Agentic AI

What Agentic AI actually changes in practice

Agentic AI turns static AI models into goal-driven assistants that can plan, decide, and act across digital and physical systems with human oversight.

Instead of offering a single answer to a single question, an Agentic AI system can receive an objective, break it down into steps, call tools and APIs, and loop until it reaches a satisfactory outcome within defined boundaries. In industrial contexts, this means moving from blinking alarms and manual log checks to active, continuous troubleshooting support.

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You can think of an agentic AI system as a digital colleague that reads instructions, chooses relevant tools, performs work, and reports back with results and explanations. It operates within the policies and guardrails you define and remains auditable, so its actions can be traced and reviewed.

From single answers to autonomous workflows

Process automation depth

End-to-end

User effort per task

Minutes → seconds

Mode of interaction

Goal → outcome

Under explicit human control

Connects to existing tools and data

Explains what it did and why

This shift is tangible for business teams. Processes that used to require manual follow-up, multi-system lookups, or coordination across departments can be orchestrated end-to-end by an AI agent, while people stay focused on supervision, exceptions, and high-value decisions. In troubleshooting scenarios, the agent does not just raise an alarm; it investigates, tests hypotheses, and proposes or executes safe corrective actions.

Business impact and benefits

Agentic AI creates value by combining language understanding, decision logic, and tool usage into one orchestrated capability that can run continuously, not only on demand.

Operational efficiency and throughput

Workflows that used to involve multiple employees and systems can be handled by a swarm of AI agents that logs in, gathers information, performs calculations, drafts outputs, and asks for confirmation only where it is needed. In operations and maintenance, this can reduce the time between the first alarm and a concrete corrective step from tens of minutes to seconds or a few minutes.

Typical effects include shorter turnaround times for complex requests, higher throughput per employee, and smoother handovers between teams because the agent retains context across steps and across incidents.

Quality, consistency, and auditability

Because agentic AI follows explicit policies and step-by-step reasoning patterns, you gain more consistent handling of cases, automatic documentation of actions, and easier compliance reporting. Every decision, tool call, and parameter change can be written back into a knowledge base.

The system can be configured to ask for human approval at key points, enforce mandatory checks, and provide a trace of how key decisions were reached. Over time, this creates a reusable library of resolved cases instead of scattered troubleshooting notes.

Employee and customer experience

Internally, employees receive proactive assistance. The agent prepares drafts, suggests next steps, surfaces relevant context before meetings or decisions, and handles routine incidents without waiting in a support queue. Externally, customers reach resolutions faster and receive more tailored responses, because the agent can access their history and preferences in real time.

Strategic flexibility

Once an agentic AI layer is in place, new processes can be digitised and orchestrated faster. Changing a policy or workflow often means adjusting the agent’s instructions rather than rebuilding traditional software. The same platform can support troubleshooting, reporting, optimisation, and other use cases without separate point solutions.

Reduced downtime and scrap in production environments

Higher automation depth without rebuilding core systems

Better use of existing data, APIs, and tools

Transparent, reviewable AI behaviour and decisions

How agentic AI works in practice

At a high level, an agentic AI system receives a goal, plans a path, calls tools and data sources, and iterates until it achieves an acceptable result under predefined rules and supervision. In troubleshooting scenarios, the same mechanism is triggered by events such as alarms, quality deviations, or performance drops instead of a user question.

Conceptual schema

Business goal or event

“Resolve this case”
or “Investigate this deviation”

➞

Agentic AI orchestrator

Understands intent, plans steps, chooses tools

➞

Tools, data, and systems

APIs, databases, control systems, knowledge bases

⬆

Actions and outcomes

Commands, reports, messages, configuration changes

⬆

Human oversight

Approvals, corrections, policy updates

1. Understand the goal or trigger: The agent reads a human request or a system event, interprets the intent, and translates it into a clear internal objective, such as resolving a specific alarm or restoring performance.

2. Plan a sequence of steps: Based on predefined policies and its knowledge, the agent decomposes the objective into actions such as gathering data, checking rules, running diagnostics, or preparing a recommendation.

3. Call tools and systems: The agent uses connectors (APIs, RPA, integrations, control system interfaces) to query databases, read telemetry, call external services, update records, or execute predefined commands. Each step is logged.

4. Evaluate and iterate: The agent checks whether the intermediate result satisfies the goal and applicable constraints. If not, it adjusts its plan, explores alternative hypotheses, or requests additional diagnostics.

5. Involve humans when needed: For sensitive actions or ambiguous cases, the agent requests approval, presents options, and incorporates feedback into its next decisions. If a problem cannot be safely resolved, it hands over the case with a clear explanation instead of guessing.

Key use cases in industry and healthcare

Agentic AI is particularly suitable where processes are multi-step, knowledge-intensive, and require coordination between systems and people.

Industrial operations and maintenance

An agent can continuously monitor sensor data, maintenance logs, and production schedules to propose optimal maintenance plans, create work orders, and coordinate resources while staying within safety constraints.

It can also assist technicians on site by retrieving procedures, suggesting diagnostics steps, and updating documentation as tasks are completed.

Supply chain and logistics

Agentic AI can track shipments, inventory, and demand signals across multiple systems, then proactively adjust orders, re-route deliveries, or suggest alternative suppliers when disruptions occur.

The agent can notify stakeholders with concise status summaries and recommended decisions instead of raw data.

Quality, compliance, and reporting

An AI agent can collect evidence from production systems, compare it with regulatory requirements, flag non-conformities, and prepare draft reports for quality managers.

Clinical workflow assistance

In healthcare settings, an agent can pre-review patient information, summarise histories, highlight missing data, and prepare structured notes for clinicians, always subject to medical review.

It can also help route cases, schedule follow-up, and ensure that required documentation is complete before procedures.

Care coordination and patient support

Agentic AI can support chronic-care programmes by monitoring patient-reported data, checking adherence to care plans, sending reminders, and escalating to human staff when certain thresholds are reached.

Medical administration and coding

An agent can assist with coding, billing, and insurance pre-authorisations by reading clinical notes, mapping them to codes, validating entries, and preparing submissions for human verification, reducing administrative burden on clinical staff.

Multi-step workflows across systems

High documentation and reporting requirements

Need for timely alerts and escalation

Human oversight preserved at critical decisions

Case example: agentic troubleshooting on a bottling line

A concrete application of the Agentic Troubleshooter concept on a bottle assembly line, designed to work with existing PLCs, sensors, and knowledge sources rather than replacing them.

On a bottle assembly line, production stops or slowdowns often start with a single alarm, a quality deviation, or a subtle change in throughput. Traditionally, a technician would need to walk to the line, inspect the HMI, check logs, look up manuals, try a few interventions, and only then discover the root cause.

In this deployment, an agentic troubleshooting system is connected to the line’s control and monitoring environment. When a relevant trigger occurs, such as a PLC alarm, a drop in bottles per minute, or a repeated quality limit breach, the system automatically creates a case with the context of what happened and when.

From there, the AI agents read the case, inspect current telemetry, and search through the customer’s own knowledge sources, including historical incidents, service reports, machine manuals, configuration documents, and troubleshooting guides. Where necessary, the agent requests additional diagnostics from the machine, for example, targeted sensor reads or a controlled test routine that has been approved in advance.

Bottle assembly line – at a glance

The system acts as an autonomous co-pilot for the line. It investigates faults, checks what is safe to do, and either performs corrective actions or hands over to a technician with a structured explanation.

Primary goal

Less downtime

Scope

Alarms, quality, speed

Integration

PLC, MES/SCADA, manuals

Trigger: Alarm, throughput drop, or quality deviation on the bottling line.
‍Investigation: AI reads logs, telemetry, and knowledge, and may request targeted diagnostics.
‍Action: Safe adjustments or predefined command sequences, or structured escalation.
‍Learning: Every incident and its resolution are written back into a searchable knowledge base.

Designed around existing capabilities

The solution does not invent new machine behaviour. It works strictly within a customer-defined list of safe commands and test procedures for the bottle assembly line, such as controlled resets, recalibration sequences, or parameter adjustments within permitted limits. Actions that require physical intervention remain assigned to human technicians, but with clearer guidance on likely causes and recommended steps.

From one line to a reusable architecture

Although this example focuses on a single bottling line, the underlying architecture is generic. The same pattern can be adapted to other lines, machines, and plants by mapping which problems matter, which information is available, how the system should be triggered, and what the machine is allowed to do. This keeps the platform reusable while each deployment reflects the specific environment and risk profile.

Questions and Answers

Typical questions stakeholders ask when evaluating agentic AI for their organisation.

How is agentic AI different from a traditional chatbot?

A traditional chatbot usually responds within a single exchange and does not perform multi-step actions on your systems. An agentic AI system receives an objective, plans several steps, calls tools and APIs, and loops until it reaches a defined outcome. It is closer to a digital operator than to a conversational FAQ.

Do we lose control if the AI can act autonomously?

Autonomy is limited by the guardrails you define. In practice, you can restrict which tools the agent may call, which data it may access, and which actions require mandatory human approval. All actions can be logged so that behaviour remains reviewable and auditable.

Where should we start with agentic AI?

A common starting point is a well-defined workflow that is painful to run manually, involves multiple systems, and has clear rules and quality criteria. You can first deploy an agent in “copilot” mode where it prepares drafts and recommendations, then gradually allow it to execute more steps automatically once you trust its behaviour.

What are the main risks and how can they be mitigated?

Key risks include incorrect actions due to misunderstood instructions, inappropriate tool usage, or data quality issues. Mitigation typically relies on careful scoping of what the agent is allowed to do, explicit policies, multi-step validation, human-in-the-loop review for sensitive tasks, and continuous monitoring of behaviour and outcomes.

How does this fit with our existing IT and OT landscape?

Agentic AI usually sits as an orchestration layer on top of your existing applications and data sources. It does not replace core systems; instead, it uses them through APIs or integration adapters, including PLCs, SCADA, MES, and enterprise IT. This means you can start with a narrow scope, demonstrate value, and then extend coverage to additional processes over time.

In summary, agentic AI is a way to let AI systems pursue well-defined goals within your environment, under human-set rules, by coordinating tools, data, and actions across the organisation and capturing what is learned from each case.

Contact and next steps

If you would like to explore how agentic AI and autonomous troubleshooting could apply to your machines, production lines, or technical systems, you can request a conversation using the form.

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