What are agentic workflows?

Agentic workflows are AI-driven processes in which agents use large language models to interpret goals, plan and execute tasks, access external tools, evaluate results, and adapt when conditions change, unlike deterministic workflows that follow fixed predefined steps. The two approaches can be combined so rule-based automation handles predictable, lower-cost tasks while agents address ambiguous work such as lead research, refund decisions, ticket triage, and access provisioning. Core elements include LLM reasoning, agents, tool integrations, feedback loops, memory, and orchestration, with systems ranging from single-purpose agents to multi-agent teams for complex work. Potential benefits include reduced manual effort, greater scalability, faster customer responses, and more informed prioritization, though governance is needed to limit permissions and protect data. The piece identifies frameworks such as LangChain, CrewAI, and Microsoft AutoGen for code-based development, while presenting Zapier as a low-code orchestration platform that connects agents to thousands of applications through AI by Zapier, MCP, SDK, and CLI tools with managed credentials and access controls. Examples from sales, marketing, customer support, and IT describe organizations using these workflows to consolidate data, automate reporting, shorten ticket research, and process natural-language provisioning requests.

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