You cannot mathematically prove an LLM correct. But you can architect around it. Part 2 surveys the three research communities approaching semantic correctness for LLM outputs — formal verification, runtime guardrails, and post-condition verification — maps their results honestly, and delivers a concrete engineering playbook: four patterns engineers can implement today to build agentic systems that fail visibly, recover gracefully, and escalate correctly.
Every major agentic AI pattern has a named distributed systems ancestor going back decades. MoA is ensemble computing. ReAct is a control loop. AutoGen is the actor model. LangGraph is a workflow engine. Multi-agent coordination is a blackboard system. This article maps each AI concept to its CS lineage, explains what that means for engineers building today, and names the three problems that genuinely have no distributed systems precedent — the real frontier where new thinking is required.
The AI industry uses "model," "agent," and "orchestrator" interchangeably — but the distinctions matter. This article walks through the hierarchy from language models to full orchestration layers, explores why multi-model architectures are gaining traction, and looks at the "write skills, not agents" pattern emerging from Claude Code's Boris Cherny. Covers three practical principles of context engineering — reference-based skills, right-sized agents, and conditional context loading — and why giving agents a way to verify their own work may be the single highest-impact improvement.
The intersection of AI agents and blockchain is one of the most misunderstood areas in modern software architecture. This article examines when blockchain genuinely solves problems in multi-agent systems—identity, trust across boundaries, and Byzantine fault tolerance—and when it's expensive theater. Includes practical analysis of MCP, transport security, and the real decision framework for choosing between traditional infrastructure and distributed ledgers.
