Acceldata Launches Autonomous Data & AI Platform for Agentic AI Era. Learn More →

Explore the future of AI-Native Data Management at Autonomous 26 | May 19 --> Save your spot

Agentic Data Management

AI that Validates Enterprise Data.
Not just answers.

Runtime validation for agentic systems — ensuring outputs are correct with respect to your data, not just plausible responses.

See ADM in Action

Explore Use Cases

TRUSTED BY ENTERPRISE DATA TEAMS WORLDWIDE

Not a model problem. A data problem.

Four root causes — observed across millions of datasets in production.

AI responds instantly. Data might be old.

Confident ≠ Current

High scores, stable trends — from a policy run 3 hours ago. The answer is correct for a moment that's passed.

Your table is healthy. Your answer is not.

Table-level ≠ Field-level

CUST_PROFILE shows green overall — but PHONE, ADDRESS, POSTAL_ZIP fail at 1.7%. The agent doesn't see field-level detail.

Quality doesn't survive transformation.

Source quality ≠ Downstream quality

Data passes ingestion checks. After joins and aggregations, lineage context is gone. The agent has no record of what changed.

Confident ≠ Correct.

High scores ≠ No issues

The agent says DQ checks are comprehensive — because it doesn't know what it isn't monitoring. Gaps exist exactly where nobody looked.

Observing what happened ≠ knowing if the answer was correct.

Validation

Freshness, coverage, incidents — per asset

Source verified, not just cited

Confidence score before execution

A runtime engine any agent can call before answering or acting

Inline validation — not post-hoc audit. Three functions, always available.

validate_data_context

Is the data behind this answer trustworthy?

Returns freshness, coverage, and active incidents for every asset in scope — plus a confidence score — before the answer is generated.

Freshness — last updated vs. query requirement
Coverage — are all needed dimensions monitored
Active incidents — open quality issues on any asset in scope
Confidence score — surfaced before response is returned

get_asset_reliability

How reliable is this asset right now?

Reliability summary at table, column, and partition level — not just the aggregate score that masks field-level failures.

Table score — overall asset health
Column breakdown — field-level failures the table score hides
Partition status — stale or failed runs caught before they surface
Trend — improving, degrading, or anomalous vs. baseline

explain_validation_risk

Why should this answer be warned or blocked?

Human-readable explanation of the risk — so agents can surface the reason and teams can act without digging through logs.

WARN: "CUST_PROFILE is 3 hrs old. Freshness SLA is 1 hr. Results may not reflect latest registrations."
BLOCK: "2 of 4 assets have open incidents. Confidence below threshold. Answer blocked."
PASS: "All assets fresh. No active incidents. Coverage complete. Confidence 0.91."

Five steps. Every query. No exceptions.

LangGraph supervisor: classify → route → validate → score → log. In that order, every time.

Classify Intent

LLM detects intent + complexity — simple single-domain vs. complex cross-system.

Select & Route

Fast path for 90% of queries via registry lookup. LLM planner only for multi-agent jobs.

Validate & Execute

Schema checks, parameter enforcement, then parallel execution via AgentExecutor.

Evaluate Results

Confidence score (0–1) on every response. Insufficient results loop back to select.

Synthesize & Log

Multi-agent results merged by LLM. Every action logged with full provenance.

Validation is architectural. Not bolted on.

Defined at the agent contract level —BaseAgent.validate_request()— runs before any execution begins.

Generic AI Agents

Fabricate entities when no results found

No visibility into response confidence

RAG drift goes undetected — faithfulness not measured

Multi-agent cascade failures with no recovery

No provenance — actions not logged or auditable

Acceldata Agent Validation

Result verification layer — every entity checked against live data

Confidence float (0–1) returned on every AgentResponse

DeepEval metrics: faithfulness, hallucination, contextual precision on every RAG call

Circuit breakers + graceful AgentResponse with error — N failures auto-disable agent

Full agent registry + execution log — every action with source, agent, and confidence

Ready to get started

Explore all the ways to experience Acceldata for yourself.

Expert-led Demos

Get a technical demo with live Q&A from a skilled professional.

Book a Demo

30-Day Free Trial

Experience the power of Data Observability firsthand.

Start Your Trial

Meet with Us

Let our experts help you achieve your data observability goals.

Products

AI that Validates Enterprise Data.Not just answers.