AI Agent Mining · Native to Snowflake

See how your AI agents actually work

QPR Agent Mining for Snowflake turns the data your AI agents already record into a clear view of how they work: the routes they take, where they loop or fail, and what drives cost. 

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Trusted by global enterprises

Your AI agents keep a diary. Nobody is reading it.

Every time an AI agent runs, it records every step: which tools it called, where it retried, how much it cost. That record already sits in your Snowflake account.

But most teams can only read it one run at a time. So the simple questions stay unanswered:

  • Why did our AI bill go up?
  • Which routes are our agents actually taking?
  • Where do they loop, fail or get stuck?
  • Can we show what an agent did when audit asks?

Looking at one run at a time is like trying to understand traffic by watching a single car. QPR Agent Mining reads the whole diary, as a process.

 

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Read every agent run as a process.  

QPR Agent Mining applies process mining to your AI agents. It reads the steps your agents have already recorded and rebuilds every run as a process, so you see the patterns behind them, not just one run at a time.

Agent data → QPR Agent Mining → Routes · Loops · Failures · Cost · Outcomes

Runs natively in Snowflake, using data already in your account.



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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

  • Inspect one run at a time
  • View steps, errors and performance
  • Best for investigating a specific run

Answers: What happened in this run?



 

External AI Observability

Third-party monitoring tools

  • Debug individual calls and track metrics over time
  • Monitor errors, quality scores and performance trends
  • Best for finding where a specific call broke

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

QPR AI Agent Mining for Snowflake

A process-level view across all agent runs

  • Discovers the routes, loops and rework across every run
  • Ranks routes by cost, speed and failure rate
  • Shows how your agents behave at scale

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

What QPR Agent Mining reveals

Understand how agents actually behave

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

See the real routes

Discover the routes your agents actually take, where they loop and where they drift from the intended flow. 

Improve reliability

Find the patterns behind failed, slow or overly complex runs, and see what successful runs do differently. 

Control cost

See which agents, tools and routes drive token and compute spend, including the cost of rework. 

Strengthen governance

Compare what agents actually did with what they were supposed to do, backed by an evidence-based record of every run. 

Native to Snowflake

Runs where your agent data already lives. 

Built on QPR ProcessAnalyzer's Snowflake-native architecture, QPR Agent Mining analyzes your agents' data directly in Snowflake. No new data silo, no separate monitoring tool.

  • No data moved out of Snowflake
  • No changes to your agents
  • No new data pipeline

Your data stays within your Snowflake environment and existing governance model.

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Why QPR

Process Intelligence is what we do.

QPR has spent decades making complex enterprise work visible, measurable and improvable. QPR Agent Mining extends that to a new kind of work: work done by AI agents. 

 

Built for enterprise complexity.

AI agents branch, retry, call tools, fail and adapt. Process intelligence reveals those patterns across thousands of runs, not one trace at a time. 

 

From business processes to agent processes. 

The same process mining that global enterprises use to improve their operations now shows how your AI agents work. 

Start reading your agents' diary.

See the routes, 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 AI 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 AI 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 AI 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 AI 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.