AI Agents for Regulated Industries: DAGs, State Machines & Plan-and-Execute Explained скачать в хорошем качестве

AI Agents for Regulated Industries: DAGs, State Machines & Plan-and-Execute Explained

Your AI agent works great in a demo. Then it hits production — and nobody can explain why it took 47 steps to do a 5-step job. The problem isn't intelligence. Its structure. Part 3 of my Agentic AI Architectures series is live: Structure & Control — Workflow-Based Architectures. Free-form agent loops are powerful but unpredictable. Same task, different execution path every time. For regulated industries, for enterprise SLAs, for anything where auditability matters — that's a non-starter. Workflow-based architectures fix this. They sit in the sweet spot between full autonomy and rigid determinism. LLM intelligence at the nodes. Deterministic control between them. Three patterns covered in this episode: DAG / Pipeline — A directed acyclic graph where each node is an LLM call, a function, or a conditional branch. The killer feature? Nodes without dependencies run in parallel. Three analysis steps that take 5 minutes each? Sequential = 15 minutes. Parallel DAG = 5 minutes. AWS Step Functions + Bedrock is the production-grade way to build this on AWS. State Machine — The agent can only be in one defined state at a time. Every transition is explicit. Every path is predetermined. You can answer "what state was the agent in and how did it get there?" at any point. For healthcare, financial services, government — where compliance teams need an audit trail — this is your architecture. Plan-and-Execute — The agent creates a full plan before taking a single action. Then executes step by step. If something unexpected happens, it re-plans with new context. This is fundamentally different from ReAct, which figures out the next step one at a time. Plan-and-Execute sees the whole journey first. That global coherence is why it dominates for complex, multi-step tasks like end-to-end code migrations. Here's how I think about choosing between them: Need parallel execution? → DAG Pipeline. Need regulatory compliance? → State Machine. Need adaptive multi-step reasoning? → Plan-and-Execute. The real unlock is that these compose with everything from Parts 1 and 2. A DAG pipeline where one node is an orchestrator-worker system. A state machine where each state runs a ReAct agent internally. Structure on the outside, intelligence on the inside. Missed the earlier episodes? Part 1 → Single-Agent Architectures (ReAct, Code-Act, Tool-Use Loop) Part 2 → Multi-Agent Architectures (Orchestrator-Worker, Hierarchical, Debate, Swarm) Links in the comments. Coming next: Part 4 → Reflection, Human-in-the-Loop & Agentic RAG Part 5 → Frameworks, Production & Decision Guide Question: Are you running any workflow-based agent patterns in production today? What's working and what's painful? Tell me in the comments. #agenticai #aiarchitecture #cloudmigration #dataengineering #genai #aws #llms #aiagents #enterpriseai