Agentic automation is having a moment. The promise is seductive: AI agents that can interpret context, make decisions, and trigger real work across systems with minimal hand-holding.

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But there’s a quiet reality most teams run into fast:

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If you don’t understand how work actually flows today, you can’t safely automate what happens tomorrow.

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That’s why process mining is quickly becoming the difference between “cool demos” and production-grade agentic automation. By reconstructing real workflows from event logs, process mining reveals bottlenecks, variants, rework loops, and compliance gaps that never show up in neat process maps. It gives AI agents something they desperately need: operational truth.

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In this article, we’ll unpack how process mining drives agentic automation, what typically breaks when you skip it, and a practical “process-to-agent” approach, including examples and perspective from Verdant Data.

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What is agentic automation (and what makes it risky)

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Agentic automation is often described as an execution layer that turns an agent’s decisions into real work, combining AI agents, automation, integrations, and people to complete multi-step tasks across systems.

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That combination is powerful, but it raises the stakes:

• Agents act across applications, not just one interface or workflow.
• They must handle exceptions, ambiguity, and messy inputs.
• Without guardrails, autonomy can scale inconsistency faster than humans ever could.

This is where many programmes stumble. Teams build the agent first, and only later realise they don’t have shared clarity on what “the process” even is.

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Process mining: the operational truth layer agents have been missing

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Process mining is evidence-based process analysis using event logs. It’s typically framed as three core capabilities:

• Discovery: what the process really looks like in reality
• Conformance checking: where reality deviates from policy, controls, or intended models
• Enhancement: how to improve or extend the process using data

For leaders, a simple way to think about process mining is this: it turns event data into a digital trace of work, showing patterns, variants, and performance across the full flow, including cycle time, handoffs, and rework.

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And that’s exactly what agentic automation needs to become reliable: not assumptions, but evidence.

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Why process mining is the driver for agentic automation (not just a nice-to-have)

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1) Context grounding for agents

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Agents need to know what “normal” looks like, what’s allowed, and where decisions should escalate. Many automation platforms describe this as context grounding or creating a trusted operational context.

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Process mining provides grounding by showing:

• The real sequence of steps (including hidden work)
• The real frequency of process variants
• The real triggers of exceptions and delays

2) Exception intelligence (the place agents fail first)

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Most automation failures happen in edge cases, not happy paths. Process mining helps teams quantify exceptions before automating them:

• Which exceptions are common vs rare?
• Which correlate with rework or compliance risk?
• Where do exceptions originate: upstream quality, missing data, broken handoffs?

3) Automation readiness, you can defend with data

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Process mining helps teams separate:

• Steps that are stable and deterministic (often better for automation or bots)
• Steps that require judgment (human-in-the-loop)
• Steps where agents can orchestrate work across systems with guardrails

This is huge for executive buy-in, because it turns “we think this could work” into “here’s what the data shows will work”.

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4) Governance and auditability

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Conformance checking compares event logs to policies or models and surfaces deviations. That becomes critical when you’re introducing systems that can act autonomously.

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It also supports a more mature governance approach: you can explicitly define what an agent is allowed to do, what requires escalation, and what must always follow a controlled path.

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5) Measurement and ROI baselines

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Agents don’t get funded on vibes.

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Process mining gives you baselines and impact tracking tied to real flow metrics: cycle time, SLA breaches, rework rate, throughput, and compliance variance. Those baselines are what allow agentic automation to scale beyond pilots.

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Verdant Data’s Andrew Johnston puts it plainly: “True strategic value comes when AI is embedded into core processes, where it can drive measurable operational improvement.”

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The “Process-to-Agent” pipeline (how to make this real)

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A practical approach that works for both internal stakeholders and delivery teams looks like this:

1. Extract event logs from ERP, CRM, ticketing, workflow, and operational systems
2. Discover the real process (variants, bottlenecks, rework loops)
3. Run conformance checks against policies, controls, or desired flows
4. Choose the execution pattern
5. Build the grounding pack for the agent
6. Monitor continuously for drift (process changes, new variants, rising exceptions)

Done well, this turns agentic automation into a controlled system, not a free-roaming experiment.

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Where agentic automation usually fails (and how process mining prevents it)

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Failure mode: “We automated the wrong thing.” Process mining fix: It shows where time and cost are truly leaking, often in rework loops and handoffs.

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Failure mode: “The process has 50 variants, and we built for 3.” Fix: Variant analysis helps you prioritise which variants to automate first and where standardisation will unlock scale.

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Failure mode: “Compliance found gaps after go-live.” Fix: Conformance checking surfaces policy deviations early, using event evidence.

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Failure mode: “The agent performs well until the business changes.” Fix: Continuous monitoring detects drift so you can update rules, thresholds, and controls before performance drops.

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Verdant Data examples: what measurable impact can look like

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Verdant Data has shared examples where process intelligence and AI-enabled process mining translate into tangible outcomes:

• A large Swiss corporate bank achieved results that contributed to a 30% reduction in transaction errors, alongside stronger compliance outcomes.
• A German manufacturing company reduced energy consumption by 20%, improving both cost efficiency and sustainability performance.

These outcomes matter because they frame agentic automation correctly. It’s not AI for AI’s sake. It’s measurable operational performance.

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And on the “context grounding” theme, Ryan Beangstrom summed up the combination neatly in a LinkedIn comment: “AI agents and process mining as the context grounding.”

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What to do next (if you’re exploring agentic automation)

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If you’re leading an agentic automation initiative, the next best step usually isn’t “build the agent”.

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It’s this:

• Pick one high-volume, high-rework process
• Use process mining to identify variants, bottlenecks, and exception drivers
• Define human-in-the-loop points and governance requirements
• Pilot agentic automation with clear baselines: cycle time, rework rate, SLA stability
• Monitor for drift and scale based on evidence

If you’re considering agentic automation and want to ground it in what’s actually happening inside your operations, Verdant Data’s process intelligence approach can help identify the best candidates, quantify ROI, and design governed execution that scales.

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Start by asking: Which process is costing us the most in rework and delay, and what does the event data say is really happening?