The dirty secret of agentic AI is that most systems fail because the data pipes feeding them broke three hours ago and nobody noticed.
The Summary
- Definity raised $12M Series A to embed monitoring agents directly inside Spark and DBT pipelines, catching failures during execution instead of after the damage is done.
- One enterprise customer found 33% of optimization opportunities in week one and cut troubleshooting time by 70%.
- The company claims customers are resolving complex Spark issues up to 10x faster by operating inside the execution layer, not reading logs after the fact.
The Signal
Most companies building agentic AI are optimizing the wrong layer. They're tuning models, refining prompts, and testing agent frameworks while assuming the data plumbing underneath just works. It doesn't. Definity's approach addresses a problem that scales exponentially with agent adoption: data pipelines that fail silently don't just break dashboards anymore. They break autonomous systems making real decisions.
The Chicago startup embeds agents directly into the Spark or DBT driver, the execution layer where data transformations actually happen. Traditional monitoring tools like Datadog, Databricks system tables, and platforms like Unravel Data all read metrics after a job completes. By that time, your agentic procurement system already ordered 40,000 units based on stale inventory data, or your AI sales assistant spent two hours generating proposals from last quarter's pricing.
"A pipeline that fails silently or delivers stale data doesn't just break a dashboard — it breaks the AI system depending on it."
CEO Roy Daniel frames the requirement cleanly: "You need three big things for agentic data operations: full stack context that is real time and production aware. Control of the pipeline. And the ability to validate in a feedback loop." Everything else is read-only forensics. The difference matters more as agents move from experimentation to production operations. An agent can retry a failed API call or rephrase a prompt, but it can't fix upstream data corruption it doesn't know exists.
The numbers from early deployments suggest this isn't theoretical. One enterprise customer identifying 33% of optimization opportunities in the first week points to how much silent failure lives in production pipelines. The 70% reduction in troubleshooting effort is the labor savings, but the real value is temporal: catching issues before they propagate downstream means agents don't make decisions on bad information.
The funding round's composition tells you who cares about this problem. GreatPoint Ventures led, but Dynatrace participated as both investor and potential competitor. Dynatrace has monitoring capabilities across the stack. Their investment signals either strategic hedge or acknowledgment that monitoring from outside the execution layer isn't enough once agents start operating autonomously at scale.
Key capabilities Definity claims:
- Real-time visibility into Spark job execution while it's running
- Ability to intervene during pipeline execution, not just alert after failure
- Closed-loop validation to confirm fixes worked before the next run
- Full stack context from infrastructure through application layer
The Implication
If you're building agentic systems, the weak link probably isn't your model or your framework. It's the data pipes feeding them. Traditional monitoring assumes humans are in the loop to notice dashboard alerts and investigate. Agents don't check dashboards. They consume data and act. The companies that figure out how to make pipelines self-healing before agents notice failures will ship agents that actually work in production. The rest will spend 2026 debugging why their autonomous systems keep making terrible decisions on perfect models.
Watch for more infrastructure plays focused on the execution layer, not the observation layer. The agent economy needs pipes that fix themselves.