Guides
In-depth practical guides on AI/ML engineering, agentic systems, and production deployments
Context Engineering: The Skill That Separates Production Agents from Demos
Prompt engineering tells the model what to do. Context engineering determines whether it can actually do it.
Agent Skills Are Not Prompts. They Are Production Knowledge Infrastructure.
Every team is re-teaching their agent the same workflows on every call. Skills are how you stop paying that tax.
Subagents: How to Run Parallelism Inside a Single Agent Session Without Poisoning the Parent
Every subagent burns its own context so the parent doesn't have to. That's the entire architecture.
Hooks: The Enforcement Layer That Turns Agent Policy Into Agent Fact
Prompts suggest. Hooks enforce. Until you know the difference, your agent's safety guarantees are probabilistic.
Which Claude Code Layer Solves Your Problem? A Diagnostic Guide for AI Engineers
Reaching for a subagent when you needed a skill is the most common mistake teams make. Here is how to stop making it.
Four Habits from the Creator of Claude Code That Will Change How You Ship
Boris Cherny runs 10-15 parallel sessions, ships 20-30 PRs a day, and calls his setup 'surprisingly vanilla.' The gap is not configuration. It is operating model.
You Can't Debug What You Can't See: Observability for Claude Code Sessions
Most Claude Code failures leave no trace. Here is how to build the audit trail that tells you exactly what happened, why it went wrong, and how to stop it happening again.
How to Know Your Claude Code Setup Actually Works: Testing Beyond the Skill Level
Skill evals tell you a skill works in isolation. They do not tell you whether your agent produces consistently good code. That requires a different kind of test.
Unified Observability Across Agent Fleets: Building the Control Plane Metric Layer
Teams running agent fleets think they have observability because they have traces. They don't - they have logging. Here's what the difference costs you in production.
Global Policy Enforcement vs. Per-Agent Gate Rules: Two Layers That Must Not Collapse Into One
Treating fleet-wide policy and per-agent gate logic as the same problem is how you end up with governance theater and brittle agents at the same time.
Multi-Agent Pipeline Orchestration and Failure Propagation: Designing for Blast Radius
Retry logic tells an agent what to do when it fails. A pipeline halt protocol tells the entire fleet what to do. Most production systems only have one of these.
Agent Versioning and Deployment Strategies: Shipping Agent Updates Without Breaking Running Pipelines
Deploying a new agent version into a live multi-agent pipeline is not a software deployment. It is a distributed state migration - and most teams treat it like the former.
Cost Governance and Budget Allocation Across Agent Types: Token Spend Is Infrastructure Spend
Most teams discover their agent fleet's true cost on the invoice. By then, three budget cycles of misconfigured pipelines have already run.
Compliance, Audit Trails, and Regulatory Requirements for Agentic Systems
The EU AI Act full enforcement begins August 2, 2026. The gap between running agents and running auditable agents is not a documentation problem. It is an architectural one.
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