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Operations AI agent · Process mining

Process Mining AI Agent

Replay what the systems already recorded — the order steps ran in, the queues they sat in, the loops they repeated — as an aggregate model, with individual attribution a separate consulted step.

4–6 weeksTypical delivery
Your stackDeployment
Aggregate firstNamed sponsor
Agent CareAfter launch

What this agent does

Models the process, not the person

In
01

Event logs are read out of ERP, ticketing, CRM and workflow systems, and each row is about a person.

02

A trace is built from those rows, and the aggregate model is what the agent publishes by default.

Reason
03

A log rich enough to model work is objectively suitable for monitoring it, which is the § 87(1)(6) test.

04

An indicator derived per employee, not the raw scanner log, is what CNIL held unlawful in SAN-2023-021.

05

A score a manager draws strongly on is itself the decision, and a human deciding later does not cure it.

Decide
06

A workflow is planned, and § 90(1) no. 3 attaches there, before any device is introduced at all.

07

An emotion inferred from a work pattern crosses Article 5(1)(f), which no configuration makes remediable.

Out
08

An event log goes live inside this agent, and Article 26(7) has the employer inform workers first.

09

Execute write actions only inside the approval boundaries agreed during implementation.

Product statement

Reading, joining and modelling belong to the agent. A named process sponsor releases the model, and any view that names a person waits on that sponsor and on consultation.

Example workflow

One process, log to release

AgentHuman
1Event log receivedERP, ticketing, CRM or workflow-engine export
2Cases assembledThe events, the order they ran in, the waits between them and the systems they came out of
3Aggregate model builtThe paths, the waits, the rework loops and confidence
4Controls appliedScope checks, cohort-size checks, attribution checks and model confidence
No human action required

Stages 1 to 4 run unaided, and nobody is named at any of them — the agent is modelling, and the sponsor lane opens at the attribution gate.

5DecisionSplits at the attribution gate
Aggregate only

Goes to the named sponsor to release.

Anything attributable

Adds an employment counsel read first.

Sponsor review

The model is held with its scope, its cohort sizes and the logs it was built from.

Release · Narrow scope · Send to employment counsel
Released — by the named sponsor
6Process and improvement records updatedOnly where write access and records policy allow it
7Outcome evaluatedScope stability, cohort sizes, sponsor corrections and what review found
Corrections

Every sponsor correction is counted in the evaluation.

What should not run autonomously

Human approval stays in control

Outside the boundary — human approval required8 items
Attributing a trace to a named employee.
Judging whether a person performed well.
Putting the agent into service unconsulted.
Ranking teams or individuals against each other.
Automation boundaryAgent acts unaided
Build the aggregate process model from the logs.
Report where work waits, loops back and hands over.
Hold individual attribution behind a separate and recorded request.
Flag any view that would identify a person and route it to consultation.
Attributable views are released inside the agreed boundaries, never ahead of the sponsor.
Deciding what the works council is told.
Using a process model in a disciplinary case.
Setting who counts as an identifiable actor.
Changes to attribution, retention or access rules.

Example output

One process model, annotated

This serves an operations team that will answer to a works council: in the Netherlands a decision taken without consent is void if nullity is invoked, and Austria conditions validity on consent.

Process model · single processIllustrative example
Process
Recorded as
Cohort
Source of record
Confidence
Held for
Purchase to pay, one region
Aggregate model, no actor named
Scope, consulted
Event logs, 3 August 2026
Held unreleased
The process sponsor, by name
As receivedBuilt from the event logs those systems already keep, and it reaches no further than those events.
What the record holds Event logs Consulted scope Cohort sizes
Why no name hereNaming an individual is a step the sponsor takes, after consultation.
ActionReleaseNarrow scopeSend to employment counsel
What the score decidesBelow the configured threshold a model picks up a counsel read before the sponsor sees it.

Value

Where AI adds value

The same four claims, placed at the point in the workflow where each one applies.

Where the value landsValue 01 – 04
Every event logFrom the system that recorded it
03Model

Where the model is used

A works agreement governs whether this may be deployed, not whether what it produced may later be used: BAG 2 AZR 296/22 holds that the works parties cannot create an exclusionary rule.

01Approved path

The log is about people

Article 5(1)(c) does not reach ordinary productivity analytics: limb (i) wants detriment in a context unrelated to the one the data came out of.

02Human review

What was checked, and not found

No US federal statute restricts an employer reading its own systems, and notice there is state law: New York, Connecticut and Delaware. Nothing in this landscape is certified, filed or attested, while a DPIA here is mandatory rather than discretionary.

04Build an evidence trail

The trace, the consultation record behind it and the sponsor who authorised it stay together.

Integrations

Typical integrations

Five system groups connect to the same agent. Which of them are in scope is decided in discovery.

ERP and finance systemsSAP · Oracle · Dynamics
Order, invoice and payment events
Ticketing and service desksServiceNow · Jira · Zendesk
Ticket, queue and handover events
CRM and case systemsSalesforce · Dynamics 365
Opportunity and case events

Agent

Process mining

Reads the logs
Builds the model
Holds for the sponsor

Workflow and BPM enginesCamunda · Pega · Appian
Task, timer and escalation events
Observability & evaluationOpenTelemetry · Langfuse
Supported monitoring/evaluation sources

Integration availability depends on the client's existing systems and API access.

Agent controls

Six screens between the log and a named person

Six screens stacked deep, the last the closest. What reaches you is set out in the map below.

L6 · Outermost — last line of defenceInward → L1 · closest to the model
L6Rollback / safe modeNarrow the agent to the aggregate model when evaluation or production signals degrade.Roll back
L5Version monitoringTrack model, prompt and scope rules; a system profiling performance at work stays high-risk whatever Article 6(3) seems to offer.Track
L4TraceabilityRecord each trace, the log under it and every read of the model; L1222-4 wants a device known to the employee before it collects.Record
L3Sponsor releaseHold an attributable view for the named sponsor; the hold governs release, not whether the model itself is right.Gate
L2Scope guardrailsTest each view against the consulted scope and refuse one reaching further; Annex III point 4(b) names monitoring performance and behaviour.Restrict
L1Confidence thresholdsRoute a thin trace, or a cohort small enough to name someone, to an employment counsel read first.Require review
Model coreModel built — the paths, the waits, the loops and confidence
L1 – L2Test whether a view may stand
L3Leaves the release to a named sponsor
L4 – L5Keep the trace and the consultation behind it
L6Stops at the aggregate model when signals degrade

How Nestack evaluates it

Evaluate the whole reconstruction — not only the model that comes out.

Coverage runs the whole depth of the workflow, and every layer is cut by slice.

Surface — the model an operations team reads
Depth of coverage ▼
E1Final-output evaluationDid the model reflect the events the logs actually hold?
E2Step-level evaluationDid the agent read the right systems, the right period and the consulted scope?
E3Tool evaluationDid it read and write the correct process and the correct case?
E4Confidence calibrationDo low-confidence traces actually attract more sponsor corrections?
E5Slice evaluationHow does performance change across specific processes?
E6Business outcomeHow many models needed a correction before the sponsor released?
Floor — the decision the company answers for

Failure modes

Where each failure originates in the agent

Seven failure modes, each placed at the stage the log first shows it.

Agent lifecycleDirection of processing →
01 · Retrieval1 mode
MU-03

Partial log read

The log read covers only part of the case.

Stage gathersThe systems, the logs, the cases and the actors
02 · Reasoning2 modes
MU-04

Model asserted, not mined

A path appears with no events under it.

MU-06

Cohort narrows to one actor

A group view resolves to a single person.

Stage proposesThe traces, the model and the attribution risk
03 · Tool / write2 modes
MU-02

Attributed view passed forward

A named view moves on without consultation.

MU-05

Trace bound to wrong case

Events are joined into the wrong case.

Stage writesOnly where write access and approval policy allow it
04 · Output1 mode
MU-01

Released, consultation unrecorded

The model shows release but not the consultation.

Stage returnsThe model a sponsor releases and a board reads
05 · Change / Version1 mode
MU-07

Silent scope drift

A model widens to fields nobody consulted on.

Stage tracksModel, prompt, scope rules and access rules
Sev-1 · a person named without consultation Sev-2 · a wrong path reaches the review Sev-3 · log degrades, model held back

Affected slices

Cross-system traces absorb the reviews

A process-level attribution figure can read clean while cross-system case traces carry most of the reviews. Nestack reports the review rate by process, not only in total.

Slice performance — reported separately, not only in aggregateIllustrative example
SliceFailure rateLift Lift vs. thresholdStatus
Cross-system case traces9.9%3.7× Review
Manual rework loops7.1%2.6× Review
Shared service queues4.4%1.6× Watch
Single-system processes1.8%0.7× Normal
Bar: review-rate lift vs. single-system baseline · scale 0–4.0× · tick marks the 2.0× review threshold 2 of 4 slices over threshold

Evidence-linked improvement

What an unconsulted rollout costs

A loop ends when the unconsulted deployment is a standing case. That suite is what the next model published is measured against.

Improvement cycle · five stagesSwitchback — the path turns at Improve and returns at Learn
01Detect

Review rate rises on cross-system case traces.

02Diagnose

The per-analyst handling-time chart nobody had told the works council about is worked backwards until one cause is left standing.

03Improve

A change goes out numbered, and the traces that forced it travel with it.

04Verify

Each touched trace case is run again, and one red holds it back.

05Learn

It stays as a standing test, and the consultation rules travel with it.

Learn → DetectThe return edge. The next model is measured against a suite one case longer.

Typical build scope

Twelve workstreams across six weeks

The build scope read against the delivery timeline. Week structure follows the six-week plan — discovery, sources, model assembly, evaluation, integration, then production validation and handover.

Workstream Week 1Week 2Week 3Week 4Week 5Week 6
01Attribution-boundary and consultation-scope work.
02ERP, ticketing, CRM and workflow sources.
03Event-to-case and actor-identifiability mapping.
04Event-log ingestion.
05Case, activity and timestamp binding.
06Cohort scoring and review routing.
07Sponsor release workflow.
08Process and improvement integration.
09Trace and consultation cases.
10Guardrails and disclosure controls.
11Event-trail instrumentation.
12Deployment, documentation and Agent Care handover.
12 workstreams · 6 weeks · bar shows the weeks a workstream is active — several run in parallel Final scope and sequence confirmed in discovery

Engagement tiers

What each tier includes

Rows are the capabilities named in each tier's scope. Higher tiers include everything below them.

Capability✓ in scope · — not at this tier PilotOne process, one region ProductionProduction process workflow AdvancedMultiple processes / jurisdictions
Introduced at Pilot
Model assembly from your logs
Named sponsor release
Event-log baseline
Introduced at Production
Reporting by process
Sponsor review workflow in your systems
Approved write-back
Event-source integration
Introduced at Advanced
Multi-system event correlation
Cross-process model packs
Large event volumes
Multi-jurisdiction consultation controls
Build price From $5,000 From $8,000 Custom quote
Final build priceConfirmed after discovery based on integrations, workflow complexity, event volume, approval controls and deployment requirements.
Separate from buildBuild pricing is separate from recurring Agent Care, which covers managed monitoring, evaluations, incidents and verified improvements after launch.

What we need from you

What you bring, and what we build with it

Each input maps to a piece of build scope and a week in the delivery timeline.

You bringWe build with it
01Your processes and the systems that log them Scope capture and attribution-boundary definitionWeek 1
02Representative event-log exports Log binding, case logic and the model baselineWeek 2
03Your works council contacts and consultation calendar Consultation mapping, scope binding and the automation boundaryWeek 1
04Access to relevant APIs, feeds or exports ERP, ticketing, CRM and workflow source assessment, then integration setupWeek 2
05Traces you would not want disclosed Consultation cases and the evaluation runWeek 4
06What no process model may establish Cohort scoring, review routing, guardrails and release controlsWeek 3
07A named sponsor who releases the model Release to the named sponsor, then pilot and production validationWeeks 5–6
Nothing else is required Deployment, documentation and Agent Care handover are ours.

Delivery timeline

Four phases across six weeks

Read the bands as measurements, not decoration. Two share week five because that is when they overlap.

Phase W1W2W3W4W5W6
Discovery W1
Build W2 – W3
Evaluate W4 – W5
Pilot & Launch W5 – W6
Week focus W1Process discovery, consultation mapping and the automation boundary W2Event-source integration and the event-log baseline W3Model assembly, attribution logic and release controls W4Evaluation suite, consultation cases and failure-mode testing W5Improvement-system integration, pilot processes and targeted corrections W6One discovery round run under the process sponsor, then Agent Care handover
Reading the bandEach band runs the weeks its own work is named for, and the fifth carries two because the work does.
At the end of W6Once the consultation record validates, Agent Care picks the agent up.
DurationSix-week plan shown · typical delivery 4–6 weeks depending on scope confirmed in discovery.

Next step · Operations AI agent

Build a process mining agent around the consultation your last dashboard never had.

Show us one process and the systems its events come out of. Does a works agreement make the monitoring lawful? It does not — C-65/23 leaves a court reviewing necessity in full — and co-determination binds today, while the AI Act high-risk duties wait until 2 December 2027.

Nestack Agents · Process miningAGT-OP-06 · Agent Care available after launch