Sales Leader France & Benelux. Responsible for IBM license sales and AEXIS solutions, from scoping to licensing, renewals, and software + services bundles.
When an organization discovers AI agents, the temptation is strong to create a single agent capable of addressing every need. In practice, this logic quickly reaches its limits. Business use cases combine different rules, different tools, and different skills. A strong agentic setup therefore looks less like an omniscient agent and more like an organized team. watsonx Orchestrate makes it possible to design this kind of architecture by enabling several specialized agents to cooperate within a shared workflow. For AEXIS, this approach is particularly compelling because it aligns far better with the operational realities of companies than the simplistic promise of a universal assistant.
Why the single-agent approach quickly reaches its limits
The broader an agent’s role becomes, the greater the risk of vague answers, inconsistent behavior, or excessive dependence on implicit assumptions. This becomes even more visible as soon as multiple tools or business rules come into play.
An overly generalist agent becomes difficult to govern. It also costs more to maintain because it concentrates too many responsibilities in a single definition.
Collaboration between agents enables specialization without fragmentation
A multi-agent architecture makes it possible to distribute roles clearly. One agent can focus on a calculation, another on a validation, and another on a simulation or a specific interaction with a given tool.
This specialization brings more clarity and more robustness. Each agent remains understandable, testable, and improvable within its own scope, while contributing to a broader value chain.
The manager agent becomes a strategic orchestration layer
In this type of setup, a main agent can play the role of coordinator. It understands the request, identifies useful subtasks, calls on the right collaborating agents, and then reassembles the final result into a coherent response.
This logic is particularly valuable for multi-step or multi-domain requests, where a single centralized reasoning process quickly becomes less effective than a clear orchestration between specialists.
A stronger foundation for industrialization and governance
The multi-agent approach is not only more elegant from a technical perspective. It is also healthier from a governance perspective. It is easier to know which agent does what, what data it consumes, which tools it uses, and where validations need to be applied.
This makes testing, auditing, monitoring, and gradual evolution easier. One agent can be improved without having to redesign the entire architecture.
The AEXIS approach: building a team of agents that is useful for the business
At AEXIS, we favor a progressive ramp-up. The goal is not to make the architecture complex from the outset, but to create an initial coherent set of specialized agents where the business value is the clearest.
This method makes it possible to start with a realistic use case, measure the outcome, and then extend the orchestration to other flows or other teams without losing control of the overall setup.