Solution · Process & Automation

Work that requires judgement, performed by an agent

Rule-based automation manages work where the path is known in advance. A significant proportion of enterprise work is different: it requires interpretation, a decision and an action. That is where agents are applicable.

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Overview

Agents that complete work within controls that can be defended

An enterprise agent is a defined outcome, a permitted set of actions and a point at which it must stop.

X-Shift implements AI agents within the work that has resisted rule-based automation: reading unstructured content, interpreting it against policy, determining its meaning and acting across several systems to complete it. Each agent is scoped to a specific outcome and is measured on whether that outcome was achieved, rather than on the number of messages handled. It is granted named permissions in named systems, grounded in approved enterprise information, and required to escalate, with prepared context, where the situation exceeds its remit. Every decision and action is recorded, because in enterprise functions an action that cannot be reconstructed cannot be approved.

The agent platform is X.24, built by X-Shift. Outside the contact centre, this is significant for one reason: deployment control. Agents operate inside your estate, against your systems, under your credentials. This is often the difference between the risk function approving an automation in finance or HR and declining it because the data cannot leave.

The challenge

The work remaining after automation

Every automation programme reaches a ceiling, and that ceiling is consistently the same.

What is automated readily is what follows a fixed path. What remains is work that requires reading unstructured content, interpreting it against policy, determining its meaning and then acting across several systems. This work is high-volume, repetitive and costly, and it has resisted automation because it requires judgement rather than a rule.

Requests arriving as unstructured text that a person must interpret

Decisions requiring policy to be applied to a specific situation

Work spanning several systems with no single process to automate

Exceptions that consume disproportionate specialist time

Backlogs in back-office functions that additional staffing cannot clear

Knowledge work reliant on individuals who know where to locate the relevant information

What it takes

Governance is the barrier to deployment, not capability

Enterprise agent deployment stalls on trust rather than technology. These are the controls that resolve this.

01

Defined outcomes

Each agent has an explicit definition of a completed outcome and is measured against it.

Agentic AI & Enterprise AutomationIllustrative
FEATURE Defined outcomes By segment Permitted actions 88% Grounding 71% Traceability 64% Voice 43% Digital 29% Period rolling 12 weeks Figures illustrative

Escalation

Clear thresholds at which an agent stops and hands over to a person, with the context prepared.

Controlled release

Agents are promoted between environments through approval, with versioning and rollback.

Where it applies

Where it applies

Customer service is where agentic AI is most visible. It is not where the largest volume of agent-suitable work resides.

Finance operations

Invoice and document interpretation, matching, exception investigation and reconciliation across systems.

Human resources

Policy questions answered from approved sources, request intake, validation and routine case management.

Procurement

Supplier request management, document checks, compliance validation and status follow-up.

Shared services

High-volume internal request management where the request is received as free text rather than through a form.

Back-office execution

Multi-system record updates, validation and completion of work currently queued for manual processing.

Operations support

Triage, classification and first-line resolution of operational issues before escalation.

The distinction

Orchestration and agents solve different problems

These are complementary rather than competing. Establishing the boundary correctly is the difference between an architecture that scales and one that becomes unmaintainable.

HOW IT WORKS TODAY Process orchestration The path is defined in advance The same sequence runs every time Rules are explicit and versioned Deviation is treated as an exception to be managed Best suited to regulated, repeatable, auditable processes Answers: is this process being followed correctly? WHAT IT BECOMES Agentic AI The goal is defined; the path is not The agent reasons about how to achieve the outcome Interpretation of unstructured input is required Novel situations are managed rather than rejected Best suited to judgement-intensive, variable work Answers: has this outcome been achieved? HOW IT WORKS TODAY Process orchestration The path is defined in advance The same sequence runs every time Rules are explicit and versioned Deviation is an exception to be handled Best suited to regulated, repeatable, auditable processes Answers: is this process being followed correctly? WHAT IT BECOMES Agentic AI The goal is defined; the path is not The agent reasons about how to achieve the outcome Interpretation of unstructured input is required Novel situations are managed rather than rejected Best suited to judgement-intensive, variable work

Outcomes

What changes for the organisation

The measure is completed work, not conversations managed.

01

Automatable

Work that resisted rule-based automation becomes automatable

02

Backlogs addressable

Backlogs in judgement-intensive functions become addressable

03

Specialist time refocused

Specialist time is redirected from routine interpretation to genuine exceptions

04

Traceable actions

Actions are traceable, which is what makes them approvable

05

Scalable capacity

Capacity scales without a proportional increase in headcount

06

The ceiling moves

The automation ceiling is raised

Use cases

Queues that additional headcount fails to clear

Each of these is work in which content must be read, interpreted against policy and then acted upon across more than one system.

01

Trade finance documents checked manually with X.24

Situation

A trade finance team reads shipping documents against letter-of-credit terms line by line, and a discrepancy check that requires only minutes waits hours in a queue.

What happens
  1. Understand the request
  2. Retrieve and check
  3. Act in your system
  4. Confirm the outcome
Outcome

Routine document checks are completed with discrepancies flagged, and specialists review only the cases that require adjudication.

Commonly applied in Banking and across other sectors

02

Free-text requests received in Arabic with X.24

Situation

A shared services desk receives beneficiary requests written as free text in Arabic, and a person reads each one to determine which department should receive it.

What happens
  1. Understand the request
  2. Retrieve and check
  3. Act in your system
  4. Confirm the outcome
Outcome

X.24 interprets the request, routes it to the responsible department and opens the case with the required fields populated.

Commonly applied in Government and across other sectors

03

Claim rejections investigated manually with X.24

Situation

Rejected claims are returned with coded reasons, and a billing specialist opens three systems to determine whether the rejection was correct before resubmitting.

What happens
  1. Understand the request
  2. Retrieve and check
  3. Act in your system
  4. Confirm the outcome
Outcome

The investigation is completed across the systems and the resubmission prepared, with disputed rejections escalated to the specialist with context.

Commonly applied in Healthcare and across other sectors

More ways to apply it

Supplier invoices matched by exception with X.24

The common exception types are investigated and cleared automatically, and finance manages those that require a commercial decision.

Utilities

Procurement checks before award with X.24

Document checks are completed for every bidder with the failures evidenced, so the officer reviews findings rather than attachments.

Telecommunications

Back-office work priced by headcount with X.24

Routine interpretation is completed by agents under audit, and people are assigned to exceptions and client-specific judgement.

BPO
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These are illustrative applications rather than client deployments. The work suited to an agent depends on how clearly the outcome can be defined and what the agent is permitted to write.

Why we build this

Why X-Shift owns the agent platform

Deployment inside your estate

Back-office agents act on payroll, supplier and case data that frequently cannot leave the organisation. Building the platform enables it to operate where that data already resides, under your credentials, rather than requiring an exception from the risk function.

Actions defined against your systems

Enterprise work takes place in the systems that you operate, not those with published connectors. Because X-Shift owns the action layer, an agent's permitted actions are built against your integration list rather than limited to a vendor's catalogue.

A trace an auditor accepts

Approval for autonomous action depends on the ability to reconstruct a decision months later. Ownership of the platform ensures that the reasoning trace, the permission used and the resulting action are retained in a form that your audit function can examine.

Where partner technology satisfies the requirement more effectively, that will be the recommendation.

Before you evaluate

What to establish first

Common questions

What buyers ask before committing

Next

Where to go next

Run one agent against a live backlog

Select a back-office queue that additional headcount has never cleared, and review an agent working it against a sandbox of your systems, including the cases that it declines and escalates.

Applicable where a backlog exists that is accepted as permanent.

Request a working demonstration

Establish the automation architecture first

Automation & Orchestration establishes which work belongs to agents, which belongs to orchestrated processes and what governs both, before any build commences in either.

Applicable where agents and orchestration are being considered as alternatives.

Automation & Orchestration

Work that should have been a deterministic process is the most common item assigned to an agent in error; where this is observed, X-Shift will advise accordingly and propose orchestration instead.

Where this sits

Related

Runs onX.24Enterprise agentic AI platform.
Deployed asProcess Orchestration & AutomationStructured coordination alongside agents.
Deployed asAI-Powered Customer ServiceThe same capability, customer-facing.
Designed byAutomation & OrchestrationAutomation strategy, architecture and governance.

Also relevantSolution Architecture & Systems IntegrationDigital Governance & Operating Model

Technology that enables it

What this is built on

These are platforms that X-Shift builds and supports. Where your estate already covers one of these, the engagement becomes integration and operating model work rather than a platform deployment, and X-Shift will advise accordingly.

Enterprise Agentic AI Platform

AI agents that establish intent and complete the transaction within your systems.

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Customer appointment booking journeyIllustrative
X X.24 Agent ● Online · typically replies instantly Today I want to move my appointment to next week 09:41 Of course. Your appointment is on Sunday at 10:00. I can see Tuesday 09:30 or Wednesday 14:00 are free. 09:41 ✓✓ Tuesday please 09:42 Done. Tuesday 09:30, confirmed. I have sent you the details and a reminder the day before. 09:42 ✓✓ Appointment updated · REF 48192 Reply… AGENT REASONING 2.4s Intent understood Reschedule an existing appointment Customer identified Record matched on the calling number Knowledge applied Rescheduling window and notice policy System action Availability read, slot written back Outcome completed Confirmation and reminder issued CONNECTED SYSTEM Appointment updated · REF 48192 Booking engine · synced 2.4s ago ESCALATION RULE Clinical questions go to a person Routed to the on-duty nurse within 30s Sample data. Handled end to end - no queue, no callback.

X.24 - illustrative interface, not a product screenshot.

Is this the capability you require?

Describe what is not working today, and we will advise whether this is the right answer, a smaller piece of work, or an entirely different problem.

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