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DataGuardian AI is an autonomous data-governance agent that uses DataHub context to detect risks, reason over impact, recommend fixes, and write approved remediation back to the data graph.
An autonomous data steward for DataHub. It investigates broken data, retires dead tables, and writes every finding back into the catalogue; so the next person inherits instead of repeating the work.
Production ML Agent built on DataHub's end-to-end graph lineage to protect ML models from silent upstream pipeline data mutations
It is an AI data-change intelligence agent that uses DataHub context and lineage to predict a change’s blast radius, identify what could break, and generate a safe rollout plan before production.
A two-phase offboarding agent for data engineers
AI cleanup agent that scans your DataHub context graph, flags orphaned, undocumented, and broken-lineage datasets, then writes LLM-generated fixes back to the graph.
GCIA turns DataHub metadata changes into human-gated, evidence-backed actions by mapping downstream impact, revalidating risk, and proving outcomes without blind retries.
Autonomous data incident response that investigates, explains, and learns.
Evidence-grounded data investigation powered by DataHub metadata, lineage, ownership, and impact analysis.
Know your PR's blast radius before you merge. Faultline asks DataHub which downstream assets actually read each changed column, then writes the migrations for what breaks.
Turn DataHub metadata into a safety layer for AI-driven data changes. LineageGuard detects hidden impact, blocks unsafe changes, generates a safe migration, validates it, and opens a GitHub PR
An agent that watches the world's data feeds and news for emerging risks, ranks them by world impact, and uses DataHub's graph to find the hidden connection no single feed could.
Akashic Weaver watches DataHub for breaking schema changes, traces every downstream dependency, and ships the fix as a pull request — before your team even knows something broke.
DataHub gives agents context. Gatehouse makes context decide what they may do: an MCP gateway that checks every agent tool call against live graph facts before it runs.
Designed an AI agent to search the web using a given query by requesting the Tavily API to web search and take the response and formatting it using the Gemini model which gives a structured answer
The courthouse for AI agents on DataHub: register identity, gate MCP writes, cross-examine catalog vs SQL, propose changes, write verdicts to GMS so the next agent inherits.
Aura AI: A voice-activated student super app featuring 'aura ai' for job matching, budgeting, learning, and fitness."
An agent that follows schema changes through DataHub lineage and stops risky write-backs before they break downstream data.
Most security alerts about your data pipelines don't matter. This tells you which five out of a thousand do
AI agent that reads DataHub's lineage graph to predict which PRs will break downstream dashboards and pipelines.
Discovers your AI agent fleet, detects poisoned MCP tool descriptions, and governs it all in DataHub.
LATCH turns an external AI-training dataset transfer into a governed release: DataHub context, BigQuery proof, human approval, safer output, and a verifiable passport.
Autonomous Metadata Context & Governance Engine for Production AI Agents, integrated with DataHub
Find every consumer of contaminated data, contain the blast radius, execute a real recovery, and prove what recovered.
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