Observability

What it is

Observability is the evolution of traditional monitoring. While monitoring focuses on knowing if a system is 'live' or 'dead' (through binary metrics and alerts), observability allows us to understand why something is failing. In the context of AI applied to SMEs, it is not enough to know that your customer support assistant gave a wrong answer; you need to know if the error came from the database, the language model (LLM), a failure in information retrieval (RAG), or a misinterpreted instruction.

For a company using AI systems integrated into its processes, observability is the 'control panel' that allows a look inside the black box. It is the difference between saying 'the system is slow' and saying 'the system is slow because the vector search step took 3 seconds due to an outdated index'.

How it works

Observability relies on three fundamental pillars, often referred to as the 'three pillars of modern monitoring':

1. Logs: Records of specific events occurring in the system. For example, the exact record of the question asked by the user and the response generated by the model.
2. Metrics: Aggregated and measurable data over time, such as latency (response time), token cost per request, or the success rate of API calls.
3. Tracing: This is the key piece for AI. Tracing allows you to follow the complete path of a request from the moment the user clicks a button to the delivery of the final response. In an AI architecture, this includes seeing how long it took to load the context, the LLM call, and the formatting of the output.

By crossing these three elements, engineers and product managers can reconstruct the history of any failed interaction, identifying exactly where the AI value chain broke down.

When to use

An SME should invest in observability tools and processes the moment its AI application moves from the prototype phase to the production phase. If the system is being used by real customers or in critical internal processes, observability is mandatory.

It is especially useful when:

• Costs start to rise: Without visibility, you do not know which functions are consuming more tokens unnecessarily.
• Response quality is intermittent: It helps diagnose the 'hallucination' phenomenon by allowing you to see the exact context passed to the model at the time of the error.
• There are complex integrations: If your AI system communicates with your CRM or ERP, observability allows you to distinguish whether the error is from the artificial intelligence or a communication failure with the legacy software.

Common errors

The most frequent error in SMEs is confusing monitoring with observability. Many companies set up an alert that notifies when the system goes down, but when customers complain that the AI 'is acting stupid,' the technical team lacks the data to investigate the problem. Another common error is the excessive collection of data without criteria: saving all logs without tools to filter them results in informational noise that costs money in storage and does not solve problems.

Finally, ignoring the cost of observability itself. In large-scale AI systems, tracing tools can generate significant costs if not configured for sampling—that is, analyzing only a representative percentage of interactions instead of 100% of them.

Practical example for an SME

Imagine a real estate agency that uses an AI agent to qualify leads via WhatsApp. The system uses RAG to consult the database of available properties.

Without observability, the agency only notices that the volume of bookings has dropped. With observability, the IT manager accesses a dashboard and identifies that over the last two days, 30% of interactions failed at the 'database search' stage. By investigating the trace of a specific interaction, they discover that the new address format in the CRM is not being recognized by the embeddings system. The problem is not with the AI model (ChatGPT or another), but with the data integration. The fix is made in minutes, saving weeks of guesswork.

Frequently Asked Questions

Q: Does observability increase the cost of my AI solution? A: Yes, there is a marginal infrastructure and management cost, but this is largely offset by reduced downtime and the ability to optimize token spending by identifying redundancies.

Q: Do I need a dedicated engineer to manage observability? A: For most SMEs, no. There are modern (SaaS) platforms that automate much of the data collection, requiring only a correct initial configuration by the development team.

Q: Does observability help with security? A: Yes. By monitoring input logs, it is possible to detect attack patterns such as 'prompt injection,' where users try to force the model to ignore its security rules.

Q: What is the difference between Observability and Evals? A: While Evals (evaluations) test model performance in controlled or predefined scenarios, Observability focuses on what is actually happening in real-time with real users.

• 01Detecting that a model update made responses 40% slower for customers in Europe.
• 02Identifying that 15% of chatbot failures stem from connection errors with the company's billing software.
• 03Tracking the exact cost of each AI query per department for annual budget optimization.
• 04Visualizing the data flow to understand why the model hallucinated in a specific technical response.

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