Boyd Stowe
Solutions Engineering at Tacnode
Boyd Stowe leads solutions engineering at Tacnode. With two decades of experience helping enterprises adopt new database paradigms, he previously worked at Couchbase and IBM. He writes about real-time data architecture, production deployment patterns, and the practical considerations of migrating from composed stacks to unified platforms.
Posts by Boyd (23)
![ACID for Agents: Why Database Consistency Is the Bottleneck for Production AI](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1 1"%3E%3C/svg%3E)
ACID for Agents: Why Database Consistency Is the Bottleneck for Production AI
AI agents and automated decision systems need ACID consistency on the data they read at decision time — not just the data they write. The industry agrees the agent data layer is broken; it disagrees on why. The converged-database camp says storage fragmentation. We argue it’s computation fragmentation: the derived state decisions depend on — velocity counters, risk scores, features — is maintained in pipelines outside the transactional boundary, so it’s always slightly stale. A Context Lake converges the computation, not just the storage, ingesting via CDC from existing systems without requiring migration.
Boyd Stowe|May 11, 2026Agent Coordination: How Multi-Agent AI Systems Work Together
Agent coordination is what determines whether multiple AI agents produce coherent results or expensive chaos. Here's how coordination strategies, communication protocols, and fault tolerance actually work — and what breaks in production.
Boyd Stowe|Mar 5, 2026Foreign Data Wrappers: S3, Iceberg & Delta Lake
Foreign data wrappers let you query Parquet on S3, Iceberg tables, and Delta Lake catalogs with standard SQL and zero data movement. Complete setup guide, plus where querying the lake in place stops working for real-time decisions.
Boyd Stowe|Feb 26, 2026Enterprise Integration Patterns in 2026: Streaming & AI
Publish-subscribe, content-based routing, CDC, event sourcing — the patterns haven’t changed, but the architectures have. The full pattern list, applied to modern streaming and AI agent systems. Updated August 2026.
Boyd Stowe|Feb 22, 2026Vector Quantization: Compress Vectors 4–32x Without Losing Accuracy
Scalar, product, and binary quantization — how each compression method works, when to use them, and practical SQL examples for cutting vector memory 4–32x while preserving search quality.
Boyd Stowe|Feb 20, 2026LLM Agents: 4 Components That Separate POC From Production
LLM agents plan, act, remember, and coordinate. Most die after the demo. This guide breaks down the 4 core components every LLM agent needs, the types you'll encounter in production, and the infrastructure gaps that kill real deployments.
Boyd Stowe|Feb 19, 2026Full-Text Search in PostgreSQL: A Complete Guide
Learn how PostgreSQL full-text search works: tsvector, tsquery, GIN indexes, relevance ranking, and fuzzy matching — with production-ready SQL examples.
Boyd Stowe|Feb 18, 2026Similarity Search: What It Is, How It Works, and Why Most Teams Implement It Wrong
Similarity search ranks by vector proximity, not exact keywords. Learn how embeddings, ANN indexes, and hybrid search work — and why bolting on a separate vector database creates an infrastructure trap most teams don't see coming.
Boyd Stowe|Feb 17, 2026What Is Data Observability? The Complete Guide [2026]
Data observability monitors data health across your pipelines. Learn what data observability means, how it differs from data quality, the pillars of data observability, and why reactive monitoring isn't enough.
Boyd Stowe|Feb 14, 2026Multi-Agent Architecture: 8 Coordination Patterns That Actually Work [2026]
When AI agents conflict, you get duplicate orders, race conditions, and angry customers. Here are 8 production-tested coordination patterns — from simple locks to distributed consensus — with code examples for each.
Boyd Stowe|Jan 28, 2026Feature Freshness Explained: Why Model Accuracy Drops in Production
Your model scored 94% in training. In production it's drifting toward 80%. The features you trained on don't match what the model sees at inference. Here's how to measure feature freshness, detect drift, and close the gap.
Boyd Stowe|Jan 15, 2026Primary Key Design: Best Practices for Performance and Scale
Your analytical queries are slow—and it's probably not indexing. I've seen query latency double without any code changes, just from a bad primary key choice. Here are the 3 patterns that actually work at scale.
Boyd Stowe|Dec 29, 2025Code Like a Mammal
Evolve to stay a step ahead.
Boyd Stowe|Oct 15, 2025Context Lake in Practice: Detecting Fraud with Live-context LLMs
Securing systems where milliseconds mean millions.
Boyd Stowe|Sep 9, 2025Stateful vs Stateless AI Agents: A Practical Comparison
Stateful agents retain context across requests. Stateless agents scale but forget. This guide covers the 5 failure modes teams hit in production, when to use each pattern, and the hybrid architectures that actually work at scale.
Boyd Stowe|Jan 6, 2026CQRS for AI Agents: Why Eventual Consistency Breaks Autonomous Systems
CQRS separates reads from writes. But when AI agents become the read-side consumer, eventual consistency becomes a correctness problem. Here's what changes.
Boyd Stowe|Mar 20, 2026Snowflake Dynamic Tables vs Materialized Views (2026)
Refresh mechanics, target lag, costs, and limitations — when to use each, and where both fall short for real-time decisioning workloads. Updated August 2026.
Boyd Stowe|Apr 20, 2026Postgres MCP Server: Setup, Tools, and Safe Query Access
A practical guide to running a Postgres MCP server — which implementation to pick, how to wire it into Claude Desktop, Cursor, and VS Code with Docker, what tools it exposes, and how to give an agent query access without handing it your database.
Boyd Stowe|Sep 15, 2026Agentic Commerce: How Shopping Agents Actually Buy
Agentic commerce fails at the gates, not the recommendations. Walk the six steps a shopping agent executes — discovery, cart, price, promotion, inventory, spend authorization — and see which ones break when concurrent agents read four systems at four different moments.
Boyd Stowe|Sep 11, 2026View vs Materialized View: Differences, Performance, and When to Use Each
View vs materialized view, in one line: a view stores a query and runs it on every read. A materialized view stores the result and serves it until you refresh it. Here is how they differ on storage, read cost, refresh cost and staleness, with PostgreSQL examples, how Oracle, SQL Server, Snowflake, BigQuery, Redshift and Databricks handle it, and a decision guide.
Boyd Stowe|Sep 18, 2026What Is a Materialized View? How It Works Across Postgres, Oracle, SQL Server, and Snowflake
A materialized view stores a query’s result so reads skip the expensive work. Here is how materialized views work, the five refresh strategies, and exactly how PostgreSQL, Oracle, SQL Server, MySQL, Snowflake, BigQuery, Amazon Redshift, Databricks, ClickHouse, and streaming systems implement them.
Boyd Stowe|Sep 21, 2026pg_cron: Scheduling Postgres Jobs (and Refreshing Materialized Views)
How to install pg_cron on self-hosted Postgres, Amazon RDS, Cloud SQL, Azure and Supabase, write schedules, read job history, and run the job most teams install it for: refreshing a materialized view on a schedule without blocking readers.
Boyd Stowe|Sep 22, 2026MySQL Materialized Views: How to Emulate Them (and When to Stop)
MySQL Community and Enterprise Server have no CREATE MATERIALIZED VIEW. Here are four ways to build a MySQL materialized view anyway (scheduled rebuilds, trigger-maintained summary tables, incremental refresh from a changelog, and CDC into a system that maintains views for you), with runnable SQL and a table for picking the right one.
Boyd Stowe|Sep 24, 2026