Tuesday 04 August 2026 12:00 pm

Acceldata Brings AI Observability to Its xLake Data Platform

Acceldata, the market leader in agentic data management, today announced AI Observability, a solution that brings observability for LLM and agentic AI applications to Acceldata’s xLake Data & AI Platform.

AI reliability now turns on reach. Acceldata governs and traces AI across a hybrid estate, on-premises and across clouds, without first requiring the data to be consolidated in one place. That reach does two things at once. It makes agents safe to put into production, and it lets enterprises build agents against data they could never centralize. In enterprise AI, governance is treated as the brake. Here is the engine.

AI Observability traces every prompt, model call, tool invocation and retrieval step, continuously evaluating outputs to ensure they are correct, reliable and aligned with user intent. This adds to the data quality, lineage and pipeline monitoring the platform already provides.

AI Observability gives teams one view spanning data health and AI behavior, wherever the data lives, rather than a separate tool for each layer. While tracing and evaluating agents is becoming standard, governing them wherever the data lives is not.

“The top blocker to enterprise AI is fear of ungoverned agents and data. Acceldata removes it with the introduction of AI Observability to the xLake Data & AI Platform,” said Rohit Choudhary, Acceldata founder and CEO. “Now you can govern and trace AI wherever your data lives, on-premises or across clouds, which lets you build agents against data you could never centralize. Governance becomes the reason you can do more with your data, not less.”

What Sets Acceldata Apart

Two factors set Acceldata’s approach to AI Observability apart from the rest of the market.

First, Acceldata governs and traces an agent wherever the data lives, across a hybrid estate of on-premises and cloud, so organizations can trust the agents they run and build new agents with confidence.

“Tracing and evaluating agents is becoming standard; what matters is what a failure connects to,” said Ashwin Rajeeva, Acceldata co-founder and CTO. “We connect it to data quality, pipeline health, lineage, and compute across a hybrid estate, on-premises and cloud, on the platform teams already run for data observability.”

The hybrid estate is now the default. In a C-level survey Acceldata conducted via GLG, 80% of 40 C-level leaders at $5 billion-plus enterprises run hybrid architectures and 75% run four or more data platforms at once.

Second, an agent failure and the data behind it sit in one console. On the same platform that monitors data quality, lineage and pipelines, a team can follow a failing trace back to the data product or pipeline that produced it, instead of stopping at the model or the application layer. Existing solutions for observing agents each stop at a different boundary: the application and infrastructure layer, the cloud data warehouse or the model. Unlike tools anchored to a single cloud warehouse or the application layer, Acceldata reaches across the hybrid estate that spans public cloud and on-premises.

High-profile failures of agents acting outside their intended boundaries have moved ungoverned AI from a technical worry to a board-level risk. The exposure most enterprises face is subtler but far more common: agents built on their own data, taking actions no one can see, judge, or trace to a cause. McKinsey’s 2026 AI Trust Maturity Survey found that only about one-third of organizations report mature governance for agentic AI, even as boards push AI to the top of the infrastructure agenda.McKinsey also warned that the hardest failures to manage are the ones that cannot be reconstructed because the workflow was never logged.

Closing that gap takes three things in one place: observability of what the agent does, observability of the data it acts on and governance where the agent runs. Apart, each leaves a hole the others would fill, a failure you can see but not trace, or a policy you can write but not enforce. But on one platform, a team can catch a problem, judge whether it matters and follow it to the data behind it without leaving the console. Acceldata’s AI Observability brings the agent-behavior view to a platform already built on data observability and runtime governance.

The solution helps address issues customers face, including hallucinations, debugging without visibility, data quality issues, inflated token costs and other problems.

AI Observability Solves Key Governance Issues

AI Observability includes several additional features that help mitigate the governance issues plaguing organizations, including:

Full execution tracing. Every conversation resolves into a thread, trace and span structure across prompts, model calls, tool calls, retrieval and agent steps, so teams can debug a single run step by step.

Traceback to the data source. AI Observability shares one console with the platform’s data quality, lineage and pipeline views, so a failure seen in a trace can be followed to the data product or pipeline that produced it.

Continuous evaluation. Online evaluation scores production traffic, and offline evaluation runs regression and model comparison on curated datasets, using LLM-as-judge, heuristic and custom metrics such as hallucination, answer relevance and context precision. It alerts IT when quality, latency, cost or reliability crosses a threshold.

Feedback and dataset flywheel. With datasets treated as first-class objects, teams annotate traces with human feedback and promote production traces, including failures, into evaluation sets.

Cost and performance visibility. Token usage, cost, latency and error trends by project, model and application version.

Governance and runtime safety. Sensitive data and PII detection, configurable data masking, policy checks captured in the trace, audit of AI usage and multi-tenant isolation.

Broad framework coverage. Integrations across LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, ADK, OpenAI and Anthropic, plus SDK instrumentation for custom applications.

Acceldata AI Observability software is delivered as a native capability of the Acceldata platform rather than a standalone point product. Teams instrument applications through the SDK and framework integrations.

Additional Resources

About Acceldata

Founded in Campbell, Calif. in 2018, Acceldata is the pioneer of cross-lake data platform architecture. Its xLake Data & AI Platform supports every stage of the data lifecycle, on any platform, bringing compute to where the data lives and ensuring data trust by unifying data quality, governance and observability into an intelligent, AI-driven fabric. Trusted by Fortune 500 companies worldwide, Acceldata empowers businesses to unlock the full potential of their data in the AI era. Customers include Dun & Bradstreet, PubMatic, PhonePe (Walmart), HCSC and more. Acceldata investors include Insight Partners, March Capital, Industry Ventures, Lightspeed, Sorenson Ventures, Sanabil, Prosperity7 and Emergent Ventures.