What is Data + AI Observability?
Data + AI Observability is a comprehensive approach to observing the health and reliability of your data, system, code and AI models end-to-end. It closes the loop between data inputs and AI agent outputs—ensuring trust, scale, and business impact.
The agent trust gap is widening
AI adoption is accelerating. Organizational trust isn't keeping pace.
Data arrives incomplete, stale, or silently changed.
Models drift, hallucinate, and produce biased results.
Leaders won't put AI into production they can't verify.
“More than 40% of companies don't trust the outputs of their AI/ML models and more than 45% of companies cite data quality as the top obstacle to AI success ”
BARC, Observability for AI Innovation, 2025
Data observability and AI observability, in isolation
Data observability ensures data pipelines are accurate, complete, and timely. AI observability monitors the model performance, drift, and bias.
The problem? Looking at one without the other creates blind spots.
- If you only observe data, you might miss model behaviors like hallucinations or bias.
- If you only observe models, you risk overlooking upstream data issues that silently degrade performance.
- And when agents act autonomously, neither view is sufficient on its own. An agent can draw on reliable data, run on a healthy model, and still take the wrong action — calling the wrong tool, skipping a required step, or producing an answer that’s fluent and wrong. Verifying the inputs and verifying the model doesn’t tell you whether the agent behaved correctly.
Data + AI Observability Is Comprehensive
Data + AI Observability is the comprehensive monitoring, analysis, and understanding of data and AI systems' health, performance, and reliability to proactively detect and resolve issues across the entire lifecycle.
It’s a holistic approach that connects—
- Data inputs → quality, lineage, and pipeline reliability
- System & code → infrastructure and transformations
- AI agent models and outputs → drift, bias, and answer quality in production
By closing the loop across the entire lifecycle, Data + AI Observability ensures that reliable inputs drive trustworthy outputs—and bridges the trust gap between data teams, AI teams, and business leaders.
Benefits of end-to-end data and AI observability
Without end-to-end observability, most organizations stall at pilots. A holistic approach builds the trust and reliability needed to take AI into production at scale.
- Trust → Reliable inputs produce reliable outputs.
- Efficiency → Free up engineering time from constant firefighting.
- Risk Reduction → Stay ahead of compliance, privacy, and bias challenges.
- Innovation → Confidently scale GenAI and agentic AI initiatives.
- Collaboration → Align data, AI, and business teams around shared outcomes.
"Since we're already monitoring data in our data lake with Monte Carlo, combining data and agent observability in a single platform gives us visibility into the full agent lifecycle — from the structured data to the unstructured knowledge base to the agent’s behavior — all in one place."
Travis Lawrence, Senior ML Manager, Pilot Flying J
Trust is the outcome.
Observability is how you earn it.
Data + AI observability emerged to answer a question about pipelines and models: is what we built working? Agents change the question. When software reasons, chooses tools, and acts without a human in the loop, reliability isn't only a property of the inputs — it's a property of the behavior.
Agent trust is the practice of verifying that AI agents operate correctly in production, across four dimensions: the context they draw on, their performance, their behavior, and their output. It doesn't replace data and AI observability. It's what the discipline becomes when the systems consuming your data start acting on it.