Agentic Analytics: Architecture, Context, and Why the Semantic Layer Does the Heavy Lifting
Agentic analytics represents a shift in how data questions are handled by leveraging LLM-powered agents. Unlike traditional systems that require human input for each query, agentic analytics autonomously breaks down questions, retrieves context, and synthesizes answers. This approach enhances the efficiency and accuracy of data analysis by minimizing human intervention.
- ▪Agentic analytics systems autonomously break data questions into sub-tasks without human coordination.
- ▪These systems utilize a semantic layer to pull relevant context and execute queries.
- ▪The architecture of agentic analytics allows for loop-based autonomy and grounded execution, improving data reasoning capabilities.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3547741) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Jakkie Koekemoer Posted on May 18 • Originally published at jedify.com Agentic Analytics: Architecture, Context, and Why the Semantic Layer Does the Heavy Lifting #agentskills #ai #mcp An agentic analytics system is one where LLM-powered agents autonomously break a data question into sub-tasks, retrieve relevant context, execute queries, evaluate the results, and return a reasoned answer. There’s no human coordinating each step.
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