A change of comprehensive focus, ad hoc tasks for each agent and human validation, among the main keys
Migrations to the cloud continue to be one of the most complex challenges within technological modernization processes. Multiple systems, dependencies that are difficult to identify, critical architectural decisions, and a high workload of manual labor are part of the context they face. Added to this, moreover, is the lack of complete or updated documentation, which increases the risk of failure, lengthens deadlines, and makes projects more expensive.
In this scenario, multi-agent systems begin to gain prominence in the way of approaching development and cloud migrations with greater traceability, less manual effort, and more control capacity. However, for a migration based on agentic AI to be truly effective, it is key to take into account the following aspects:
1. A change in the comprehensive approach: Migration must be managed as an orchestrated process, not as a sum of isolated tasks. A cloud migration does not fail solely due to technical complexity, but due to a lack of coordination between analysis, design, execution, and validation. Agentic AI allows the transition to be structured as an orderly, traceable, and progressive process, where each phase generates deliverables and feeds the next without losing context.
It is not just a migration, it requires context and deep analysis. A migration to the cloud is not based solely on the movement of systems.
Before migrating, one must truly understand what exists and how it relates. Migrating is not just about moving systems from one environment to another. The first step is to analyze repositories, pipelines, configurations, libraries, connected services, and data flows to understand how the current environment is built. This analysis allows for the discovery of hidden dependencies, reduces the risk of production outages, and identifies opportunities for improvement before defining the target architecture.
2. Specific tasks for each agent: The value lies in coordinating specialized agents, not in using a single generic AI. Agentic AI is especially effective when different specialized agents take on specific roles within the process, such as functional analysis, architecture design, implementation, review, or supervision. The key is not just to automate tasks, but to do so with a logic of collaboration between agents that replicates the real dynamics of a technical team.
3. Design based on scalability, resilience, and costs: Starting from the platform that orchestrates different specialized agents, the migration strategy is defined and the most suitable cloud services are selected. This approach allows for the joint analysis of aspects such as scalability, security, resilience, or costs, proposing an architecture that is consistent with both technical needs and business objectives.
4. Automation yes, but continuous human validation too: Specialized software agent systems that replicate usual roles coordinate like a virtual team, hand in hand with human talent. The change in perception is key, as agentic AI must stop being a tool and become part of the team. The expert team is not replaced; instead, agents automate repetitive and structured tasks, while professionals focus on validation, supervision, and critical decision-making. The result is not less control, but a more robust and traceable execution model.
Santiago Ballestero, commercial director of Mática Partners, explains: "The approach developed by Mática combines generative AI and specialized agents that allow structuring and accelerating cloud migrations that, otherwise, would require months of manual analysis and a high consumption of expert resources. This frees up the senior human team so they can focus on higher-value tasks such as control, validation, and decision-making".
"Furthermore, Ballestero points out, our multi-agent system model, Matix, has already been implemented in migration projects with clients and in internal processes, and we have verified that manual effort has been reduced by more than 85% and execution costs have been reduced by more than 50%".