ArticleAnna GellerAnnouncement
Workflow orchestration is moving beyond rigid DAG definitions. Engineering teams can now blend deterministic pipelines with adaptive LLM-driven tasks within a single declarative, observable, and code-managed platform.
By treating an AI Agent as a regular workflow task, Kestra seamlessly connects autonomous LLM reasoning with existing plugins, sub-flows, and Model Context Protocol (MCP) clients. Anna Geller highlights the trade-offs: non-deterministic output, higher latency, and consumption costs make agents unsuitable as a blanket replacement for core pipelines. Instead, Kestra advocates a hybrid model where predictable steps remain static and agents handle open-ended, adaptive requirements.
Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.
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