Inside Claude Code Dynamic Workflows: The Architecture
How dynamic workflows in Claude Code work end to end: the agent loop, context assembly, skills, MCP tools, hooks, and subagents explained for engineers.
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
How dynamic workflows in Claude Code work end to end: the agent loop, context assembly, skills, MCP tools, hooks, and subagents explained for engineers.
The next wave of Claude agent orchestration: long-running agents, richer MCP ecosystems, and self-improving loops — plus how to prepare now.
Autonomous reviewers, continuous agentic auditing, and self-healing pipelines — where Claude-driven source-code security is going, and how to prepare now.
The next phase of zero trust for Claude agents: native agent identity, runtime policy engines, attestation, and the moves teams should make now to prepare.
Quality, cost, reliability, and trust metrics that prove a Claude agent orchestration system works — plus the leading signals that warn you early.
The metrics, signals, and eval loops that prove a Claude-driven source-code security program is genuinely working — precision, remediation, and trust.
The metrics that prove zero trust for Claude agents works: injection block rate, credential TTL, least-privilege coverage, and audit coverage explained.
A realistic Claude agent orchestration build from messy problem to shipped outcome: decomposition, MCP tools, evals, and staged rollout.
A realistic end-to-end walkthrough of using Claude to find, triage, and fix a real source-code vulnerability — from messy problem to shipped, verified fix.