The most powerful agentic systems aren't the ones that respond in 2 seconds -- they're the ones that work for 2 hours while you sleep, checkpoint their progress, and have results waiting when you wake up.

Claude Code is an autonomous AI coding agent available across terminal, IDE, and desktop -- it reads your entire codebase, makes multi-file edits, executes commands, and integrates with git autonomously.

A high-throughput, memory-efficient LLM serving engine that uses PagedAttention to achieve 2-4x higher throughput than naive implementations, with an OpenAI-compatible API server for drop-in replacement of commercial LLM endpoints.

The difference between deployed models and production ML systems: Continuous measurement of model and data health, drift detection, automated alerts, and feedback loops to trigger retraining before users are impacted.

Anthropic's safety-first approach to AI has produced a family of reasoning-optimized models with 1M token context as standard and constitutional AI reducing prompt injection success rates to ~4.7% vs. industry average 15%.

The model is a CPU. Without an operating system -- the harness -- it's powerful but inert. Reliability is the real work. Prompting is the easiest part.

Safely shipping models to production: Packaging, staged rollout (shadow mode, canary, A/B test), infrastructure, and strategies to minimize risk and measure impact.

Google Cloud's next-generation conversational agent platform -- the enterprise successor to Dialogflow CX, built on ADK and Gemini, capable of deploying personalized voice and digital agents in days instead of months.

Systematic experimentation and iteration: Train candidate algorithms, tune hyperparameters, validate robustly, analyze failures, compare, and select the best model for production.

How you wire agents together determines whether your system is a reliable product or a demo that falls apart under real load -- the orchestration pattern is the most consequential architectural decision in any agentic system.