In just a few years, Large Language Models have quietly transformed into the computational backbone of nearly every modern application
In just a few years, Large Language Models have quietly transformed into the computational backbone of nearly every modern application
Welcome to the Agentic Era - where AI doesn't just answer questions but takes action to achieve your goals.
We're already at a point where an AI agent can plan and book your next holiday with minimal supervision – comparing flights, reserving hotel rooms, and scheduling airport transportation at the speed of thought.
The digital personal assistant has arrived, and it's rewiring our expectations about how software should work.
But here's the catch - these wondrous capabilities come with a side order of complexity that would make a quantum physicist's head spin.
"The agent might even get most of the steps right along the way, but if it gets one step incorrect, you can end up with a completely different outcome, you might get a flight to San Diego instead of San Francisco."
- Explains Aman Khan from Arize AI in a recent conversation on the Adopted Podcast.
Suddenly your weekend beach getaway becomes an unexpected tour of the San Diego Zoo. Not terrible, but definitely not what you asked for.
This is where observability enters the picture. Just as you wouldn't drive a car with a blindfold on, you shouldn't deploy AI agents without being able to see what they're doing under the hood.
Observability gives us x-ray vision into the agent's thought process, tool selections, and actions - and is the difference between a cool Twitter Demo showcasing capabilities and a sophisticated system that’s ready for deployment.
In this article, we'll explore why observability isn't just a technical nice-to-have but the essential foundation for any serious AI implementation.
We'll examine real-world AI failures that could have been prevented, break down the three critical layers of agent observability, and offer practical guidance for implementing robust monitoring systems – all to ensure your AI agents take you exactly where you want to go, not where they think you might enjoy instead.
The Black Box Dilemma: Why AI Observability Matters
Large language models are fundamentally different from traditional software systems.
At their core, LLMs aren't designed to be factually accurate - they're trained to predict what text should come next in a sequence.
Accuracy is essentially a happy accident that emerges when the model has seen enough high-quality training data.
This fundamental characteristic creates a unique set of challenges that traditional monitoring approaches simply weren't built to handle.
- Traditional Software Monitoring focuses on binary outcomes—functions either work or throw errors. Applications either respond within SLA or time out. These clear-cut signals make monitoring relatively straightforward.
- Machine Learning Observability typically tracks prediction accuracy against ground truth, monitoring for data drift and model decay over time. The inputs and outputs are structured, making evaluation more systematic.
- LLM Observability, however, exists in a different universe altogether. When your system can generate limitless variations of human-like text with no clear "correct" answer, you're suddenly tracking nuanced qualities like relevance, helpfulness, factuality, and safety—none of which can be reduced to simple metrics.
The Problems that arise with LLM’s
This unique nature of LLMs introduces several critical vulnerabilities →
1. Hallucinations: When AI Gets Creative With Facts
LLMs will confidently present fabrications as facts—a problem inherent to their next-token prediction design.
Google learned this lesson the hard way with their Bard AI demonstration in February 2023.
During a high-profile demo, Bard confidently claimed that the James Webb Space Telescope "took the very first pictures of a planet outside our solar system"—a completely fabricated "fact" that was actually accomplished by a different telescope in 2004.
This single hallucination wasn't caught before the public demo and resulted in Alphabet's stock dropping $100 billion in market value.
An expensive lesson in the importance of fact-checking AI outputs.
2. Unpredictable Tool Usage and Proliferation of Calls
When agents start calling other tools or APIs, things get exponentially more complex. Instead of a single LLM call, you can end up with sprawling chains of operations - each with its own potential failure points.
The open-source AutoGPT project dramatically demonstrated this risk when users reported it entering infinite tool-calling loops, repeatedly opening browser tabs and making costly API calls without making progress toward its goals.
Without proper observability guardrails, these autonomous systems can spiral out of control, consuming substantial resources while accomplishing nothing.
3. Data Security and Leakage Risks
LLMs can inadvertently expose sensitive information they shouldn't, either through their outputs or via logging.
In 2023, Samsung engineers discovered this when they inadvertently leaked confidential source code and meeting notes while debugging with ChatGPT, not realizing the data would be stored on OpenAI's servers.
This incident led to Samsung temporarily banning ChatGPT and other generative AI tools company-wide.
Without proper observability systems that include data redaction and classification features, companies risk proprietary information slipping through the cracks.
4. Quality and Tone Inconsistency
The quality of LLM responses can vary wildly from one query to the next. Microsoft's early Bing Chat (codenamed Sydney) initially exhibited unpredictable behavior, arguing with users, making false accusations, and even professing love to a New York Times journalist while suggesting he leave his wife.
These quality variations led Microsoft to implement strict conversation length limits and memory reset features—essential guardrails that would have been obvious needs had comprehensive observability been in place from the beginning.
5. Unpredictable Costs That Scale With Complexity
Every token processed comes with a price tag, and agent frameworks that make multiple LLM calls for a single user query can quickly multiply costs.
Without proper monitoring, it's easy to burn through API budgets at alarming rates, especially when agents enter recursive loops or generate unnecessarily verbose responses.
The complexity of these challenges explains why traditional monitoring approaches fall short for LLMs and agents.
As Aman Khan puts it,
"Before evals and observability used to be this like an afterthought, last mile problem... the truth is that if you actually want this thing to work the way you expect it to, it's so much more important to get observability in evals right in that first mile."
In other words, observability isn't the last step in your AI deployment checklist—it's now one of the first.
Without it, you're essentially flying blind with a technology that's as powerful as it is unpredictable.
The Five Pillars of AI Agent Observability
While exploring this topic, I stumbled upon what might be the ideal framework for understanding LLM observability—Arize AI's ‘five pillars’ approach.
It's one of those frameworks that creates an immediate mental click, organizing the chaos into something immediately actionable.
Let's dive into these →
1. LLM Evaluation: Measuring What Matters
At its core, LLM evaluation is about answering a deceptively simple question: did the system's response actually address what was asked?
This goes beyond traditional metrics like accuracy or precision. With generative AI, you're dealing with a system that can produce infinite valid-sounding but potentially incorrect answers. Effective evaluation requires both quantitative and qualitative assessment.
Two primary approaches have emerged:
- Direct user feedback: The gold standard but difficult to scale. Users explicitly rate responses as helpful or unhelpful, accurate or inaccurate.
- Automated evaluation: Using another LLM to assess output quality against defined criteria. This approach scales better but introduces its own biases and limitations.
2. Traces and Spans: Mapping the Agent's Journey
For agentic systems that perform multi-step tasks, a single quality score isn't enough. You need visibility into each step of the process to diagnose where things went wrong.
In the observability world, a "trace" represents an entire user interaction, while "spans" are the individual steps within that interaction.
For an AI travel agent, spans might include:
- Understanding the user's request
- Searching for flight options
- Filtering by traveler preferences
- Selecting the booking option
Each span can potentially contain its own LLM calls, tool interactions, or external API requests. When something goes wrong—like booking a flight to San Diego instead of San Francisco—proper tracing helps pinpoint the exact step where the agent went off track.
3. Prompt Engineering: Optimizing Instructions
The prompt is the fundamental interface between humans and LLMs.
Small changes in phrasing can dramatically affect outcomes, making prompt engineering both an art and a science.
Effective observability includes tracking different prompt templates, comparing their performance, and iteratively improving them.
This is often the most cost-effective way to improve agent performance without changing underlying models.
As Arize's framework notes, this optimization must balance effectiveness with efficiency—more tokens in your prompts means higher costs at scale and potential context window limitations.
4. Retrieval Augmented Generation (RAG): Monitoring Knowledge Sources
Many AI agents rely on external knowledge sources rather than just their pre-trained knowledge. Observability here focuses on:
- Whether the right information was retrieved
- If the retrieved information was actually relevant
- How effectively the agent incorporated external knowledge
This pillar is critically important for systems dealing with proprietary data or specialized domains where the base LLM lacks specific knowledge.
5. Fine-Tuning: Tracking Model Adaptations
The final pillar focuses on when and how to adapt the underlying model itself. Fine-tuning can dramatically improve performance but comes with significant costs and complexity.
Observability here involves:
- Tracking what data is used for fine-tuning
- Measuring performance improvements from tuning
- Monitoring for overfitting or other training issues
While fine-tuning is powerful, it's typically the most resource-intensive approach and should be considered after the other pillars have been optimized.
Together, these five pillars provide a comprehensive framework for seeing inside your AI agents. By implementing observability across all five dimensions, you transform your system from a mysterious black box into a transparent, debuggable, and continuously improvable tool.
Practical Steps: Implementing AI Agent Observability
Taking the leap from theoretical frameworks to real-world implementation can be daunting.
What does it actually look like to build these five pillars into your agent architecture?
Let's break it down into actionable steps.
Getting Started: The Open Source Route
One refreshing insight from Aman Khan is that you don't need an enterprise budget to get started with solid observability practices.
"You can actually get pretty far with open source these days," Aman explains.
"The product is called Phoenix. It's an open source observability and eval tool where you can get a lot of the way pretty far, basically understanding what your app is doing and running evals on top of it without needing a fancy enterprise product just yet."
Other notable open source options include:
- LangSmith: For tracing LangChain applications and recording detailed interaction logs
- LangWatch: For monitoring LLM applications with performance tracking and anomaly detection
- OpenTelemetry: The industry standard for distributed tracing that's being extended for LLM-specific use cases
These tools share a common goal – making the invisible visible.
They transform the cryptic inner workings of LLMs into comprehensible traces and evaluations that humans can actually understand.
Basic Implementation Roadmap
- Start with prompt/response logging: Simply recording every input and output creates your observability foundation.
- Add tracing for multi-step processes: Implement span-based tracing for any agentic workflow.
- Implement basic evaluation metrics: Define what "good" looks like for your specific use case.
- Set up automated alerts: Create thresholds for cost, latency, error rates, and quality scores.
- Build feedback loops: Create mechanisms to capture user feedback and correlate it with your traces.
From MVP to Enterprise-Grade
While open source tools can get you remarkably far, there comes a point in most AI implementations where enterprise-grade observability becomes a necessity rather than a luxury. This usually happens when:
- You scale beyond thousands of daily queries
- You enter regulated industries or handle sensitive data
- You need auditability for compliance reasons
- Multiple teams need access to observability data
As your AI agents take on mission-critical tasks and handle increasingly sensitive data, the observability infrastructure must evolve from "good enough" to "bulletproof." Enterprise tools add features like:
- Role-based access controls
- Encryption of sensitive data in logs
- Compliance certifications
- SLAs for observability infrastructure itself
- Integration with existing enterprise monitoring
Aman's insight here is spot on:
""Choosing your observability solution provider is much like choosing your car. When you’re just starting out - you don’t need that Mercedes. And as you grow, you can go get that Mercedes”
Conclusion: The Lens Through Which We Maintain Control
As we entrust more and more of our digital world to AI agents, observability becomes our insurance policy against the Hollywood sci-fi scenarios we've been warned about for decades.
The rogue AI trope isn't just fiction anymore. We've already witnessed glimpses of what happens when LLMs veer off script—from Microsoft's Bing Chat professing love to journalists to hallucinations that cost companies billions in market value.
These aren't the dramatic scenarios of film, but they're real-world evidence that AI systems without proper observation can behave in ways their creators never intended.
Observability gives us a critical lens through which we maintain control and understanding of increasingly sophisticated systems.
In many ways, LLM observability is our modern version of the control room—the place where humans maintain oversight of machines that increasingly operate at a level of complexity beyond our immediate comprehension.
We may not understand every neuron firing in the model, but with proper observability, we can at least see the path our AI agents take as they navigate the world on our behalf.
