Explore how AI observability transcends traditional monitoring to track model behavior, agent decisions, and cost, plus a practical framework for managing AI agents in production.
Once an AI agent becomes part of operations, success is no longer defined only by infrastructure health metrics like uptime, but also by the quality, cost, and safety of the outcomes the agent produces. But what does this mean in practice?
Imagine you own a logistics company, and your business's AI-powered support assistant shows a sudden spike in customers flagging conversations as unhelpful. Although operational metrics are normal, you soon discover that the assistant has been giving incorrect answers on edge cases, confusing customers, and triggering repeated follow-ups.
Meanwhile, token costs are quietly climbing, and the cost per interaction is much higher than expected. The system appears healthy on the infrastructure level, but the AI's incorrect responses degrade the customer experience, and the unexpected costs are bleeding budgets dry.
This is the core problem that observability is designed to solve. Traditional observability focuses on system health: tracking availability, performance, and errors across infrastructure and applications. AI observability extends traditional observability to explain the behavior of AI models and agents, uncover where value is gained or lost, and understand how agent behavior accumulates cost.
In this article, we'll take a closer look at AI observability to understand what it is, how it differs from traditional software observability, the strengths and limitations of available AI observability tools, and how to build a complete framework for observing AI in production.
What AI Observability Means Beyond Traditional APM
AI observability is the practice of observing AI applications to understand how they work and to diagnose issues if they arise. A complete observability strategy builds on traditional observability pillars such as logs, metrics, and traces, but extends them to include AI-specific metrics collected from across the tech stack. These metrics include token usage, model drift, response quality, agent reasoning paths, and tool call chains.
Together, they provide administrators with better insight into the AI system's inner workings, enabling them to eliminate bottlenecks, reduce hallucinations and latency, and more effectively manage costs alongside other common issues.
AI observability is often confused with AI monitoring. But AI monitoring is a component of AI observability, not a substitute for it. Monitoring tells you when something crosses a threshold; observability explains why.
For instance, while monitoring might surface a spike in error rates, observability will reveal which model version caused it, how input patterns have shifted, and whether similar issues have occurred before.
How Does AI Observability Differ From Traditional APM?
AI observability differs from traditional APM in three specific ways: it tracks probabilistic rather than deterministic outputs, captures AI-specific signals like token usage and model drift, and traces multi-step agent workflows instead of single-process executions.
Traditional application performance monitoring (APM) was built on the assumption that software behaves predictably. Software either works or it doesn't, performance stays within expected ranges, and when something breaks, there's a clear reason for it. This predictability makes it relatively straightforward to monitor, troubleshoot, and debug the performance of traditional software systems.
For example, when a web server returns a 500 error, it's often due to a crashed server code or a dependency failure — a database, an API, and so on. Conventional observability tools are enough to help you trace the issue and fix it. These tools understand the internal state or condition of a complex system using the three pillars of observability: logs, traces, and metrics.
AI applications do not behave like traditional deterministic software. Their outputs are probabilistic, meaning identical inputs can produce different responses. This unpredictability makes troubleshooting, debugging, and performance monitoring much more complex in AI systems, requiring unique observability tools.
Key Signals: Latency, Errors, Traces, Token Usage, Cost
AI systems naturally generate telemetry that is distinct from traditional application and infrastructure monitoring data. When collected and analyzed, these signals provide AI teams with complete end-to-end visibility into the health, performance, and cost of AI in production.
Latency
Latency in AI systems is the time between sending a request and receiving a complete response, including model inference, tool calls, and any retries along the way. Tracking latency is critical for user-facing AI applications like chatbots and copilots, where even small delays matter. Beyond user experience, latency affects system performance, and monitoring latency metrics can help you uncover performance bottlenecks and computational inefficiencies.
Errors and Output Quality
Unlike traditional software systems that fail predictably, AI systems fail in unpredictable ways: through hallucinations, subtle inaccuracies, or gradual model drift. Key metrics for assessing output quality include:
- Factual accuracy: Ensuring the model's outputs are correct and aligned with real-world facts or trusted sources.
- Hallucination frequency: Tracking how often the model generates information that is not grounded in known data. Hallucinations are especially problematic because the response may sound plausible but be false.
- Response relevance: How well the model's output matches what the user actually asked for. A response can be correct and factual, but irrelevant if it doesn't address the user's goal.
- Output consistency: How much quality fluctuates across similar inputs. Large variations suggest instability in model behavior.
These metrics are collected through a combination of automated system evaluations, real user feedback, and periodic manual reviews.
Traces
End-to-end tracing captures sequences of model calls, tool usage, retries, and decision paths. For agents, this is particularly important. Full trace visibility allows teams to diagnose bottlenecks and computational inefficiencies and understand how different components interact to produce final outputs. Without it, you can see that something went wrong; you just can't see where.
Token Usage
A token is a chunk of text — a word, part of a word, or a symbol — that an AI model uses as its basic unit for processing language. Tokens matter because they determine cost (most AI models charge per token), performance (more tokens mean more processing time), and limits (models have maximum token limits per request).
Tracking token usage patterns over time can reveal:
- Token consumption rates and costs, which help in assessing operational expenses
- Token efficiency, which determines if tokens are used without wastage, with the goal of producing high-quality outputs while minimizing the number of tokens consumed
- Token usage patterns across different prompt types, which are effective at identifying resource-intensive uses of models
When paired with cost data, this provides a clear view of how spending accumulates and where optimization opportunities exist.
Cost
Token usage only tells part of the story. Complete visibility into AI cost requires tracking spend across model calls, tool invocations, external API calls, and retries, and tying these metrics to business outcomes. This enables organizations to determine not just what was spent, but whether it was justified.
Monitoring system resource use also belongs here. AI workloads place significant demands on infrastructure, making it important to monitor GPU usage, memory consumption, and overall compute efficiency. Inefficient use of system resources leads to longer processing times and higher costs per request.
Bottlenecks in network infrastructure, such as slow data transfer or problematic dependencies, can further degrade performance by increasing latency and forcing the system to wait on external services. These delays often lead to retries, longer execution paths, and increased token usage, all of which drive up operational costs.
How LLM Observability Differs From Agent Observability
AI observability is more complex than traditional software observability, and it becomes even more challenging as we move from large language models (LLMs) to agent-based systems.
A standard LLM's workflow is relatively straightforward to trace: a prompt goes in, a response comes out, and key metrics such as the number of tokens used, latency, model version, and cost can be captured from that interaction. If something goes wrong during that process, the failure surface is contained because there are no branching paths, tool calls, or intermediate steps that could introduce errors.
An AI agent, on the other hand, takes the initiative to accomplish tasks using whatever tools it has access to. It can break a task into multiple steps, query APIs, interact with other systems, and make sequential decisions, with each step influencing the next.
For instance, say a software developer asks their GitHub Copilot agent to “set up a simple Python project.” That simple request will trigger a sequence of actions that includes generating a project structure, creating files, installing dependencies, writing boilerplate code, and even suggesting testing setup and configuration.
From the developer's perspective, it may look like a single action. But in reality, it's a coordinated sequence of steps. Token usage, cost, and potential points of failure accumulate across the entire execution path.
This requires expanding the scope of AI observability to account for:
- Multi-step execution flow: Where each step is an independent inference call with its own cost, latency, and context
- External tools and API calls: With additional latency, cost, and potential points of failure
- Loops and retries: Because re-attempting tasks or re-evaluating decisions can multiply costs and execution time
The fact that agents act autonomously makes expanding observability imperative. When an agent can reach the same outcome through different paths, it is in the organization's economic interest to ensure it takes the most direct and efficient route. Without visibility into how agents operate internally and how costs accumulate across their workflow, it becomes harder to tell whether a task was completed efficiently or whether money was wasted along the way.
The Gap Between Performance Observability and Economic Observability
Traditional observability answers the “is the system working?” question, monitoring infrastructure KPIs such as latency, error rates, throughput, and uptime. These signals are essential, and no AI observability strategy is complete without them. But for AI workloads, they are not sufficient on their own.
Performance observability tells you that the agent completed the task in 400ms. It doesn't tell you that the agent made three API calls when one would have sufficed, or that a pagination bug quietly inflated token usage on every similar request, or that the model upgrade was triggered by a reasoning heuristic that wasn't calibrated for cost. This is the gap that Economic Observability fills.
Economic Observability is the discipline of treating cost, value, and business outcomes as first-class signals in the AI observability stack, alongside latency and model output accuracy. Where performance observability asks, “Is the AI agent up and creating the right outcomes?”, Economic Observability asks: “Are the agent's decisions creating measurable business value relative to their cost, and how can that trade-off be optimized?”
Consider a concrete example. Your company has deployed an AI customer support agent inside its helpdesk system. A customer submits a support ticket asking why their invoice amount changed. The agent receives the query and calls the billing API to retrieve the invoice. The API returns a partial response due to a pagination issue the agent isn't handling correctly, so the agent calls the billing API again.
It then calls the customer account API to pull the customer's plan details, realizes it needs pricing history to explain the change, and makes a third call to a pricing API. At some point in the reasoning chain, the agent decides the query is complex enough to route to a more capable (and more expensive) model. Eventually, the agent responds in 400ms, and the support ticket is resolved.
Three issues are hiding inside that workflow:
- The agent made three API calls when one well-structured call would have sufficed
- The pagination issue quietly inflated token usage on every similar query
- The model upgrade was triggered by a reasoning heuristic that wasn't calibrated for cost
Assuming the AI agent processed a thousand of these requests per day, the business would incur $400 in daily costs for a workflow that could have been optimized. Insights like these are only visible when observability also tracks the agent's execution path and running cost at the request level.
It's worth noting that a high-cost workflow is not inherently a problem. If an agent spends $2.00 to complete a task that generates $50 in revenue or resolves a complex issue that would otherwise require human intervention, that cost may be entirely justified. What matters is cost relative to outcome, and whether that cost can be reduced by optimizing the agent's workflow.
Without tools for Economic Observability, organizations lack the context to make informed trade-offs between speed, quality, and cost. They might optimize for latency and inadvertently increase cost. Or they might reduce costs by switching models while quietly degrading output quality. Economic Observability provides the unified view across performance, cost, and outcomes needed to make the right trade-offs.
“Is It Working?” vs. “Is It Worth It?” — Why Both Questions Matter
Performance observability and Economic Observability are not competing frameworks. They answer different questions, and organizations need both.
“Is it working?” covers the reliability and correctness dimension: Is the agent completing tasks? Are responses accurate? Is latency acceptable? Are error rates within the right threshold? These questions map to the traditional observability pillars of latency, errors, and traces, and must be answered before anything else. A system that isn't working correctly cannot be optimized for value.
“Is it worth it?” covers the economic dimension: Is the agent completing tasks efficiently? Does the business outcome justify the money spent on producing it? Are there patterns of waste or unnecessary model upgrades? Are high-cost workflows producing proportionally high business value? These questions can only be answered when cost and outcome data are tracked at the task level, not just in aggregate.
In practice, the two questions interact in important ways. A team that only asks “Is it working?” may operate an agent that runs reliably but hemorrhages budget on inefficient workflows. A team that only asks “Is it worth it?” may cut costs in ways that quietly degrade output quality. Neither is good observability.
The goal is to have a unified view of what specific workflow is slow, expensive, and producing lower-quality outputs — and then trace exactly why, fix the routing logic, and verify that both performance and cost improved after the change. This is where having the right tooling becomes essential.
The Current Tooling Landscape: Open-Source and Vendor Options
Several tools have emerged to address AI observability, each with meaningful strengths and real limitations. No single platform covers every need, so it's worth understanding what each category does well and where it falls short.
LangSmith
LangSmith offers a polished, well-integrated experience — especially for teams already using LangChain — with a solid UI playground, prompt versioning via SHA-based commit IDs, and SDK support for CI/CD pipelines.
However, it's closed source with no self-hosting option (unless on an enterprise plan), and its core weakness is that it treats prompts as isolated text templates disconnected from the surrounding application logic, which can cause drift and make debugging harder over time.
On the economics side, LangSmith surfaces basic metadata such as token counts and costs per trace, but doesn't offer deeper financial tooling around budget controls, cost attribution, or ROI measurement.
Langfuse
Langfuse addresses the self-hosting concern by being open-source, and it adds useful features such as prompt composability (referencing one prompt within another) and integer-based versioning with label pointers like “production” or “staging.” Its SDK makes it easy to update prompts without redeployment.
That said, like LangSmith, it still separates prompts from the codebase, so the surrounding logic, model settings, and runtime context aren't captured alongside the prompt text. Its prompt retrievals are also not type-safe, which can introduce subtle bugs.
From an Economic Observability standpoint, Langfuse tracks latency, token usage, and cost at the trace level, making it useful for spotting inefficiencies. But like LangSmith, it stops short of true economic governance or meaningful spend attribution across teams or products.
Datadog
Datadog is a feature-complete out-of-the-box option, with 800+ integrations and a unified platform covering logs, metrics, APM, security, and now LLM monitoring. Its observability pipelines — which let you preprocess and route telemetry before it's stored — are particularly valuable for cost control at high volumes. The tradeoff is that this breadth comes at a price, both literally and in terms of complexity. It's best suited to larger engineering teams running cloud-native architectures who can justify the spend and configuration overhead.
When it comes to AI economics, though, Datadog's cost visibility stays fairly surface-level. You can track token usage and API call volumes, but connecting those to business outcomes or per-unit cost attribution requires considerable custom work.
Grafana
Grafana is an open-source full-stack observability platform, offering unmatched flexibility through its modular stack (Loki, Tempo, Mimir) and deep integration with tools like Prometheus and Elasticsearch.
It’s big on customization and avoids vendor lock-in, but it demands real engineering investment to set up and maintain. Teams that want control over every layer of their stack will love it; teams that want something working on day one may find it heavy going. Its managed cloud offering closes some of the gap, adding AI/ML features and synthetic monitoring.
Like Datadog, however, its cost tracking capabilities are general-purpose. Meaningful AI economic visibility has to be largely hand-rolled on top of it.
New Relic
New Relic sits comfortably in the middle — simpler and more developer-friendly than Datadog, less raw than Grafana. Its APM 360 view and usage-based pricing make it accessible to smaller or dev-first teams that need full-stack visibility without a massive operational footprint.
It doesn't go as deep as the others in any single dimension, but its clarity and affordability make it a solid all-rounder for teams that prioritize usability. That said, like its peers, it wasn't designed with AI economics in mind.
Revenium
Revenium focuses on the economic layer of AI systems. Where traditional observability tools track what the AI agent did, Revenium links each of the agent’s actions (model call, tool invocations, etc.) to the workflow that triggered it, and the business outcome it produced.
It’s a modern AI Economic Control System that enables organizations to answer Economic Observability questions that other tools can't: which workflows are cost-inefficient, which model calls are unjustified given their output quality, and where optimization would have the highest financial impact.
What Should You Look for in an AI Economic Control System?
To track AI spending and measure return on investment, you need a platform with these core capabilities:
- Cost attribution: It’s not enough to know you spent $2,000 on AI last month. You need to know exactly which features, workflows, or customers drove the cost. Your tool should be able to assign the full, detailed expenses of your AI models — including compute, tokens, and storage — to specific products, teams, or projects.
- Visibility into inner workings of models: A good platform should show the full sequence of steps for each task (model calls, tool use, retries) so you can spot where things are inefficient. This is especially crucial for monitoring autonomous agentic workflows.
- Linking cost to results: Cost data alone isn’t very useful. Your tool should also be able to tie costs to measurable results, like resolved support tickets, completed purchases, or new leads. This helps you tell the difference between spending that’s worthwhile and spending that isn’t.
- Controls to manage spend: A strong platform should let you set limits, prevent wasteful usage, and guide how models are used based on cost and quality.
- A shared view across teams: The best results come when engineering, finance, and product teams all see the same data. It’s harder to make good decisions when cost data and KPIs live in siloed tools, often requiring manual correlation.
Without these capabilities, AI costs remain hard to explain, harder to control, and nearly impossible to tie to real business value.
Build Observability Into Your Agentic AI Stack
Unlike a single LLM call with a bounded cost and a contained failure surface, agents can make autonomous decisions that compound across multiple steps, tools, and model calls. Without observability that traces the full execution path and connects it to cost and outcome, you don't have visibility. You have logs.
Revenium is built for AI teams that need that full picture. Its AI Economic Control System connects AI usage data to cost, performance, and monetization in one place, giving engineering, FinOps, and product teams the visibility to understand, control, and act on their AI spend in real time.
Sign up for free and try it for yourself.
