Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Anthropic's RSP v3.0 (February 2026) added a new CBRN capability threshold and disaggregated AI R&D thresholds. v3.1 followed shortly. Here is what the ASL framework asks of model providers — and what downstream voice AI vendors inherit.
The near-term trajectory of Claude-based agentic defense — multi-agent teams, autonomous purple teaming, MCP standardization — and how to prepare now.
Where multi-agent coordination on Claude is heading next — shared protocols, durable agents, managed coordination — and how to prepare your team now.
The metrics and signals that prove Claude-based defense agents reduce risk: false-negative rate, time to contain, coverage, override rate, and eval drift.
Metrics and leading signals that prove a multi-agent Claude system works — task success, cost per outcome, coordination overhead, and escalation rates.
A realistic end-to-end walkthrough of shipping a multi-agent Claude system — from problem framing to architecture, evals, and a safe production rollout.
A realistic problem-to-shipped build of a Claude phishing triage agent: scoping, tools, skills, evals, shadow rollout, and results.
Failure scenarios, blast radius, and containment patterns for Claude-based security agents — tiered authority, rollback timers, and kill switches.
Failure modes, blast-radius sizing, and concrete containment patterns for multi-agent Claude systems — scoped tools, budgets, idempotency, and tracing.
