01
Goal-Oriented Agents
Pre-built and custom agents for pipeline authoring, anomaly triage, root-cause analysis, and natural-language analytics — every step traceable.
Solutions · Agentic AIGENEDATA / 01
From an objectiveto accountable action.
Give agents the context and tools to work across your data—with clear boundaries, human approvals, and a traceable execution history.
Shared context · Lineage · Governance
Connected
Your sources
Business objectives
Knowledge & tools
Permissions
Connected intelligenceAgent workflow
Governance
Business impactReviewed outcomes
Shared contextLineageGovernance
02
Governed by Default
Agents inherit IAM, data-classification, and policy guardrails from the platform — no shadow access, no PII leakage, no jailbreaks.
03
Multi-Model Routing
Cost-aware routing across OpenAI, Anthropic, open-source, and on-prem models with per-call telemetry and budget controls.
Built On
The products behind this solution.
Agentic workflows are a governance problem before they are a modelling problem, which is why all four products are involved.
Cortex AI
The agent runtime, model gateway, and vector indexes — the surface agents actually run on.
Learn moreGeneCatalog
Decides what each agent may read or write, per action, under the identity that invoked it.
Learn moreGeneFlow
Supplies the governed tables agents ground on, and is the surface agents author pipelines into.
Learn moreCortex SQL
Resolves natural-language questions against certified metric definitions rather than free-form SQL over raw tables.
Learn moreAgent Runtime
A goal becomes a plan, and every step is authorised.
The difference between a demo and a deployable agent is what happens between intent and action. Each pass through this loop is authorised against the invoking user's rights, checked against policy, and recorded — so an agent can be given real work without being given standing access.
How Deployment Works
Scope, ground, supervise, promote.
Agents earn autonomy rather than starting with it — every deployment begins supervised and widens as its record justifies.
Scope the mandate
Define which tools an agent may call and which data it may reach. The tool catalog is an allowlist, not a suggestion.
Ground in your data
Retrieval runs against governed warehouse tables and the catalog, so answers cite real assets rather than a stale training snapshot.
Run supervised first
Agents propose changes for human approval while their accuracy is measured against a held-out set of real tasks.
Promote on evidence
Autonomy is widened per task type once measured performance justifies it, and narrowed automatically if quality regresses.
Agent Catalog
Agents that do specific, checkable work.
Broad, open-ended autonomy is hard to trust. Each of these has a narrow mandate and a verifiable output.
Pipeline author
Drafts a pipeline from a natural-language spec, including quality assertions, and opens it as a reviewable change.
Incident triage
Correlates a failing task with recent schema changes, upstream freshness, and similar past incidents before anyone is paged.
Root-cause analyst
Walks the lineage graph backwards from a wrong number to the transform or source that introduced the discrepancy.
Analytics responder
Answers business questions against certified metric definitions, returning the query it ran alongside the answer.
Reconciliation agent
Compares figures across systems on a schedule and raises differences with the supporting rows attached.
Per-step audit
Every prompt, retrieval, tool call, and result retained and replayable — the whole trace, not just the final answer.
Who Benefits
Where autonomy pays off first.
The best early candidates are tasks that are repetitive, well-specified, and currently done by someone reading dashboards at an inconvenient hour.
Data Engineer
Arrive at an incident with a hypothesis
The triage agent has already correlated the failure with recent changes and upstream signals by the time a human opens the alert.
Mean time to diagnosis measured in minutes.
Business User
Ask without learning SQL
Questions resolve against certified definitions, and the agent returns the query it ran so an analyst can verify the logic.
Self-service answers that survive scrutiny.
CISO / Risk
Approve AI without a blind spot
Agents act with the invoking user's rights and every step is recorded, so the audit position is no weaker than for human access.
Autonomy with an unbroken audit trail.
Versus Point Solutions
What changes when agents run inside the platform.
Bolt-on AI assistants need a copy of your data and answer to a different permission model than the rest of your stack.
| Dimension | Before Genedata | With Genedata |
|---|---|---|
| Data access | A service account with broad standing rights | The invoking user's rights, checked per action |
| Grounding | A corpus copied into a separate index | Governed tables and the live catalog |
| Tool use | Whatever the model decides to call | An explicit allowlisted tool catalog |
| Auditability | A chat transcript | Per-step traces, retained and replayable |
| Rollout | Enabled for everyone at once | Supervised first, autonomy widened on measured evidence |
99.9%Uptime objective
Governed, multi-providerModel Gateway
Per-stepAudit Granularity
SaaS · VPC · On-premDeployment
The next step
Operate at AI speed without losing the audit trail.
Genedata AI Agentic gives every team an autonomous data partner that respects your data perimeter — turning natural-language goals into safely executed, fully observable workflows.
FAQ
Agentic AI, answered.
What platform and security teams ask before putting agents near production data.
What stops an agent doing something destructive?
Agents act under the invoking user's identity and every action is authorised per call, so an agent's blast radius is exactly its operator's. Write operations additionally pass the same approval gates a human would face.
How do we audit what an agent did?
Every prompt, retrieval, tool call, and response is retained as a decision trace — not just the final answer — so an agent's reasoning path can be replayed months later.
Can agents use our own models?
Yes. The gateway fronts hosted, open-source, and on-premise models behind one endpoint, so an agent can run entirely against models inside your perimeter if policy requires it.
What prevents prompt injection reaching our data?
Retrieval is scoped by policy before it reaches the model, and tool calls are authorised individually rather than granted as a session capability. Content retrieved is treated as data, so an instruction embedded in a document cannot escalate the agent's permissions.
How is agent spend controlled?
Per-call token and cost tracking attributed to team and project, with budgets that alert before overrun rather than on the invoice.
Related content
Architecture
Agentic Reference Architecture
Agent runtime, tool catalog, and IAM enforcement model.
GuideAgent Guardrails
Identity inheritance, tool scoping, and decision-trace retention.
GuideGrounding on Governed Tables
Retrieval without copying data into a second store.
RelatedGovernance
The policy layer every agent action is evaluated against.
Take the next step
Put an agent on a real workflow.
Watch an agent triage a live incident end to end, under full policy and audit.