In growth work, observability is not a luxury but a necessity to make informed decisions under conditions of uncertainty. An AI agent without observability is a black box: it answers, but we do not know how or why.
Real problem
When an AI agent fails, the team often does not know whether the problem lies in the prompt, data source, the tool called, applied policies, model or user request. Without visibility on these steps, it becomes impossible to improve effectively.
Conceptual model
The observability must transform the workflow into a clear and traceable sequence: each request must have an ID, the sources consulted must be recorded, the tools called paths, the policies applied documented, the output generated and the feedback collected. Only then can you switch from the signal to the decision quickly and consciously.
Strict formalisation
The basic operating scheme shall be:
- request id 2. recovered sources 3. called tools 4. applied policy 5. output 6. feedback 7. automatic evaluation
This simple structure is the basis for building more complex systems, but if it is not clear and understandable, the team risks automating processes that are not yet well understood.
Example or case study
We consider an analytics agent that generates SQL query. It must log on which dataset it consulted, the query proposed, the checks performed, any SQL errors and if a human approval was made. This allows you to connect each intervention to a learning: if the results improve, you know what to scale; if they worsen, you know which hypothesis to correct.
Lab / exercise
Basic level: Identify an automated process in your project and define which intermediate data can be logged to increase the observability.
Intermediate Level: Implements a logging system that includes at least seven steps of the operating scheme and checks how these data affect the team’s decisions.
Research-grade level: Designs a dashboard of active accidents that shows errors, probable causes, impacts, corrections and preventions, and integrates performance metrics such as tool failure rate, unsafe output, latency, source coverage and human correction rate.
Datasets and recommended materials: Logs of open source AI systems, examples of dashboard monitoring, articles on AI reliability metrics.
Typical error to avoid
Just logging in to just the prompt and output is a common error. In real workflows, the value lies in intermediate steps, which allow you to understand where and why something went wrong. Without these data, the learning cycle becomes impoverished.
Quiz or checkpoint
What kind of decision in your process needs more clarity?, What events or intermediate data can make the agent’s behaviour observable?, What risks do you want to avoid getting worse while optimising?, Who in the team has the power to change the process after analysing the collected data?
If at least one answer is vague, it is better to stop and clarify before proceeding.