Many discussions about open source AI agents start with the same image: a single assistant responding to prompts. That model works well for demonstrations, but it breaks down quickly in production.
One of our speakers for the AI track at OCX 26, Luca Bianchi, explained in an interview, “a production system generally uses a lot of models, not just one model doing back and forth with the user.” Once systems move beyond experimentation, even common patterns such as retrieval-augmented generation become multi-stage pipelines rather than simple request–response flows.
In theory, this looks straightforward. Knowledge is embedded, queries are encoded, and results are retrieved based on semantic distance.
In practice, production constraints surface immediately. Luca described how, in a real system, similarity scores should have ranged from 0 to 1, but instead clustered between 0.5 and 0.7, making it difficult to distinguish results. Solving that problem required additional steps: re-ranking, metadata-based filtering, query rewriting, and selective composition.
Each of those steps introduces another model into the system. Luca mentioned, “You end up building a complex pipeline of many different models, and this is just for RAG (Retrieval-Augmented Generation).” When teams move further into agentic architectures, orchestration becomes unavoidable. A controlling agent must route requests to sub-agents, each of which invokes its own workflows and models. In production environments, these agentic pipelines place very different demands on latency, cost, and orchestration than a single assistant responding to prompts.
This is where the limits of the “single assistant” model become clear. Latency and cost compound across the pipeline. If each model in the chain takes tens of seconds to respond, the complete pipeline is going to take a very long time.
At that point, system design is no longer about prompts or raw model capability. It is about how pipelines are structured, how responsibilities are split across models, and how orchestration is handled. Production-ready AI agents are not assistants. They are pipelines and their success depends on engineering decisions made early.
In this session at OCX 26 in Brussels, Luca Bianchi will break down how real production-ready AI agent ecosystems are designed, using concrete examples of multi-model pipelines and orchestration. Attendees will gain a practical understanding of how agentic systems evolve beyond single assistants and of the architectural decisions that determine whether those pipelines remain usable at scale.
