Industry stakeholders are willing to incorporate AI systems in their pipelines, therefore they want agentic flexibility without losing the guaranties and auditability of fixed pipelines. This paper describes ORCHESTRA, a portable and extensible microservice architecture for orchestrating customizable multimodal AI workflows across domains. It embeds Large Language Model (LLM) agents within a deterministic control flow, combining reliability with adaptive reasoning. A Dockerized Manager routes text, speech, and image requests through specialist workers for ASR, emotion analysis, retrieval, guardrails, and TTS, ensuring that multimodal processing, safety checks, logging, and memory updates are consistently executed, while scoped agent nodes adjust prompts and retrieval strategies dynamically. The system scales via container replication and exposes per-step observability through open-source dashboards. We ground the discussion in a concrete deployment: an interactive museum guide that handles speech and image queries, personalizes narratives with emotion cues, invokes tools, and enforces policy-compliant responses. From this application, we report actionable guidance: interface contracts for services, where to place pre/post safety passes, how to structure memory for RAG, and common failure modes with mitigations. We position the approach against fully agentic and pure pipeline baselines, outline trade-offs (determinism vs. flexibility, latency budget), and sketch near-term extensions such as sharded managers, adaptive sub-flows, and streaming inference. Our goal is to provide a reusable blueprint for safely deploying agent-enhanced, multimodal assistants in production, illustrated through the museums use case.

Recent studies suggest that increasing the context window of language models could outperform retrieval-augmented generation (RAG) methods in certain tasks. However, in domains such as art and museums, where information is inherently multimodal, combining images and detailed textual descriptions, this assumption needs closer examination. To explore this, we compare RAG techniques with direct large-context input approaches for answering questions about artworks. Using a dataset of painting images paired with textual information, we develop a synthetic database of question-answer (QA) pairs for evaluating these methods. The focus is on assessing the efficiency and accuracy of RAG in retrieving and using relevant information compared to passing the entire textual context to a language model. Additionally, we experiment with various strategies for segmenting and retrieving text to optimise the RAG pipeline. The results aim to clarify the trade-offs between these approaches and provide valuable insights for interactive systems designed for art and museum contexts.