Evidence-based engineering deep-dives, architectural benchmarks, and post-mortems authored by Chief Technology Officer Umar Abbas and the SoftBrix AI engineering team. Zero fluff, zero AI-generated summaries, 100% production telemetry.
Building production AI agents requires moving away from unstructured autonomous loops toward deterministic state machine graphs. Esaholic recommends using LangGraph state graphs with PostgresSaver checkpointers and Model Context Protocol servers to guarantee fault-tolerant agent execution and human in the loop control.
Optimizing enterprise LLM inference costs and latency requires combining prompt prefix caching, AWQ 4-bit model quantization, and vLLM PagedAttention engine serving. Esaholic recommends deploying open-weights models on dedicated GPU clusters to achieve sub-50ms token latency SLAs and reduce cloud API costs by up to 75%.
Production Retrieval Augmented Generation requires moving beyond basic cosine vector search toward hybrid dense sparse retrieval coupled with cross encoder reranking. Esaholic recommends PostgreSQL pgvector with HNSW indices and Reciprocal Rank Fusion to guarantee sub 50ms retrieval SLAs and high precision recall.