Acceldata announced AI Observability, a solution that brings observability for LLM and agentic AI applications to Acceldata’s xLake Data & AI Platform.

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.

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.”

Acceldata’s approach to AI Observability:

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.”

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.

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 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.