A technical deep dive on implementing AI guardrails at the gateway layer using Bifrost.
The article explores policy-driven guardrails, CEL Rules & Profiles, input/output validation, streaming protection, and multi-provider safety integrations for production AI systems.
Sharing this Medium publication for anyone interested in dbt and analytics engineering
Built a gateway that reduces LLM costs using semantic caching, MCP-aware routing, code mode detection, and intelligent model selection.
A lot of teams jump straight into tables, schemas, and database implementation when discussing data modeling. This article explains why that's a mistake and breaks data modeling into its three distinct layers:
It also walks through an end-to-end modeling process and discusses the key decisions made at each stage.
A practical deep dive into building and deploying Model Context Protocol (MCP) servers on Google Cloud. Covers architecture patterns, context-aware AI design, security considerations, and how MCP enables standardized integrations between AI agents, tools, and enterprise systems. MCP is becoming a key building block for scalable AI-native architectures.
A deep dive into why traditional Data Vault hash keys don’t align well with BigQuery’s clustering and pruning mechanisms. The article explores how introducing physical locality through structured surrogate keys, dates, and bucketing can significantly improve query performance and reduce scan costs. Based on practical BigQuery architecture considerations.
An exploration of how to design a modern financial data lakehouse using Spark, declarative pipelines, and Apache ecosystem tools. A practical approach to improving scalability, maintainability, and efficiency in large-scale data processing workflows.
How the Model Context Protocol transforms data platforms into agent-native systems capable of autonomous discovery, governance, orchestration, and execution across the modern data stack.
How to design scalable, declarative, and production-grade orchestration systems using Dynamic DAGs, YAML contracts and metadata-driven workflows.
A deep dive into automating attribute-based access control (ABAC) in BigQuery using IAM resource tags. Really interesting approach to making data governance more scalable and fine-grained in modern data platforms.
A deep dive into how schema evolution works in Apache Iceberg and why it’s so powerful for Kafka-based data platforms. Worth a read if you work with streaming data or lakehouse architectures.
I just published a new article where I explore how to better control BigQuery jobs when using dbt, focusing on:
If you're working with dbt + BigQuery in production, this might help you avoid runaway costs and better structure workloads.
I recently read this article on guardrails in LLM agents and it made me rethink how we’re building production AI systems.
The core idea is that guardrails are not just “safety filters”, but actual system architecture:
What stood out to me is the framing that as models get more capable, guardrails become *more important (*not less) because capability increases impact of failure.
This Agent Skills Framework idea is really interesting. The concept of a middle layer for modular, reusable agent capabilities feels like a step toward more structured and scalable AI systems rather than prompt-heavy setups.
I just read an interesting article about using Apache Spark not only to transform data else also to enforce data contracts within pipelines.
The key idea: the problem isn't that jobs fail, but that they don't fail when they should. The pipelines keep running, but the data might be corrupted → silent errors.
The proposal:
This transforms pipelines into systems that guarantee quality, not just move data.
If you don't validate your data within the pipeline, you're relying on assumptions.