Agentic data is the key to Agentic AI, and its importance is on the rise. But what is it and how does it work? Agentic data refers to all the digital traces left by autonomous AI systems as they work toward their goals. Unlike simple chatbots that only generate text responses, agentic AI systems can plan multi-step workflows, use external tools, retain memory across sessions, and take real actions within business systems.
Every step in this process generates data, including conversation turns, reasoning traces, tool calls with inputs and outputs, memory updates, cost metrics, safety signals, and error conditions.
Agentic data is based on a firm data foundation
Importantly, agentic data doesn’t spring out of nowhere. This data flows through the same systems your existing data pipelines already touch. That’s critical because it means your existing infrastructure is already part of the solution.
In production, this can take many forms. Kafka Streams might carry real-time agent events, Elasticsearch could store observability traces, MongoDB might store session state, and Iceberg tables could provide long-term analytical storage. The true challenge isn’t finding places to put this data. Instead, the real problem is making sense of it all when it’s scattered across multiple systems with different schemas and retention policies.
For all of these reasons, organizations are scrambling to capture and analyze these traces.
This article will unpack what’s involved in that scramble. We’ll look at what makes ingestion so complex and how data teams are building robust pipelines to handle this new category of information. Most importantly, you’ll learn practical approaches for getting started without rebuilding your entire data stack.
Unpacking agentic data
The moment you start running AI agents in production, something interesting happens. Agentic data becomes your window into system behavior that would otherwise remain invisible. That’s the real power of both Agentic AI and the Agentic data that powers it.
Examples of agentic data in action
Consider customer service operations, where Gartner predicts that agentic AI will autonomously resolve 80% of common issues by 2029. Without detailed traces of agent decision-making, you’re essentially flying blind on resolution quality, escalation patterns, and failure modes. This visibility becomes critical when agents move beyond simple conversations into business-critical workflows.
How agentic data operates in financial services
The same is true in the financial services world. Financial services data analytics teams use agentic data to track compliance across automated trading decisions and risk assessments. Healthcare data analytics organizations monitor agents that support diagnosis, ensuring each step in the reasoning process meets regulatory requirements. Manufacturing companies analyze agents that manage supply chain disruptions, measuring how quickly they identify alternative suppliers and reroute shipments.
The technical foundation for this analysis comes from emerging observability standards. What makes agentic data particularly valuable is its multi-dimensional nature. Unlike traditional application logs that focus on system performance, agentic data reveals cognitive patterns. You can analyze not just what an agent did, but why it made specific choices, which knowledge it retrieved, and where its reasoning broke down. This insight directly informs prompt engineering, tool selection, and guardrail configuration.
Wrestling with agentic data complexity
All of this goes to the heart of the complexity of agentic AI, especially the management of agentic data. To understand why agentic data is difficult to manage, it helps to start with understanding where this data is actually located.
Understanding the decentralization of agentic data
Part of the problem is decentralization. In most production environments, agentic data is spread across the business. A single agent session does not write to one system. It writes to many simultaneously. The reasoning traces to your observability platform. Tool call inputs and outputs get written to application logs. Memory updates flow through a session store. Cost and latency metrics end up in a separate monitoring system. And on top of all of that, the raw conversation turns are retained directly by the agent platform itself.
That last point is where things get particularly complicated. OpenAI retains run step data for 30 days, Anthropic’s tool use records follow their own retention schedule, and AWS Bedrock sessions are governed by a separate policy altogether. When those retention windows close, any data that was not captured into long-term storage is simply gone. This means that pipeline reliability is not just a performance concern. It is a data preservation concern.
Schema evolution headaches
With that distribution problem in mind, consider what each of those systems is actually storing. Every platform has its own schema, its own query syntax, and its own assumptions about what an agent interaction looks like. That alone creates significant engineering complexity. But what makes it substantially harder to solve is that the underlying data has no fixed shape to begin with.
To see why, consider what happens inside a single-agent conversation. It can contain branching reasoning chains, parallel tool calls that resolve out of order, mid-session retrieval operations, and error recovery paths that restart steps already in progress. The structure of any given trace depends on which agent ran, what tools it had access to, and what conditions it encountered at runtime. The result is that no two traces are guaranteed to look the same, even when they come from the same agent running the same task.
This is where traditional ETL pipelines begin to break down. Those pipelines are designed around a core assumption that incoming data has a predictable, stable structure. Agentic data violates that assumption by design. It is deeply nested, variable in length, and structurally inconsistent from one run to the next. The practical implication is straightforward. An ingestion architecture built for agentic data cannot treat schema variability as an edge case to be handled. It has to treat it as the default condition.
Multi-system data sprawl
Agentic data doesn’t live in one place. Streaming platforms like Kafka carry real-time events, but agent platforms often retain detailed traces in their own systems for only 30 days before automatically deleting them. Long-term storage requirements push teams toward an open data lakehouse architecture, while operational teams need immediate access through observability platforms like Elasticsearch or Splunk.
This distribution creates data engineering nightmares. A single agent session might generate events in five different systems, each with its own authentication, query syntax, and performance characteristics. Joining traces across these systems for root cause analysis becomes an exercise in complex federation, especially when time-sensitive incident response is involved.
Security and governance gaps
Traditional data governance models weren’t designed for agentic workloads. Agent conversations often contain PII, proprietary information, or sensitive context that needs careful handling. But unlike structured database records, this sensitive data appears throughout nested JSON fields in unpredictable locations. Standard masking and filtering approaches struggle with the complexity.
The challenge intensifies with emerging agent frameworks. Model Context Protocol (MCP) servers and similar tool integration patterns create new attack surfaces that require additional monitoring and governance. Teams need to balance comprehensive observability with security requirements, often lacking clear policies for retention, access control, and cross-border data movement in agent contexts.
Building robust agentic data pipelines
Successfully ingesting agentic data requires embracing federation rather than fighting it. Instead of trying to copy everything into a single warehouse, effective architectures query data where it naturally lives while providing unified access and governance. This approach acknowledges that streaming systems excel at real-time processing, search platforms optimize for text analysis, and open data lakehouse solutions provide cost-effective long-term retention.
Starting with streaming foundations
Begin your agentic data journey by establishing reliable capture from real-time sources. Kafka connectors provide the backbone for ingesting agent events as they occur, supporting the high-concurrency, low-latency access patterns required by agent monitoring. Configure authentication carefully—many agent platforms support OAuth, SCRAM, or Kerberos for secure event streaming.
The key insight is preserving raw event fidelity while building curated views for common access patterns. Store complete JSON payloads in your lakehouse for comprehensive analysis, but also extract frequently queried fields into optimized table structures. This dual approach gives you both flexibility for exploratory analysis and performance for operational dashboards.
Consider partitioning strategies that align with your query patterns. Agent trace tables typically benefit from partitioning by event timestamp, agent type, and operation category. This structure enables efficient filtering for time-range analyses and makes cost management easier when dealing with high-volume agent workloads. For optimal performance, consider optimizing Iceberg table performance strategies.
Optimizing for analytical performance
Agentic data analysis involves complex queries across nested JSON structures and time-series patterns. Warp Speed acceleration can dramatically improve interactive exploration performance on lakehouse data, while result caching helps dashboard queries that repeatedly analyze agent metrics.
For heavy analytical workloads, fault-tolerant execution ensures that expensive joins between agent traces and business data complete successfully even when individual nodes fail. This reliability becomes crucial when analyzing correlations between agent behavior and customer outcomes across large datasets.
Implementing comprehensive governance
Agentic data governance requires thinking beyond traditional row-and-column access controls. Use service accounts for automated pipelines that ingest agent data, ensuring non-human workloads operate with appropriate privileges. Configure row-level filtering and column masking to protect sensitive information within agent conversations.
If you already use governance platforms like Apache Ranger, policy import capabilities let you extend existing access controls to agentic datasets. This integration becomes particularly important in regulated industries where agent decision-making must meet the same compliance standards as human-driven processes.
Practical steps for implementation success
The path to successful agentic data ingestion starts with understanding your organization’s current agent deployments and their data generation patterns. Survey existing AI and analytics solutions to identify which platforms they use, what types of traces they generate, and where this data currently lands. This inventory reveals integration opportunities and helps prioritize connector implementations.
Establishing data standards early
Adopt the OpenTelemetry GenAI semantic conventions as your canonical model, even though the specifications are still under development. Design your lakehouse schema with versioning support to handle future convention changes gracefully. Create curated views that normalize data from different providers into consistent structures, using native JSON processing functions to efficiently extract and transform nested fields.
Document your field mappings and transformation logic carefully. As agent platforms evolve their APIs and new providers emerge, these mappings become critical institutional knowledge. Consider creating reusable transformation templates that can adapt to new trace formats with minimal modification, following proven ELT data processing patterns.
Building incrementally with federation
Rather than migrating all agentic data into a single system immediately, start by federating access to existing stores. Query agent traces directly in OpenSearch for recent operational analysis while accessing historical data from what is Apache Iceberg tables.
This approach provides immediate value while you build more sophisticated ingestion pipelines and can be implemented using either the Starburst Galaxy for the cloud or Starburst Enterprise for hybrid deployments. You can also use the Starburst AI Data Assistant (AIDA) to leverage even more conversational insight.
Monitoring and iteration
Implement comprehensive monitoring of your agentic data pipelines themselves. Track ingestion latency, schema evolution events, data quality metrics, and governance policy violations. Agent data volumes can grow unpredictably as workloads scale, so establish alerting for unusual patterns that might indicate configuration drift or security incidents.
Plan for regular pipeline reviews as the agentic AI ecosystem continues evolving. New agent frameworks, updated API specifications, and changing business requirements will drive ongoing refinements to your ingestion architecture. Build flexibility into your design decisions, favoring approaches that can adapt to new data sources and access patterns.
The future of agentic data lies in seamless integration between autonomous AI systems and traditional business intelligence workflows. Teams that establish robust ingestion capabilities today will be positioned to unlock insights from AI agent behavior at scale, turning what once was invisible cognitive processing into measurable business intelligence. Your data engineering expertise becomes the foundation for understanding and optimizing the AI agents that increasingly drive business outcomes.
Want to understand more about Starburst and Agentic AI? Read the Agentic Workforce whitepaper.
