Engineering teams developing natural language-to-database systems are increasingly shifting from single-agent architectures to specialized multi-agent pipelines to handle complex query workloads. Recent development efforts on a text-to-SQL application demonstrated that while single-agent models perform adequately for straightforward operations, they consistently fail under complex, multi-step requirements. The core limitation stems from context window saturation. A single model attempting simultaneous intent decomposition, schema mapping, query generation, and self-validation accumulates failed iterations within its memory, leading to contradictory outputs and degraded performance after minimal retries. To resolve this, developers implemented a sequential multi-agent pipeline leveraging LangGraph for explicit state and edge management. The architecture decomposes the workflow into five distinct nodes. An intent parser isolates and structures analytical requests, preventing premature SQL generation. A dedicated schema agent performs precise table and column mapping, eliminating hallucinated database references. The query builder then constructs syntax, while a critic agent evaluates semantic accuracy against original requirements. A final response node formats outputs for end users. Deterministic routing directs the workflow, as the pipeline sequence remains fixed and predictable, making LLM-based orchestration unnecessary. State management is central to the system. A structured data object carries contextual information across nodes, enabling transparent debugging and targeted error routing. The critic node feeds specific failure reasons back to the query builder during retry cycles, preventing unproductive loops. A strict three-retry ceiling ensures token efficiency by surfacing the best available output rather than perpetuating evaluation spirals. Production deployment reveals several architectural constraints. Context bleed remains a primary risk, where early intent decomposition errors propagate silently through downstream nodes. Large database schemas require vector-retrieval optimization to prevent context overflow. Token costs compound rapidly across sequential calls, necessitating model tiering and rigorous profiling. Furthermore, structured output parsing must include robust fallback mechanisms to handle malformed LLM responses without pipeline collapse. Industry analysts note that multi-agent architectures introduce significant overhead, increased latency, and complex debugging requirements. The approach is strictly reserved for workloads exceeding single-model capabilities. For simple queries and limited schema structures, optimized single-agent systems with retry loops remain more cost-effective and maintainable. Teams are advised to validate single-agent performance against real-world traffic before investing in specialized multi-node deployments. The implemented pipeline establishes a reproducible foundation for reliable, transparent, and auditable enterprise AI systems, prioritizing functional specialization over model scale.