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.

This paper explores hate speech detection in Devanagari-scripted languages, focusing on Hindi and Nepali, for Subtask B of the CHIPSAL@COLING 2025 Shared Task. Using a range of transformer-based models such as XLM-RoBERTa, MURIL, and IndicBERT, we examine their effectiveness in navigating the nuanced boundary between hate speech and free expression. Our best performing model, implemented as ensemble of multilingual BERT models achieve Recall of 0.7762 (Rank 3/31 in terms of recall) and F1 score of 0.6914 (Rank 17/31). To address class imbalance, we used backtranslation for data augmentation, and cosine similarity to preserve label consistency after augmentation. This work emphasizes the need for hate speech detection in Devanagari-scripted languages and presents a foundation for further research. We plan to release the code upon acceptance.