As AI agents move from experimentation into real business operations, a new question is emerging: how do you actually know what your agents are doing at scale?
QPR Software will join the data and AI community at Snowflake World Tour Stockholm on September 23, 2026, bringing a Process Intelligence perspective to one of the most important questions in enterprise AI today: how to understand and improve the behavior of AI agents at scale.
This emerging challenge is what QPR calls AI Agent Mining: applying Process Mining and Process Intelligence to understand how AI agents actually execute work across many runs.
AI agents are rapidly becoming part of how work gets done. But as organizations move from individual agent experiments toward enterprise-wide deployment, visibility becomes a new challenge.
A single trace can show what happened in one agent run. But what happens across hundreds or thousands of runs?
Where are agents looping, retrying or deviating from the intended flow? Which execution patterns are driving unnecessary token usage, latency or failures? And when governance or audit asks what AI agents actually did, can organizations see the bigger picture?
This is where QPR is taking Process Intelligence next.
QPR AI Agent Mining for Snowflake applies Process Mining and Process Intelligence to AI agent execution data, turning individual agent traces into a process-level view of recurring execution paths, variants, loops, deviations, costs and outcomes across many agent runs.
Instead of looking only at one run at a time, organizations can uncover:
actual execution paths and variants across agent runs
loops, retries, deviations and rework
token usage, latency and failure patterns
differences between successful and unsuccessful executions
evidence of how agents behaved across the organization
The goal is not to replace AI observability. It is to answer a different question.
Traditional AI agent observability helps explain what happened in an individual run. AI Agent Mining reveals patterns across many agent runs — how agents behave as a system, where inefficiencies emerge and where that behavior can be improved.
QPR AI Agent Mining builds on QPR ProcessAnalyzer, QPR Software’s Process Intelligence platform, and its Snowflake-native architecture, allowing AI agent execution data to be analyzed directly within Snowflake.
That means organizations can bring Process Intelligence to agent behavior without introducing another analytical data silo or unnecessary data movement.
The approach extends QPR’s broader vision for enterprise AI: as AI becomes more deeply embedded in business operations, enterprises need both sides of the equation.
AI needs to understand how the business operates. And the business needs to understand how its AI operates.
QPR is addressing both.
Through QPR ProcessAnalyzer and its Model Context Protocol (MCP) capabilities, AI agents can access trusted Process Intelligence and operational context from enterprise processes.
With QPR AI Agent Mining for Snowflake, Process Intelligence can also be turned toward the agents themselves — revealing how AI-driven work actually executes at scale.
Snowflake World Tour Stockholm brings together data, AI and technology leaders exploring how organizations can move AI from ambition to measurable enterprise impact.
QPR Software will be there to exchange ideas around Snowflake-native Process Intelligence, enterprise AI and the emerging field of AI Agent Mining.
If you are attending Snowflake World Tour Stockholm and would like to discuss how Process Intelligence can help organizations both power AI agents with operational context and understand how those agents actually behave, get in touch with QPR.
Snowflake World Tour Stockholm
September 23, 2026
3Arena, Stockholm