Agentic Workflows Explained: When to Use AI Agents vs Linear Pipelines

Agentic workflows give AI agents a goal while allowing them to determine the runtime path by planning subtasks, selecting tools, evaluating results, and iterating until completion or a safety stop, unlike deterministic workflows that follow predefined rules and non-agentic AI pipelines that place models within otherwise fixed sequences. They are most suitable for open-ended tasks such as research, competitor monitoring, code generation, and unstructured data analysis, whereas predictable, high-stakes processes such as payments, payroll, compliance reporting, and alarms generally benefit from deterministic control. Production agentic systems require orchestration, planning, tool access, memory, state management, verification, escalation paths, observability, and cost and latency controls to prevent unreliable or expensive behavior. Multi-agent designs can assign specialized roles such as extraction, validation, analysis, and reporting under an orchestrator, while hybrid architectures retain deterministic rules for permissions, approvals, state changes, and system-of-record updates. The material also presents Firecrawl as a web-data service that provides search, scraping, crawling, browser interaction, and research capabilities through SDKs and a free keyless Model Context Protocol server, enabling agents to access current web information without building custom scraping infrastructure.

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