Tools for building AI agents in companies: what the market offers
The agent market combines models, frameworks, orchestrators and connectors. Choosing requires separating the demo from the operational architecture.
By Synthex Team
What makes up an agent
Building an enterprise agent is not a matter of picking a model and connecting it to an interface. Model, instructions, memory or context, information retrieval, tools, permissions, observability, evaluation and a clear error-handling mechanism all come into play.
Models, tools and memory
The model is only one piece. Tools determine what the agent can look up or modify; memory and context determine what information it can use; the control layer decides which actions are allowed. Separating those responsibilities keeps a model improvement from forcing a redesign of the whole process.
Frameworks vs managed platforms
Frameworks can speed up development and provide patterns for orchestration and tools. Managed platforms usually bring connectors, observability and governance. A custom implementation can offer more control, but it also requires maintaining integration, security, evaluation and operations.
Evaluation criteria
Evaluation should look at quality, latency, cost, integration, privacy, traceability, error recovery and maintenance. It also matters how the system behaves on unexpected inputs, because a happy-path demo rarely represents the variety of a real operation.
The demo is not production
A tool can be technically excellent and still not fit a simple process. If a deterministic rule solves the task, adding an agent can introduce more cost and risk than value. Decide which component needs reasoning and which part can remain a normal automation.
Choose by process
The final choice should start from the process: what information comes in, which systems are involved, which actions the agent needs to execute, what permissions it requires and which cases must be escalated. Choose the tool that fits that architecture. Demos and ads are not a reliable guide.