Multi-step agentic workflows
A sequence of autonomous or semi-autonomous AI agents executing interconnected tasks to solve complex problems through iterative reasoning, tool use, and state management. Requires explicit coordination mechanisms and context preservation across steps.
Core Components
- Agent specialization: Each agent handles distinct subtasks (e.g., data retrieval, code generation, validation)
- State propagation: Shared memory or structured outputs between steps
- Error recovery: Built-in fallbacks for failed steps (e.g., re-trying, escalating to human)
Key Enablers
- GLM-4.7: Open-source model (Zhipu AI, 2026) with optimized coding/automation capabilities, supporting local deployment and complex reasoning for workflow execution
- Open-Source & Local: Downloadable weights for unrestricted deployment
- Core Coding Excellence: Full-stack coding optimization for business automation
- agentic-ai: Self-directed agents with goal-oriented task decomposition
- Business automation: Workflow integration for enterprise processes
Implementation Considerations
- Tool integration: API connectors (e.g., for databases, CRM systems)
- Cost management: Step-level resource allocation tracking
- Verification: Intermediate output validation at each step
2026 04 14 Julian Goldie SEO channel GLM 47
Source Notes
- 2026-04-23: https://www.youtube.com/watch?v=uy7F7u8A0jo # GLM-4.7: Advancing the Coding Capability & Business Automation GLM-4.7 is the latest open-source AI model from Zhipu AI (z.ai), designed specifically for real-world automation, advanced coding, and complex reasoning. It aims to comp (GLM-4.7: Advancing the Coding Capability & Business Automation)
- 2026-04-14: # Opal Labs - Sam Witteveen --- --- https://www.youtube.com/watch?v=CJyg30kowg0 Google Labs has introduced Opal, an experimental no-code AI tool designed to help users describe, create, and share AI mini-applications. This platform is part of a growing trend in AI developm (Opal Labs - Sam Witteveen)