Glossary

Agentic Workflow

An agentic workflow is a design pattern where an AI system iteratively executes tasks, utilizes tools, and makes autonomous decisions to achieve a defined objective. Unlike linear prompt-response chains, these workflows incorporate feedback loops, self-correction mechanisms, and multi-step reasoning to navigate complex processes without requiring constant human intervention at every stage.

The shift toward agentic workflows represents a transition from static, single-turn interactions to dynamic, goal-oriented systems capable of managing multi-step business processes. In enterprise environments, this approach is critical for automating complex operations that require context awareness, tool integration, and error handling. By decoupling the reasoning engine from the execution environment, organizations can deploy systems that handle ambiguity, adapt to changing inputs, and maintain state across long-running tasks, thereby increasing the reliability and scope of automated enterprise architecture.

In practice, implementing an agentic workflow requires defining clear boundaries, tool access, and termination criteria to ensure system stability. Practitioners must design robust observation loops where the agent evaluates the output of each step against the desired outcome, triggering retries or alternative strategies when necessary. Successful deployment involves rigorous governance, specifically monitoring token usage, latency, and the integrity of tool-calling sequences. Architects should prioritize modularity, ensuring that individual agent capabilities are testable and auditable within the broader system orchestration.

Last updated: 2026-08-25