The Machine Learning Practitioner's Guide to Agentic AI Systems - MachineLearningMastery.com
agentic-aiml-practitionersagent-frameworksproduction-systems
Abstraction: Roadmap for ML practitioners transitioning to production agentic systems
Key points:
- Three core architectural patterns: ReAct (reasoning+acting loop), Plan-and-Execute (cheaper for complex workflows), and Reflexion (self-improvement via linguistic feedback)
- Framework landscape in 2025: LangGraph for production/stateful workflows, CrewAI for rapid multi-agent prototyping, AutoGen for conversational enterprise patterns (March 2025 unified SDK)
- Agentic AI market projected to grow from $5–7B (2025) to $50–200B by 2030–2034
- Memory systems best practice: hybrid vector search + knowledge graphs + decay strategies; LangGraph LangMem and Redis Agent Memory Server are production-proven
- Model Context Protocol (MCP), broadly adopted in 2025, is becoming standard for agent-tool-data connectivity
- Emerging roles: AI Engineer, Agent Orchestrator, AI Architect — practitioners who can design and ship agents are in high demand
Connections: Langgraph · Crewai · Autogen · AI Agents · Retrieval Augmented Generation · Model Context Protocol
Source: https://machinelearningmastery.com/the-machine-learning-practitioners-guide-to-agentic-ai-systems/