AI makes writing code, tests, and documentation significantly faster, but faster execution does not guarantee better results. As AI handles more tasks and gains autonomy, it becomes increasingly important to define which decisions it can make, what context it can use, which rules it must follow, and when human intervention is necessary.
In this video, Darío Renzulli, Solution Architect at Flux IT, explores what changes when we stop measuring AI solely by its speed and start considering it part of a system that requires governance.
The Bottleneck Has Shifted from Writing Code to Validating Decisions
One of the most common mistakes is assuming that greater speed automatically means higher quality. AI can produce and generate results much faster, but this does not necessarily mean that those results are better.
The faster AI executes tasks, the more important it becomes to review what is being built and why; in other words, whether what we generate actually addresses the problem we intend to solve. As a result, the bottleneck is no longer specifically in writing code. Instead, it lies in defining problems, establishing context, and validating decisions.
For this reason, quality is set long before the final result. It is built through how we define the problem, the context we provide to AI, the standards we establish, and the criteria we use to validate its response. AI makes software development easier and faster, but sound engineering decisions still depend on the judgment that guides our work.
Measure System Performance, Not Individual Productivity
In terms of speed, if AI changes how work is executed, measuring only how much faster a person can produce is also insufficient. Although this may appear to be a simple metric, it can be misleading: in real projects, incorporating AI not only changes individual performance, but also affects how the entire development system operates.
AI rarely eliminates work in a linear manner. Instead, it primarily redistributes it. For example, we write less code manually, but at the same time, more time is devoted to defining context more precisely, reviewing results, and validating that the solution meets technical, business, and security standards.
If the only measurement is the time required to write code, AI may seem like a success. However, if the goal is to evaluate whether it generates real value, we need to stop focusing on individual performance and start examining the performance of the entire system.
Governance Means Providing Autonomy within a Defined Framework
As agents capable of executing tasks autonomously are incorporated, the question is no longer what AI can do, but rather, “What do we want it to do for the team?”. AI can analyze information, generate code, execute tests, or perform repetitive tasks, but decisions related to architecture, user experience, security, or business priorities still require human judgment because of their potential risk or impact.
This is where governance becomes essential: to establish what context an agent receives, which actions it can execute, which actions require approval, and how its results are supervised.
Governing an agent does not mean limiting its autonomy. It means establishing the framework that allows it to act autonomously without compromising quality, security, or business decisions.
When these criteria are defined from the design stage, governance is no longer an additional control layer applied afterwards. Instead, it becomes part of the development system itself.
Greater Autonomy Requires Organizing Information First
When AI risks are discussed, hallucinations or model errors often receive the most attention. In practice, however, the challenge begins earlier, with the information AI requires to perform its work effectively.
People complete tasks using knowledge they have accumulated over time through architecture decisions, team conversations, business rules, or information that was never documented. An agent cannot operate in the same way because it requires that knowledge to be available and structured.
For this reason, incorporating AI also requires defining a data governance and security strategy, including which information can be used, how it is protected, who manages permissions, and how the traceability of an agent’s decisions is guaranteed. Not all information should be available to every model, and not every task requires the same access level.
The conversation therefore extends beyond just selecting the best model and focuses on establishing a framework that enables the secure and controlled use of information in alignment with organizational policies.
As agents become more autonomous, their reliability increasingly depends on decisions that people have made beforehand: the context they receive, the decisions they can make independently, and the information they can access to. Scaling AI is not a race for speed, but a continuous exercise in designing this decision-making system.