Agentic AI in Manufacturing: 11 Proven Insights for Smarter Factories

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The Industrial Shift: From Automation to Intelligence

For decades, manufacturers have focused on efficiency – optimizing for speed, cost, and consistency.
But efficiency alone is no longer enough.

Factories are now expected to be adaptive, data-driven, and resilient in the face of supply chain shocks and shifting demand.
To stay ahead, manufacturers need systems that can sense, decide, and act – not just execute.

Learn more from the UiPath Manufacturing Summit insights , where enterprise leaders discussed real-world use cases of AI and orchestration.

That’s where agentic AI in manufacturing comes in.

Agentic AI combines automation with intelligence, giving machines and processes the ability to understand context, make decisions, and collaborate with humans.
It’s not about replacing people – it’s about orchestrating humans, robots, and AI agents to work together in harmony.

This article explores how agentic AI in manufacturing is reshaping operations, blending human expertise with automation to unlock new levels of productivity and resilience.

Defining the New Manufacturing Stack

At UiPath Fusion 2025, leaders across manufacturing shared a common message:
automation has matured, and the next leap is orchestration powered by agentic AI.

This evolution transforms the traditional manufacturing stack into three distinct, complementary layers:

Robots handle deterministic, rule-based actions.
Agents make context-based decisions and adjust dynamically.
Humans provide oversight, governance, and innovation.

In other words, robots do, agents think, and people lead.

This orchestration doesn’t just increase productivity – it creates a self-improving industrial ecosystem that learns continuously.

Paul Boris’ Framework: Vision, Value, and Tactics

Paul Boris, UiPath’s Global VP for Manufacturing, summarized this transformation through a simple but powerful framework:

Vision: Define what truly matters to the organization – customer impact, sustainability, quality, or speed.
Value: Quantify where automation and AI create measurable outcomes.
Tactics: Deploy small, high-impact projects to validate assumptions and scale what works.

As Paul Boris put it:

“If it takes you 18 months to define your transformation roadmap, you’re already behind. The technology and your business will both have changed by then.”

Manufacturers can’t afford to wait for massive multi-year transformations. They need agile orchestration – quick wins that deliver measurable results while keeping the long-term vision intact.

Case Study: Love’s Travel Stops – From Regression to Reinvention

A standout example at UiPath’s Manufacturing Summit came from Love’s Travel Stops, a company operating one of the largest networks of fueling and convenience locations in North America.

Their challenge was common in manufacturing and retail operations alike:
aging test systems, slow validation cycles, and manual regression testing that consumed hundreds of hours.

The Challenge

Manual regression testing across thousands of POS systems and hardware components.
120+ man-hours per test cycle.
Fragmented processes and limited scalability.

The Solution

Modernized their testing suite using UiPath Test Suite and cloud orchestration.
Partnered with UiPath Professional Services and Royalo to build modular, data-driven workflows.
Shifted from linear testing to a behavior-driven approach, powered by reusable automation components.

Love’s story proves that agentic AI in manufacturing is not theoretical – it’s practical, measurable, and ready for scale.

The Results

88% of eligible test cases automated.
7,500+ test executions completed this year alone.
Reduced test cycles from 120 hours to 25 hours.
Collapsed 1,100 individual test cases into just 7 modular flows.

Love’s didn’t just accelerate testing – they created a scalable automation foundation that can now integrate agentic decision-making for future scenarios.

As their automation team described it:

“Automation didn’t just make us faster – it made us more consistent. Every test runs the same way, every time.”

Aggranda has worked with global manufacturers to achieve similar results through intelligent automation. Explore our AI automation case studies for real-world examples.

Why Agentic AI in Manufacturing Matters for the Industry

Agentic AI introduces decision-making, adaptability, and context awareness to the automation layer.
In manufacturing, that translates into:

Predictive quality control – agents detect anomalies and flag potential issues before defects occur.
Dynamic scheduling – AI adjusts production based on real-time material and labor availability.
Intelligent maintenance – robots act when agents predict failures, not after breakdowns.
Continuous improvement – processes learn from every iteration, optimizing automatically.

The result is not just higher efficiency – it’s resilience.
Factories can now adapt to change instantly, without waiting for human intervention or reprogramming.

The rise of agentic AI in manufacturing marks a shift from static automation to adaptive systems that think, decide, and learn in context.

Breaking the IT/OT Divide

One of the biggest obstacles in manufacturing transformation is the historical divide between IT and OT (Operational Technology).
But at UiPath Fusion, experts reframed the problem:

“It’s not IT versus OT – it’s Engineering, Manufacturing, and Field Operations running in silos.”

Agentic AI and orchestration close that gap by creating a shared intelligence layer.
It enables seamless data exchange and action coordination across ERP, MES, SCADA, and quality systems.

This means:

Engineering sees real-time data from the factory floor.
Manufacturing instantly understands upstream and downstream impacts.
Field operations get predictive alerts and prescriptive guidance.

In essence, agentic AI connects the factory like a living organism – each part aware of and responsive to the others.

Continuous Improvement 2.0: The Rise of Digital Kaizen

Traditional Kaizen relies on manual review cycles: gather teams, analyze metrics, plan actions, repeat.
Agentic automation digitizes that philosophy.

Imagine a “Digital Kaizen” loop where:

Sensors and systems identify bottlenecks automatically.
AI agents propose improvement actions.
Robots implement them safely and instantly.
Human leaders validate and monitor outcomes.

This creates a culture of real-time continuous improvement, not just quarterly reviews.

As UiPath’s manufacturing leadership put it:

“We no longer look at value streams once a year. We watch them evolve live, every second.”

Conagra Brands: Stepping Into the Agentic Future

Another manufacturing pioneer showcased at Fusion 2025 was Conagra Brands, a leader in food production.
Their team implemented a hybrid agentic automation process in treasury operations – combining RPA bots with intelligent agents that make contextual decisions.

It’s a glimpse of what’s next:

Finance and operations align through shared AI orchestration.
Decision agents evaluate risks and execute transactions autonomously.
The model is extendable to production, supply chain, and quality management.

This step proves that agentic AI in manufacturing isn’t a distant future – it’s already here, starting with internal processes and expanding to operational ones.

How to Begin Your Agentic AI in Manufacturing Journey

For manufacturers ready to move beyond pilot projects, here’s a roadmap to adoption:

Identify high-value, low-risk areas
- Start with processes that are repetitive, measurable, and data-rich.
Connect your data silos
- Integrate systems (ERP, MES, SCADA) into one orchestrated view.
Build small, test fast
- Launch pilot automations and measure ROI before scaling.
Leverage orchestration tools like UiPath Maestro
- Manage agents, bots, and humans within one governed control plane.
Empower your workforce
- Train teams to design, test, and scale automations safely.
Scale iteratively
- Replicate success across sites and functions, ensuring governance and visibility.

Aggranda’s Perspective

At Aggranda, we’ve seen this transformation unfold across industries – from discrete manufacturing to energy, logistics, and industrial services.
Our work with global enterprises proves that agentic AI in manufacturing delivers real results when built on a solid automation foundation.

We help companies:

Integrate automation with existing systems (ERP, MES, QA platforms).
Design and deploy orchestrated workflows that blend human oversight with AI-driven execution.
Build scalable, secure foundations for agentic automation and intelligent decision-making.

Explore how it looks in practice in our Aggranda manufacturing case studies or discover how we’ve helped OMV and other industrial leaders build automation that scales across operations.

The Future of Agentic AI in Manufacturing: Orchestrated Intelligence

Manufacturing is entering a new chapter – one driven by collaboration between humans, robots, and AI agents.

The factory of the future won’t just produce goods faster.
It will think, adapt, and improve itself – continuously.

Agentic AI won’t replace the workforce; it will amplify it.
It gives engineers, planners, and operators the tools to innovate at machine speed, while keeping human judgment at the center.

As the sessions at UiPath Fusion 2025 made clear:

“The winners won’t be those with the most automation – but those who orchestrate it best.”

The companies that will lead this decade are those that turn agentic AI in manufacturing into orchestrated intelligence – where people lead, agents think, and robots do.

Manufacturing is entering a new era – one where agility, orchestration, and intelligence must coexist. Agentic AI doesn’t replace human skill; it amplifies it. The most advanced factories are already proving that when automation meets autonomy, the outcome is not just efficiency – it’s reinvention. For leaders ready to take the next step, now is the time to align your automation strategy with an agentic vision that scales.

For more on how automation transforms large-scale industries, read our automation success stories with UiPath.