Articles

Build logs, concept explorations, and technical writing. Entity memory, governed AI, emerging patterns from the infrastructure layer.

Featured10 min read

Moving Governance and Evaluation Below the Application Layer

Governance lives in system prompts. Evaluation lives in separate pipelines. State lives in session stores. We moved all three into the infrastructure layer of an AI API. Here is the architecture and what it changes.

AI AgentsArchitectureAI EngineeringAPI DesignPersonize
Featured15 min read

We Replaced messages[] With steps[] in Our Agent API. Here's Why.

We started with the same messages[] pattern everyone uses. For complex, repeatable agent workflows it kept failing in predictable ways. So we decomposed instructions into sequential steps with scoped tools and shared context. Here's what we learned.

AI AgentsArchitectureAI EngineeringLLMPersonize
Featured11 min read

Code-Orchestrated Agents vs. Tool-Calling: The Architecture Decision That Matters Most

Stripe, Shopify, and Salesforce all converged on the same pattern: LLM decides, code executes. Here's the architectural reasoning, the trade-offs, and when tool-calling actually makes sense.

AI AgentsArchitectureAI EngineeringDesign Patterns
Featured16 min read

The Multi-Entity Memory Pattern

Most AI systems memorize contacts. The ones that work memorize contacts, their companies, their deals, and the relationships between all of them — then recall across entity boundaries at inference time.

Entity MemoryAI ArchitectureDesign PatternsPersonizeAgent Memory
Featured8 min read

Encoding Solution Architecture Into an AI Skill

The early stages of AI implementation are mostly discovery — assembling scattered context into a coherent system design. We spent two years encoding that process. Here's what we found.

Build LogSkillsSolution ArchitectureEntity Memory