QPR AI Agent Mining · Native to Snowflake

See how your AI agents actually work.

Turn AI agent execution data into Process Intelligence — revealing the paths agents take, where they deviate, what they cost and where they can be improved.

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Trusted by global enterprises

As AI agents scale, visibility becomes the challenge 

AI agents are increasingly planning, calling tools and executing work across enterprise systems.

The execution data exists. But understanding what is happening across hundreds or thousands of agent runs is much harder.

  • Which paths are agents actually taking?
  • Where are they looping, failing or deviating from the intended flow?
  • Which AI agents, tools and execution patterns are driving cost?
  • Can you prove what an AI agent actually did when governance or audit asks?

Individual traces help investigate individual runs. QPR AI Agent Mining reveals the patterns behind them.

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Understand How Your AI Agents Behave at Scale  

AI Agent Mining applies Process Mining and Process Intelligence to AI agent execution data — reconstructing actual agent behavior as a process across every run.

QPR Agent Mining turns AI agent execution data into a process-level view of how agents actually work — revealing the paths they take, where they deviate or loop, what they cost, and how performance varies across runs.

Agent execution data → QPR AI Agent Mining → Paths · Variants · Deviations · Cost · Outcomes

Runs natively in Snowflake, using execution data already available in your environment.

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See the pattern, not just the trace.

An agent run is not just a request. It is a process.

Built-in Agent Observability 

Native tools in the agent platform UI 

  • Inspect one run at a time 

  • View traces, spans, errors and performance 

  • Best for investigating a specific run

Answers:
What happened in this run?

External AI Observability 

Dedicated third-party observability tools 

  • Per-trace debugging plus aggregate metrics

  • Track errors, scores and performance trends

  • Best for analyzing one run or one call at a time

Answers:
Is my agent performing well, and where did this call break?

QPR AI Agent Mining

Process-level intelligence across all AI agent runs 

  • Discovers paths, variants, loops and rework across all runs

  • Ranks paths by tokens, latency and failure rate

  • Reveals how AI agents actually behave at scale

Answers:
How do our AI agents behave at scale — and where is the waste?

What Agent Mining reveals

Understand how agents actually behave

Discover real execution paths, variants, loops and exceptions across agent runs.

Improve performance and reliability

Identify patterns behind failed, slow or unnecessarily complex executions — and see what separates successful runs from unsuccessful ones.

Control cost

Understand token and compute consumption by agent, tool and execution path, including cost created by rework.

Strengthen governance

Compare actual behavior with intended execution and maintain an evidence-based view of what agents did, when and how.

Native to Snowflake

QPR AI Agent Mining runs where your agent execution data already lives.

Built on QPR ProcessAnalyzer's Snowflake-native architecture, it analyzes execution data directly in Snowflake — without creating another analytical data silo or monitoring layer.

  • No unnecessary data movement
  • No agent-side instrumentation
  • No additional data pipeline

Your execution data remains within your Snowflake environment and existing governance model. 

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Why QPR

Process Intelligence is what we do.

QPR has decades of experience making complex enterprise execution visible, measurable and improvable.

QPR AI Agent Mining extends that capability to a new kind of operational reality: work executed by AI agents.

Built for enterprise complexity.

AI Agents branch, retry, call tools, fail and adapt.

Process Intelligence reveals those patterns across thousands of executions — not only one trace at a time.

Native to your data platform.

QPR ProcessAnalyzer runs natively on Snowflake, bringing Process Intelligence to AI agent execution without introducing another separate data and analytics environment.

See How Your AI Agents Actually Work

Discover patterns, loops and waste across every agent run. 
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Frequently asked questions

What is AI Agent Mining?
AI Agent Mining applies Process Mining and Process Intelligence to AI agent execution data. It reconstructs agent runs as processes so organizations can analyze actual execution paths, variants, deviations, cost, performance and outcomes across many agent runs.

How is Agent Mining different from AI agent observability?
AI observability helps teams inspect traces, events and technical metrics from individual agent executions. Agent Mining adds a process-level view across executions, revealing recurring behavior, variants, loops, deviations, cost patterns and the differences between successful and unsuccessful runs.

What can QPR Agent Mining help us understand?
QPR Agent Mining helps organizations understand how agents actually execute work, which paths they take, where they loop or fail, which tools they use, what different execution patterns cost and where actual behavior differs from the intended process.

Does QPR Agent Mining move our data outside Snowflake?
QPR Agent Mining runs natively in Snowflake using QPR ProcessAnalyzer’s Snowflake-native architecture. Agent execution data can be analyzed within the customer’s Snowflake environment and existing governance model without creating an additional analytical data pipeline.

How can Agent Mining support AI governance?
Agent Mining provides an evidence-based view of how AI agents actually behaved. Organizations can compare actual execution with intended processes, investigate deviations and maintain a process-level record of agent activity to support governance, auditability and continuous improvement.