The Agentic Framework

The Intelligent Data Lifecycle

A continuous loop of planning, engineering, and operations—managed by autonomous agents that never sleep.

Analytics Lifecycle

11 agents. One connected workflow.

Every stage of your data journey — automated end-to-end by intelligent agents that never sleep.

01 · Requirements
Requirements Gathering
Capture KPIs, stakeholder needs, and success criteria before a single byte is moved.
02 · Architecture
Data Architecture & Design
Medallion layers, platform selection, storage design, and compute sizing — auto-generated blueprints.
03 · Platform Setup
Platform Setup & Configuration
Provision and configure cloud data platforms — Fabric, Databricks, Snowflake — in minutes.
04 · Discovery
Data Assessment & Discovery
Automated profiling and classification of all source systems, schemas, and data assets.
Phase 1: Plan & Design
Foundational intelligence. Agents understand business context before a single byte is moved.
05 · Data Modeling
Data Modeling & Engineering
Physical schemas, entity relationships, and Medallion-layer tables generated from business requirements.
Phase 2: Engineer
Heavy lifting automated. Code generation for ingestion, modeling, and tests.
06 · ML Modeling
ML & Predictive Modeling
Predictive and prescriptive models built, trained, and deployed by the ML agent autonomously.
Phase 3: Advanced ML
Create custom models for custom business needs.
07 · Semantic Model
Semantic Model & DAX Layer
Power BI semantic models, DAX measures, and hierarchies generated and deployed automatically.
08 · Reporting
Analytics & Reporting
Auto-formatted reports, dashboards, and insights delivered to the right stakeholders on schedule.
09 · Testing
Testing & Validation
Data quality rules generated and enforced. Automated validation gates catch issues before they reach production.
Phase 4: Build
Data-to-insight lifecycle becomes fully agent-driven.
10 · Operations
Managed Operations
SRE agents monitoring performance 24/7. Self-healing pipelines ensure 99.9% uptime with zero manual intervention.
11 · Governance
Data Governance
Policies, lineage tracking, and compliance enforced automatically. Full audit trail from source to dashboard.
Phase 5: Operations & Compliance
Introduce automated trust, validation, and compliance controls.

Agent Capabilities Matrix

Which agent does what?

01 · Plan & Design

Requirements

  • • Stakeholder interview
  • • KPI definition
  • • Success criteria mapping
02 · Plan & Design

Architecture

  • • Platform selection
  • • Medallion layer design
  • • Compute sizing
03 · Plan & Design

Platform Setup

  • • Fabric / Databricks / Snowflake
  • • Workspace provisioning
  • • Environment configuration
04 · Plan & Design

Data Discovery

  • • Source system profiling
  • • Schema auto-classification
  • • Data catalog generation
05 · Engineer

Data Ingestion

  • • Batch & streaming pipelines
  • • Schema drift handling
  • • Data connector config
06 · Engineer

Data Modeling

  • • Physical schema generation
  • • Entity relationship mapping
  • • Medallion layer tables
07 · Advanced ML

ML & Predictive

  • • Predictive model training
  • • Auto feature engineering
  • • Model deployment
08 · Build

Semantic Model

  • • Power BI semantic layers
  • • DAX measure generation
  • • Hierarchy & relationship setup
09 · Build

Reporting

  • • Auto-formatted dashboards
  • • Scheduled report delivery
  • • Stakeholder insight packages
10 · Build

Testing & Validation

  • • Data quality rule generation
  • • Automated validation gates
  • • Pre-production checks
11 · Operations

Managed Operations

  • • 24/7 pipeline monitoring
  • • Self-healing pipelines
  • • Incident auto-remediation
12 · Compliance

Data Governance

  • • PII detection & masking
  • • GDPR / HIPAA compliance
  • • Full audit trail & lineage

Start Your Lifecycle Transformation

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