Continuous Improvement: Human Teams vs Systems of Agents
In human teams, continuous improvement is focused on individual development. Through feedback, training, and experience over time, the manager seeks to evolve the skills and performance of each team member.
This is a gradual process and often subjective. It depends on each person’s capacity and motivation to learn and adapt.
Already in agent systems, the logic is different. Improvement is not an automatic or natural process, but rather an active and structured effort conducted by the manager.
Here, the focus is not on developing individuals, but on evolving the system itself. The manager is responsible for identifying improvement opportunities, testing different ways of executing the work, comparing results, and adjusting the system.
This opens a range of possibilities. With agent systems, it is possible to test multiple approaches in parallel and compare results objectively.
For example: Let us imagine that you have a system of agents responsible for responding to customers in a customer service chat.
After some time of operation, you notice that although the answers are correct, they are very long and not very objective.
If we were talking about a human team, the approach would be to give individual feedback to each attendant, guiding them on how to be more direct in their responses, and then monitor the evolution over time.
But with an agent system, we can do differently. We can create two versions of the customer service agent:
- One version with instructions to provide more detailed and complete answers.
- Another version with instructions to give shorter and more direct answers.
Next, we put both versions to operate in parallel, serving real customers. After a test period, say a week, we compare the results:
- Which version solves problems more quickly?
- Which generates fewer doubts and requests for clarification from customers?
- Which version reduces the need for additional interactions to resolve the same case?
Based on this objective data, we can determine which approach works better - detailed or direct answers. We then keep the winning version and discard the other.
And if even the winning version is still not perfect, we can iterate. We can create a third version, adjusting the points that are still not ideal, and test again.
This cycle of testing, comparing, and refining can be repeated continuously, always seeking incremental improvement of the system.
You can create different versions of the same agent, experiment with alternative workflows, or adjust the context and how the agent operates. All of this can be done quickly and precisely, directly in the system configuration.
Furthermore, improvement can be guided by data and evidence. Each execution of the system generates logs and outputs that can be analyzed for error patterns and improvement opportunities.
This systematic analysis allows for a much more objective and effective approach than relying solely on perception or opinion.
Another advantage is the possibility of incorporating validation and quality control mechanisms within the system itself. One agent can review the work of another, checkpoints can be established between steps, advancement criteria can be defined.
In this way, quality ceases to depend solely on external supervision and becomes an integral part of the system design.
However, this approach also brings challenges. It is necessary to define clear and objective metrics to measure performance and compare alternatives.
It is also necessary to find the balance between testing new approaches without losing what already works well. And ensure that improvements work consistently, not just in specific cases.
But perhaps the biggest challenge is the mindset shift necessary to embrace this new approach. Managers need to move from a vision of “people development” to “system evolution”.
They need to see testing different approaches and comparing results as central to their work. And they need to trust data and evidence, not just intuition or experience.
When this mindset shift happens, the possibilities are enormous. Customer service chatbots can be continuously improved, recommendation systems can be optimized, workflows can be simplified.
Continuous improvement becomes a systematic and data-driven process that drives system performance forward with each iteration.
In short, in the age of intelligent agents, continuous improvement is no longer just about developing people. It is about evolving systems in a structured, fast, and evidence-based way.
And the manager is at the center of that transformation. No longer just as a coach or mentor, but as a system designer and analyst, always testing, always comparing, always improving.