Introduction to Chicory
Where Data teams build reliable and accurate AI Agents
Chicory gives data teams the ability to build, deploy, and manage AI agents. Agents built using Chicory are powered by a unique combination of data tools and context, intelligently mapping your code, documents and data. This gives Chicory agents the ability to understand and work on your unique data landscape.
Key Features
Agent Development Life Cycle (ADLC): Complete framework for Build → Evaluate → Evolve → Deploy → Monitor workflows.
Data Understanding Module: Auto-builds and syncs structured and unstructured data into a unified data model.
AI Agent Framework: End-to-end environment for building, testing, deploying, and versioning data-focused agents.
Multi-Agent Orchestration: MCP Gateways enable complex agent workflows with multiple specialized agents working together.
Chicory Agentic Flow: Orchestrates multi-step analyses and automations (e.g., EDA, pipeline tuning).
Automated Runbook Execution: Define and run complex data processes via structured runbooks (YAML/JSON/Markdown).
Intelligent Data Connectors: Ingest metadata and data from 100s of sources including Databricks, GitHub, Google Drive, and data tools.
No Ops Overhead: We handle training, hosting, and scaling—so you focus on insights.
Core Use Cases
Data Understanding & Governance: Ask “Where did this metric come from?” or “What does this column mean?” and get instant, auto-generated lineage and documentation.
Data Debugging Agent: Trigger runbooks on pipeline errors, gather logs, pinpoint failures, and suggest fixes in seconds.
Pipeline Optimization: Analyze your full DAG to surface cost- and latency-hotspots, then recommend configuration tweaks that free up budget.
Business Intelligence: Trace any KPI shift through data, code and dashboards—getting you root-cause answers in minutes.
Feature Engineering Agent: Scan your data estate to discover, score and prioritize the highest-impact variables for every ML model.
Data Harmonization: Automatically align schemas, formats and business definitions at ingest—so datasets are join-ready in hours, not days.
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