Agentic Workflows

Agentic workflows are AI-driven processes where a language model operates as an autonomous agent, breaking a high-level goal into a sequence of steps, executing each step using available tools, evaluating the results, and adjusting its plan as needed. Unlike single-turn prompting where the model generates one response, agentic workflows involve loops of reasoning, action, and observation that can run for minutes or hours.

A typical agentic workflow follows a pattern: the model receives a goal, reasons about what information or actions are needed, calls an external tool (a code interpreter, web browser, API, or file system), observes the result, decides whether the goal has been met or whether further steps are needed, and continues until done. The model maintains state across steps, tracking what it has tried, what worked, and what remains.

The reliability of agentic workflows depends heavily on the model's ability to call tools correctly, recover from errors, and know when to stop. Early implementations were fragile, requiring careful prompt engineering to keep the model on track. Newer architectures like Qwen 3.5 and GLM-5 build tool use and multi-step planning into the model's training rather than relying on prompting alone, significantly improving reliability. The shift from prompt-driven to architecture-driven agentic behavior is one of the defining trends in AI development heading into 2026.