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Agentic Workflows: A Technical Deep Dive
Understanding how Kumari AI handles complex multi-step tasks
In this post, we explore the architecture behind Kumari AI's agentic workflows and how they differ from traditional linear AI responses.
Why Agentic?
Unlike standard LLM interactions where a single prompt leads to a single response, agentic workflows allow the AI to:
- Reason: Plan out steps before executing.
- Tool Use: Interact with search, databases, or APIs.
- Self-Correct: Review its own work and fix errors.
Implementation Details
We use a graph-based orchestration layer that manages state across multiple agent turns, ensuring consistency and accuracy in long-running tasks.