Agentic-Genie++

Agentic Genie++ is a data-agnostic, multi-agent analytics application that transforms Databricks Genie Spaces into an intelligent, conversational decision-support environment.

The app extends native Genie capabilities by introducing AI-driven analytics, PDF understanding, and agentic orchestration across descriptive, predictive, and prescriptive layers. It adapts dynamically to any dataset or business context—without requiring predefined schemas or domain assumptions.

**Key Highlights**

- 10 + AI Functions
- 100 % PDF Analysis
- ∞ Data Agnostic
- Requires only a Genie Space ID and a LLM Model Serving Endpoint

**Use cases**

- Conversational Analytics
Ask questions about any structured, semi-structured, or unstructured data and receive instant summaries, visualizations, and recommendations.

- Automated Insight Generation
Combine descriptive metrics, predictive modeling, and prescriptive actions—delivered through autonomous analytic agents.

- Document Intelligence
Upload and query PDFs, text files, or reports to extract relevant information, detect patterns, and summarize findings.

- Forecasting & Next-Best Action
Generate forecasts, classify trends, and produce intelligent “next-step” suggestions tailored to your context.

- Universal Data Exploration
Works seamlessly across domains such as finance, manufacturing, operations, customer analytics, R&D, or compliance—without retraining or data-model reconfiguration.

**Product details**

The app is data-agnostic and compatible with any dataset accessible in your Genie Space. Example generic datasets include:

- sample_transactions – transactional or time-series data
- entity_metrics – KPIs or aggregated indicators
- text_corpus – documents, reports, or communications
- forecast_inputs – historical records for prediction tasks
- classification_data – labeled data for supervised learning
- embedding_index – vectorized data for retrieval or semantic search
- analytics_results – generated insights and visual outputs
- system_logs – operational telemetry for monitoring
- user_feedback – survey or review data for sentiment analysis
- workflow_history – event records for automation tracing

**Sample fields include:**

record_id, timestamp, category, value, metric_name, prediction, recommendation, embedding_vector, summary_text, confidence_score.

**Additional Insights**

- Built entirely on Databricks Apps Framework using FastAPI + LangGraph for orchestration.
- Operates securely inside the workspace; no external data transfer required.
- Implements a three-tier analytics model:
- Descriptive Analytics: historical analysis and trend identification
- Predictive Analytics: forecasting and anomaly detection
- Prescriptive Analytics: optimization, recommendations, and next-best actions
- Enables real-time, scalable, and explainable analytics for any industry or data type.
- Facilitates evidence-based decisions, operational excellence, and intelligent automation without the need for domain-specific configuration.