Technical Focus
Agentic AI infrastructure, security architecture, and production migration methodology.
35 years of production software experience applied to the hardest problems in modern AI development.
Agentic AI & Claude Code
Building production-ready multi-agent systems: specialized agents, deterministic control via hooks, 140x token-efficient skills, and team architectures that scale beyond single-context limits.
Claude Code Series
View all →Multi-Agent Frameworks
Combining the Power of LangGraph with Pydantic AI Agents 2025 → AI Agent Blueprints: Implementing Anthropic's Framework with Pydantic AI 2025 → Building Domain-Specific AI Agents with LangGraph and Pydantic AI 2025 → Advanced Testing Strategies for LangGraph and Pydantic AI Agent Systems 2025 → Scaling LangGraph and Pydantic AI Systems: From Prototype to Production 2025 →AI Security Architecture
Defense-in-depth security for AI agent systems: OWASP Top 10 for Agentic Applications coverage, multi-tier trajectory monitoring, input sanitization patterns, and per-archetype security configurations across 7 project types.
Production AI Systems
Three completed AI systems proving end-to-end methodology: natural language to SQL dashboards, knowledge graph pipelines for 1,000+ research notes, and autonomous job market intelligence across 1,975 companies.
Deep-dive article series in progress for each project.
Data Intelligence & GraphRAG
From vector embeddings to knowledge graphs to GraphRAG: building the retrieval infrastructure that grounds AI in real-world knowledge. Includes a 4-part production optimization series with benchmarks.
Foundations
Vector Databases: The Engine Powering Modern AI Applications 2025 → RAG: Grounding AI with Real-World Knowledge 2025 → Graph Databases: The Foundation Enabling Context-Aware AI Applications 2025 → GraphRAG: Enhancing Retrieval with Knowledge Graph Intelligence 2025 →GraphRAG Deep Dive (4-Part Series)
GraphRAG: Building Bridges in the Knowledge Landscape - 1 of 4 2025 → Optimizing GraphRAG: Five Essential Techniques for Production Performance - 2 of 4 2025 → Optimizing Parallel Relationship Loading in Graph Databases: The Mix and Batch Technique - 3 of 4 2025 → Benchmarking and Optimizing GraphRAG Systems: Performance Insights from Production - 4 of 4 2025 →Technical Proficiency
Research Directions
Deterministic Control via Hook Engineering
A CLAUDE.md instruction achieves ~90% compliance. A hook achieves 100%. Per-agent hook embedding scales better than global hooks — and every gap found across 3 migrations occurred in an area without hook enforcement.
Progressive Disclosure Skills Architecture
Three-tier skill loading makes domain knowledge accessible without the token cost of loading everything simultaneously. Current exploration: how skill content should evolve as projects mature, when to split vs. merge, and how stale content gets detected.
Agent Prompt Injection Defense
XML boundary delimiters wrap external input so agents distinguish instructions from user content. A PostToolUse hook scans every file read for 22 injection patterns across 10 categories — role-play injection, instruction override, base64 payloads, hidden HTML comments — flagging suspicious content before it reaches agent reasoning.
Agent Trajectory Monitoring
Most Claude Code projects monitor individual tool calls — nothing monitors the pattern over time. A 3-tier system: heartbeat checkpoint (every 25 calls, 5 anomaly patterns), orchestrator watchdog timer, and optional Haiku-based trajectory analysis.
Agentic AI Security
Defense-in-depth for AI agent systems: OWASP Top 10 for Agentic Applications coverage, multi-tier trajectory monitoring, per-archetype security configurations across 7 project types, and rate limiting as circuit breakers. Every agent must prove who it is, justify what it wants, and earn trust continuously.
Building Agentic AI Business Solutions
Taking real-world workflows and building advanced agentic AI solutions: natural language to SQL dashboards, knowledge graph pipelines for research notes, autonomous job market intelligence. Each project proves the methodology end-to-end, from messy data to production system.