Guides
In-depth practical guides on AI/ML engineering, agentic systems, and production deployments
Harness Engineering: The Missing Layer Between LLMs and Production Systems
Why AI systems don't fail at the model layer - and how designing the right execution harness turns brittle prompts into reliable infrastructure
Normalization and Input Defense: Hardening the Entry Point of Your LLM System
Every unreliable LLM system has a porous entry point. Here's how to build the layer that ensures the model only ever sees clean, controlled, safe input.
Context Engineering: What the Model Sees Is What the Model Does
The Lost in the Middle problem isn't a model bug. It's a context design failure - and fixing it requires treating the context window as managed infrastructure, not a dump bucket.
Gated Execution: Why Your Agent Should Never Act Without Permission
Valid output is not safe output. The Gated Execution layer is the firewall between what the model proposes and what the system actually does - and it's the difference between an agent that assists and one that causes incidents.
Validation Layer Design: Building the Reflex That Catches What the Model Gets Wrong
The model will produce malformed output. Not occasionally - regularly. The Validation Layer is the only thing standing between that malformed output and your downstream systems.
Retry, Fallback, and Circuit Breaking: Building LLM Infrastructure That Survives Outages
Your LLM provider will have an incident. The question is not whether your system fails when that happens - it's whether you designed for it beforehand.
State Management for Agentic Systems: How to Build Agents That Don't Start Over
A long-running agent without state management is a gamble. You're betting the entire task completes before something goes wrong. At production scale, that bet loses constantly.
Deterministic Constraint Systems: Building Tool Registries That Keep Agents in Scope
The model will try to use tools it doesn't have. It will call APIs with parameters that don't exist. It will invent capabilities. The constraint system is how you make the gap between what the model thinks it can do and what it can actually do exactly zero.
From Unknown Codebase to Architecture Doc, Automated - Building the LangGraph Pipeline
How ArchLens - a 12-node LangGraph pipeline - turns any Git repository into a validated architecture document: state design, chunking logic, all four validation gates, human-in-the-loop review, and production-ready error recovery
From Unknown Codebase to Architecture Document: A Complete Practitioner's Guide
A 3-pass methodology for compressing any codebase - in any language, any architecture style - into validated diagrams, debt scores, and decisions that engineering teams and stakeholders can actually act on