Deep dives into Agentic Workflows, distributed systems, and the architectural rigor required to move AI from experimentation to enterprise-grade production.
Retrieval-Augmented Generation (RAG) architectures must be engineered as production-grade distributed systems. This article provides concrete technical specs.
Telecommunications networks are increasingly run by interoperable agents that coordinate across network elements, services, and customer interactions.
Telemetry rules are the engineering contracts that bind data collection, privacy, and governance across AI production pipelines.
Test-driven development for prompts elevates prompts from ad-hoc inputs to governed, testable artifacts in production AI systems.
AI agents operate in dynamic production environments where data shifts and policy constraints constantly evolve. A disciplined testing and validation pipeline is essential to guarantee reliability, governance, and rapid delivery.
Testing content safety filters in production AI is not optional—it's a risk control, a governance requirement, and a performance metric all in one.
Testing data pipeline integrity explains practical architecture, governance, observability, and implementation trade-offs for reliable production systems.
ETL for unstructured data is inherently more challenging than processing structured records. In production, pipelines ingest text, logs, JSON with optional fields, multimedia, and other free-form inputs that resist rigid schemas.
Yes — you can systematically test for age and gender bias in production AI by integrating bias-aware data profiling, controlled experiments, and governance checks into your deployment pipeline.