only do we have to add capabilities to this, but we must advance what already exists as well. Metrics now need to include things such as token count, token latency, and task completion time. Events and logs need to include prompts, tool calls, agent handoffs, and data retrieval. And traces now need to build the step-by-step process of what the agent actually did to put the story together. While these updates are important, arguably the most important addition the industry needs is evaluation. All the new data is necessary to paint the picture of what happened, but we need to know when to even investigate this new data. This is where evaluation comes into play. Evaluation not only lets operators know when agents are providing the wrong outputs, but it can also provide guardrails in multi-step reasoning chains. By gathering these new metrics and adding evaluation, we can now get full observability into how our agentic AI systems are performing.

Closing the Agentic AI Observability Gap

Knowing what data to collect is only half the job. The harder part is building the operational muscle to act on it. This will typically break out into three phases: organizations must capture telemetry data to gain visibility, establish evaluation frameworks, and implement runtime visibility controls. The following outlines each of these steps.

Phase 1 Capture: Get Visibility First

Step one is knowing what agentic applications you have operating today. Before you can instrument anything, you need to have an inventory of what projects are in flight and who owns them. Once you gain insight into what exists, the next move is to make instrumentation and telemetry gathering a default, not an afterthought. All agentic workloads must ship with these capabilities built in to prevent painful future retrofits. The goal is to have a unified approach across the organization to avoid disparate strategies causing pain in monitoring agentic workloads.

This brings up the question of where to start with instrumentation. Typically, this will break into proprietary instrumentation versus open-source instrumentation. Proprietary instrumentation will give organizations a faster time to set up with provider configuration and integration, a one-stop shop for installation and solution support. Open-source initiatives like OpenTelemetry's GenAI initiative give organizations portability and control, preventing vendor lock-in. Which approach you choose will vary from organization to organization.

Once instrumentation is in place, none of it matters if the data just sits there. This instrumentation needs to feed into a queryable place where you can ask questions over time to understand which agents are underperforming, drifting, costing too much, and quietly failing. Capturing this information is the foundation and the start of the observability journey.

Phase 2 Evaluate: Prove It’s Working

Visibility tells you what happened, but evaluation tells you whether what happened was actually correct and repeatable. This is one of the most important advancements because, unlike telemetry alone, it tells you whether the output was accurate. AI observability is not just a technical discipline to understand speed, performance, and uptime, it is the ability to understand whether agents are providing business value or not. Evaluation gives business leaders concrete evidence of business impact and helps solve one of the most pressing challenges today, which is the return on investment (ROI) of AI. To do this, organizations must define quantifiable metrics to evaluate success and determine whether agents are meeting strategic and operational objectives, similar to the KPIs we use to evaluate teams today. Broadly speaking, this phase breaks into two modes: offline evaluation, where agents are assessed in development, and online evaluation, in which agents are tested in production.

Offline evaluation is performed before deployment against a curated data set that simulates production use cases and tests against performance criteria. This is where the same evaluation criteria that will be used in production are tested to ensure agents are behaving properly before moving them into production. The idea is to establish reliability and repeatability.

Online evaluation monitors the real-world performance and quality of agents in production. This will typically surface edge cases that did not show up in testing, reveal quality drift over time, and inform how to improve offline testing.

Today, evaluations are mostly performed by employee review and feedback. While this is important, it is not scalable enough to meet the needs of agentic workloads. Because of this, there is a multi-pronged approach to evaluating agents that includes human review in combination with deterministic checks and AI review. These are the approaches to include.

Human Review is where a person manually reads an agent's outputs and the chain of thought behind how it got to the answer. While this method is slow and not scalable, it provides the ground truth of whether agents are working properly and creates benchmarks for other evaluation approaches.

LLM-as-a-Judge uses a separate, capable model to score agent outputs for semantic quality. This provides a scalable method beyond human review to determine whether the agent was correct, relevant, and helpful. There is also the concept of agent-as-a-judge, which can offer higher-fidelity judgment because it reasons over more context, but is slower and more resource-intensive to run at scale than a single LLM call.

User Feedback uses signals from the actual user interacting with the agent to determine agent performance. This includes direct feedback, such as thumbs up/thumbs down responses and complaints, and indirect feedback, like behavioral signals.

Phase 3 Govern: Stop Failures Early

Evaluation tells you if an agent is behaving correctly. Governance is what stops it the moment it goes astray. Today, incumbent observability providers only show what happened in the past and require a human response to alerts based on that data. AI agent observability platforms not only give operators a view into what happened in the past, but also provide runtime guardrails for governance. This is important on many dimensions because it helps govern these agents for things such as compliance, cost, and quality in real time. Runtime guardrails give organizations the ability to automatically kill runaway agent loops eating their budget, enforce policies for compliance at runtime to prevent violations, and ensure correct, high-quality responses in production. This shifts observability from showing you what went wrong to stopping it before it happens.

Where to Start Now

Agentic AI has thrown a lot of unfamiliar problems at organizations, and the hard part is knowing where to even start. Today, most organizations cannot tell you why their agents are failing. Start with gaining visibility into these systems by instrumenting telemetry, add in the most important piece, which is an evaluation framework, and lastly, build in runtime visibility and controls. Here is how to break it down into a starting point:

1. Start with Discovery: You cannot manage what you cannot see. Track down every AI agent running across the organization, then set up a process so observability gets baked into new projects from day one, not bolted on later.

2. Evaluate Telemetry Strategy: Determine your telemetry strategy up front. Decide whether you want to leverage proprietary telemetry for quick implementation or open source for portability. Typically, open standards will give organizations longevity and stability since the standard is not tied to a single provider's roadmap or continued existence.

3. Establish KPIs for Evaluation: Treat your AI agents as if they were digital employees. We establish performance metrics for our employees and monitor their performance. We must do the same for our agents. Define metrics tied to actual business outcomes, not just technical uptime, so you can point to real value.

4. Assess Tooling for Runtime Visibility and Controls: Evaluate your tooling not just for historical context and data but for runtime capabilities. In the age of AI, runtime monitoring and governance controls are necessary.

If you’re curious to learn more or want to stay on top of the latest developments in  Innovation, feel free to reach out to us at innovation@trace3.com.

Troy Cogburn is an Innovation Principal at Trace3, where he helps executives and their teams stay ahead of the technology curve. With over a decade of experience in emerging technology research and advisory, he specializes in identifying disruptive technology and translating them into actionable opportunities for the enterprise. Troy calls Colorado Springs home, and outside of work, he enjoys spending time with his wife and daughter, playing hockey, and writing music.