Posts

How AI, HR, and operations come together to support frontline workers. Key insights from the HR Happy Hour podcast, featuring Chris Kuntz from Augmentir on skills, training, and connected work.

Augmentir’s VP of Marketing, Chris Kuntz, recently joined Steve Boese on the System of Record podcast from the HR Happy Hour Network to talk about a topic that doesn’t get nearly enough attention: how AI, operations, and HR come together to support the frontline workforce.

hr happy hour podcast with chris kuntz from augmentir

Listen Now

While much of the AI-at-work conversation focuses on desk and knowledge workers, Chris and Steve shifted the spotlight to the 65% of the workforce who work on factory floors, in distribution centers, and out in the field—and how technology can be used to augment and empower, not replace, them.

Below are the key themes and insights from the conversation.

The Missing Link in Industrial Transformation: People

Chris shared his background in industrial and emerging technologies, including his work helping pioneer Industrial IoT at ThingWorx. After years of making machines smarter and more connected, his team recognized a critical gap in Industry 4.0 and 5.0 initiatives:

Humans were the missing piece.

Frontline workers—despite being essential to safety, quality, and productivity—have historically been underserved by technology. Post-pandemic workforce shifts have only intensified the challenge, with:

  • Shorter average tenure
  • Less experience on the job
  • Higher early attrition rates

These trends make traditional six-month onboarding models unsustainable and force organizations to rethink how they support and develop frontline talent.

Augmentir’s Focus: Closing the Skills and Experience Gap

Founded in 2018, Augmentir is an AI-first connected worker platform designed to address what Chris calls the most critical problem in manufacturing today: the combination of labor shortages, skills gaps, and experience gaps.

Rather than treating frontline technology as just digitized paperwork, Augmentir connects workers directly to the digital thread of the business, integrating:

  • Operational systems (ERP, MES, QMS)
  • Learning and training platforms
  • HR systems and skills data

This creates a single interface where workers are active participants in the digital ecosystem—while giving leaders unprecedented visibility into performance, skills, and improvement opportunities.

From Paper Procedures to Continuous Improvement

Chris described how many industrial processes have historically relied on paper instructions or tribal knowledge. By digitizing standard work and connecting workers digitally, organizations can:

  • Capture real-time performance data
  • Identify skill gaps and training needs
  • Reduce safety incidents, rework, and downtime

Augmentir applies machine learning to analyze hundreds of data points—from task duration to error rates—to surface insights such as:

  • Where individuals may need targeted training
  • Where processes or content need improvement
  • How onboarding and training programs are performing

For plant managers and operations leaders, this replaces backward-looking reports with actionable, real-time decision support.

Worker Empowerment, Not Surveillance

A key part of the discussion focused on worker trust and experience. Chris emphasized that successful connected worker initiatives are grounded in empowerment, not micromanagement.

When frontline employees are involved early in the rollout and change management process, the technology is seen as a tool that:

  • Helps them do their jobs safely and correctly
  • Reduces frustration and guesswork
  • Recognizes and rewards positive behaviors

From reporting safety issues to improving efficiency, these signals also provide valuable engagement insights for HR—bridging a gap that has long existed between HR and operations.

Bridging HR and Operations

One of the most compelling themes was the disconnect between HR systems and day-to-day operations. Skills matrices, certifications, and training data often live in HR tools that operations leaders can’t easily access.

By bringing skills and competency data directly into operational workflows, organizations can:

  • Schedule work based on real capabilities
  • Identify reskilling and upskilling needs
  • Measure the effectiveness of training programs

For HR leaders, this turns training ROI from a “black box” into something measurable and defensible.

The Rise of AI Agents on the Frontline

Chris also shared how Augmentir evolved beyond analytics into AI assistants and agents. From its generative AI factory assistant Augie to emerging agentic use cases, the vision includes:

  • Digital lean coaches
  • Training and skills agents
  • Root cause analysis (“5 Whys”) agents
  • Quality agents
  • Safety agents

Importantly, Augmentir has established clear guardrails—such as human-in-the-loop approvals and deterministic logic for safety-critical tasks—to ensure AI supports workers responsibly; these principles are codified in Augmentir’s Six Laws of Agents.

What’s Next: A Human-Centered Future of Work

Looking ahead, Chris highlighted how leading manufacturers like Colgate-Palmolive and Hershey are creating new roles that blend HR and operations, focused on people capability and performance excellence.

The most exciting trend?

Companies are using technology to make frontline work better—faster onboarding, skills development in the flow of work, higher retention, and a stronger sense of purpose for workers.

By truly aligning people, process, and technology, these organizations are redefining what frontline work can look like.

Listen to the Full Conversation

To hear the full discussion on connecting HR, operations, and AI for the frontline workforce, check out the System of Record podcast on the HR Happy Hour Network.

Request a demo to learn more about Augmentir or connect with Chris Kuntz on LinkedIn to continue the conversation.

 

See Augmentir in Action
Get in Touch for a Personalized Demo

In a recent write-up, Tech-Clarity highlighted how Augmentir is addressing one of manufacturing’s most pressing challenges: enabling a smaller, less experienced frontline workforce to perform at a higher level with intelligent systems and AI agents that operate as “digital workers” alongside human workers on the factory floor.

When respected industry analysts take notice, it’s a strong signal that something meaningful is happening in the market. In a recent write-up, Tech-Clarity highlighted how Augmentir is addressing one of manufacturing’s most pressing challenges: enabling a smaller, less experienced frontline workforce to perform at a higher level with intelligent systems and AI agents that operate as “digital workers” alongside human workers on the factory floor.

industrial ai agents with augmentir

The analysis points to Augmentir’s expanding use of machine learning, generative AI, and agentic AI as key differentiators—driving measurable gains in productivity, training effectiveness, and continuous improvement across global operations.

A Single Pane of Glass for the Frontline

At the core of Augmentir’s approach is a simple but powerful concept: give frontline workers exactly what they need, when they need it—no more, no less. Tech-Clarity notes that Augmentir delivers this through a single, contextual interface that integrates with a broad ecosystem of plant and enterprise systems.

augmentir is the single pane of glass for frontline operations

 

The platform supports the full spectrum of connected worker needs, including:

  • Digital work instructions and content authoring
  • Skills matrices and knowledge sharing
  • Contextual, personalized training delivered in the flow of work
  • AI Agents that act as “digital workers” within the connected worker platform – operating alongside human frontline workers
  • Embedded analytics and reporting, including Microsoft Power BI

Because Augmentir’s founders come from an augmented reality background, immersive and experiential learning is a native part of the platform rather than an afterthought.

Closing the Loop with True Insights™

What truly differentiates Augmentir, according to Tech-Clarity, is how it closes the loop between frontline execution and continuous improvement. As work is performed, the platform applies machine-learning–driven AI to analyze operational data across tasks, machines, individuals, and cohorts.

These insights—what Augmentir calls True Insights™—surface where performance breaks down and where improvement efforts will have the greatest impact. This could mean refining instructions, improving process efficiency, enhancing safety, or delivering targeted training exactly where it’s needed.

on the job training ojt with augmentir ai agents

Rather than static reporting, Augmentir continuously learns from real work being done on the shop floor.

Expanding into Agentic AI

In early 2025, Augmentir introduced its Industrial AI Agent Studio, building on its existing GenAI assistant, Augie. Tech-Clarity highlights this as a major step forward, enabling customers to create custom, no-code AI agents tailored to their unique operational needs.

industrial ai agent studio - build custom ai agents for manufacturing

These agents extend Augmentir’s capabilities with autonomous digital workers that support key frontline use cases:

  • Digital Lean Coach: AI agents that fill the role of a Lean coach, helping accelerate lean transformation initiatives.
  • Adaptive Training and Skills Management: AI agents that can act on frontline workers’ skills and training data to support frontline managers by identifying strengths and weaknesses, skills gaps, and recommend training paths.
  • Operations: Operational agents that support more proactive KPI tracking, unparalleled visibility across Operations, Continuous Improvement, and TPM.
  • Safety: AI agents that automatically analyze safety data and activities to provide early warning notifications.
  • Proactive Maintenance Execution: Agents that monitor equipment health and integrate with CMMS to trigger work orders, report issues, or initiate preventive maintenance tasks before failures occur.

Built-In Governance with the 6 Laws of AI Agents

Tech-Clarity also praised Augmentir’s thoughtful approach to AI governance. The platform embeds the company’s 6 Laws of AI Agents, ensuring safety, transparency, and accountability as customers deploy AI at scale:

  1. Transparency in execution
  2. Clear human ownership
  3. AI origin disclosure
  4. Persistent AI disclosure
  5. Human-in-the-loop for impactful actions
  6. No generative AI for life-critical actions

These principles are especially important for industrial environments where trust, safety, and compliance are non-negotiable.

Proven Customer Value at Scale

With customers in more than 70 countries across industries such as food & beverage, pharmaceuticals, chemicals, CPG, and industrial equipment, Augmentir’s impact is already measurable.

Tech-Clarity highlights results reported by customers, including:

  • Up to 31% efficiency gains when standard work is consistently followed
  • 82% reduction in onboarding time, even amid high employee turnover
  • Faster issue resolution and reduced downtime
  • Over 5 million optimized time-and-motion studies conducted on the platform

Notably, Tech-Clarity points out that customers see a 250%–400% performance delta when comparing Augmentir’s AI-driven approach to earlier-generation or non-AI connected worker solutions.

Tech-Clarity’s Take

In their closing assessment, Tech-Clarity emphasizes that Augmentir’s comprehensive use of AI—combined with real operational feedback loops—makes it particularly well suited for organizations looking to accelerate continuous improvement, not just digitize instructions.

They also note that Augmentir’s experienced founding team and growing roster of global enterprise customers speak volumes about market confidence in both the company and its platform.

As Tech-Clarity concludes, organizations evaluating connected frontline worker solutions would be well served to take a close look at Augmentir—and where it’s taking the future of industrial work.

 

See Augmentir in Action
Get in Touch for a Personalized Demo

Discover how AI Factory Agents from Augmentir are transforming manufacturing with real-time insights, automation, and workforce augmentation.

In the evolving landscape of Industry 4.0, popularized by Klaus Schwab, and now Industry 5.0, manufacturers are under increasing pressure to become more agile, resilient, and efficient. Amid labor shortages, shifting customer expectations, and digital disruption, one of the most transformative tools emerging is Factory Agents: smart, context-aware AI agents capable of autonomously performing tasks, surfacing insights, and augmenting human decision-making.

a digital factory agent in manufacturing

What are Factory Agents?

Factory agents are not physical robots, nor are they just software scripts. They’re intelligent, digital workers — powered by AI — that act on behalf of manufacturing teams to interpret data, automate actions, and optimize workflows. They serve as proactive copilots on the shop floor, embedded into the frontline work environment, continuously learning from human activity and contextual factory data to provide real-time support and operational insights.

These agents can assist with:

  • Recommending optimized workflows
  • Identifying skill gaps or training needs for frontline workers
  • Monitoring process performance and flagging anomalies
  • Automatically capturing tribal knowledge
  • Personalizing work instructions based on the worker’s experience and certification level

In short, factory agents bridge the gap between human intelligence and machine efficiency on the shop floor — and Augmentir is leading the charge.

Augmentir’s AI Agent Studio: Manufacturing Intelligence Made Easy

While agentic AI the concept of AI agents has existed in other sectors, Augmentir is the first to bring a no-code Industrial AI Agent Studio purpose-built for manufacturing. This unique platform allows operations leaders, supervisors, and even non-technical users to create and deploy custom AI agents tailored to specific needs across:

  • Workforce onboarding and training
  • Maintenance and repair operations (MRO)
  • Quality assurance
  • Safety procedures
  • Performance monitoring

industrial ai agent studio - build custom ai agents with augmentir

These agents are powered by proprietary algorithms and generative AI that continuously learn from your workforce and operations data. That means over time, the agents get smarter — making increasingly precise recommendations, automating more tasks, and reducing variability across the shop floor.

The result? An adaptive, intelligent frontline that can respond dynamically to production demands, labor variability, and skill shortages.

Meet Augie: The Face of Next-Gen Industrial AI

At the core of Augmentir’s AI capabilities is Augie — an Industrial Generative AI assistant built specifically for frontline manufacturing environments. Augie acts like a real-time guide and operational partner for shop floor workers, supervisors, and even plant managers.

Here’s what makes Augie different:

  • Context-aware assistance: Augie understands the unique context of your operation — such as a specific piece of equipment, a shift schedule, or a worker’s skill level — to tailor guidance appropriately.
  • Conversational interface: Workers can interact with Augie naturally through chat, enabling real-time Q&A, issue resolution, or step-by-step guidance.
  • Continuous learning: As workers interact with Augie, it learns and improves, capturing undocumented knowledge and institutionalizing best practices across the organization.

industrial ai agent studio operations analyst

Rather than replacing workers, Augie amplifies their capabilities, making everyone on the shop floor more confident, capable, and productive.

Real-World Impact: Augmentir in Action

Companies using Augmentir have reported measurable improvements across multiple KPIs:

  • 20–40% reduction in training time by personalizing learning to individual skill levels
  • 30% improvement in first-time quality through smarter digital work instructions
  • 25% gain in workforce productivity due to real-time guidance and fewer delays
  • Stronger worker retention through empowered learning and growth pathways

In a time when manufacturers are grappling with a skills gap, labor shortages, and increased demand for agility, these outcomes are game-changers.

Why Factory Agents Are Defining the Future of Industrial Work

The traditional shop floor has been defined by rigid systems and static processes. But today’s manufacturers need more flexibility — they need systems that adapt to shifting demand, dynamic labor pools, and constant process change.

AI factory agents offer this adaptability and with Augmentir’s Industrial AI platform, manufacturers can unlock this potential without a massive overhaul or technical burden.

Factory agents represent a new class of industrial tools — intelligent, autonomous, and human-centric. As the first platform to bring this vision to life, Augmentir is not just building tools, but reshaping how manufacturing work gets done.

With Augie and the AI Agent Studio, Augmentir is helping manufacturers step into a new era of operational excellence — where the frontline is not just automated, but truly augmented.

Learn more about how Augmentir’s AI shop floor agents can modernize your operations – contact us today for a live demo.

 

See Augmentir in Action
Get in Touch for a Personalized Demo

Recap of Chris Kuntz’s session at MD&M East 2025 on how AI copilots and AI agents are transforming manufacturing—from enhancing workforce capabilities to enabling autonomous operations.

At this year’s MD&M East, formerly IME East 2025, Augmentir took center stage as Chris Kuntz, VP of Strategic Operations, delivered a powerful presentation on the transformative role of AI Copilots and AI Agents in manufacturing.

Chris Kuntz, Vice President of Strategic Operations at Augmentir, speaking on AI Copilots and AI Agents in Manufacturing at MD&M East 2025

Read below for a brief recap of the presentation, as well as a video recording of the presentation.

Addressing the Workforce Crisis with AI

Chris opened with a sobering reality: even if every skilled worker in the U.S. were employed, 35% more manufacturing jobs would remain unfilled. Citing a $1 trillion annual opportunity cost by 2030 (Deloitte), Chris emphasized that traditional workforce strategies aren’t enough—and the time for intelligent automation and workforce augmentation is now.

workforce crisis in manufacturing and the opportunity for ai agents and ai copilots

Key Highlights from the Presentation

The Rise of AI Copilots and AI Agents

Chris introduced AI Copilots as conversational tools powered by LLMs, providing contextual, real-time support to workers. AI Agents, on the other hand, are autonomous systems that execute complex tasks independently—reducing friction, downtime, and manual inefficiencies.

The Augmented Connected Worker

At the heart of the talk was Augmentir’s Connected Worker technology—a framework that brings together:

  • AI-powered guidance and support to help frontline workers perform tasks more efficiently, safely, and accurately.
  • Real-time data capture and insights that drive continuous improvement across operations, training, and workforce performance.
  • A unified digital platform that connects people, processes, and systems to enable scalable workforce transformation in manufacturing.

6 Game-Changing Use Cases

Chris walked attendees through six real-world use cases—showing how Augmentir and Augie are delivering measurable outcomes for manufacturers:

  • Content Assistant – 76% faster digitization of SOPs and training docs
  • Work & Training Assistant – 82% reduced onboarding time
  • Image Comparison – Improved inspection accuracy, reduced rework
  • Skills & Training AI Agent – On-demand learning and certification
  • Operations Agent – Real-time troubleshooting support
  • Corporate Knowledge Graphs – Smarter access to institutional knowledge

Case studies from leading packaging and beverage companies added real-world credibility, demonstrating how organizations are scaling faster while minimizing downtime and safety incidents.

Video Recording

 

Full Transcript

My name is Chris Kuntz. I’m with an AI company called Augmentir, and we provide connected worker software for frontline workers in manufacturing. Today what I’ll be talking about is artificial intelligence, which in many ways has taken over the media, and become a major part of our lives, but I want to talk about it in the context of manufacturing and specifically talk about generative AI assistants and AI agents that can be used in manufacturing to help guide and support today’s frontline workers.

So just a quick 30 seconds on Augmentir and who we are as a company. We’re a relatively young company, founded in 2018, but we have a pretty deep history in innovative software and manufacturing, dating back to the late 1980s. The founders of Augmentir were the same industry innovators that founded Wonderware in 1987, which revolutionized HMI software in factories. Wonderware went public and is now part of AVEVA/Schneider Electric. We were the founders of Lighthammer, which is now part of SAP’s MII offering. And we were the founders of ThingWorx, which is now part of PTC and revolutionized the Industrial Internet of Things space. And when we left PTC, the team got back together again and we wanted to focus on tackling what we considered to be the next big problem in manufacturing at the time, which was the human worker.

If you think about AI and how it’s been, automation and how AI has optimized production lines, really the last mile for driving efficiency in manufacturing is the human worker. And even more apparent over the past five years since the pandemic, the labor shortage, the skilled labor shortage has created dramatic impacts on product quality, product efficiency, and overall throughput in manufacturing. And so our goal at Augmentir was to tackle that.

So let’s start this conversation by talking about AI and the history of AI in manufacturing. And it dates back to the 1960s. AI has been used in automation in manufacturing for decades now. It’s been used to drive incredible levels of efficiency. It’s been used in machine vision systems for quality improvements. And you see that you, when you walk around, manufacturing trade shows like this, it’s been used in warehouse in warehousing automation, and more recently, it’s been used in the industrial internet of things, digitally connecting equipment and using AI to analyze the data that is coming off of that equipment to drive greater efficiencies in production, production efficiency in manufacturing. But a common theme across all of this is up to this point, AI has been used to replace the human worker or to optimize manual labor or manual efforts that humans were doing in factories, previously. AI has a unique opportunity, specifically around generative AI co-pilots, if you think ChatGPT or AI agents, is to augment the human worker, not replace them.

And so the question we asked ourselves at Augmentir, when we started, was, can AI do the same for humans? Can AI drive efficiency for the humans that are still on the shop floor in manufacturing, quality, engineering, and maintenance roles and in equipment operation. More importantly, in maintenance, can AI be used to optimize the work that they’re doing? And why now?Here are some statistics from a report that LNS Research, an analyst firm based out of Boston ran last year on the future of industrial work. Pretty fascinating statistics. When they look at the average tenure rate in manufacturing, 2019 compared to the end of last year. So from 20 years to three years, the average time and position went down from seven years to nine months, and the average three-month retention rate, the rate at which people stay in after the first three months, from 90% down to 50%. So the problem you have in manufacturing today is yes, there’s a labor shortage, yes, it is difficult to find skilled labor, but because humans are required in manufacturing, what organizations are doing is hiring less skilled workers. And now you have a problem that’s really twofold on the shop floor. You have less experienced workers that also have less experience or less skilled workers, also have less experience. And that results in safety issues, quality issues, product recalls, downtime, everything possible that you can imagine that relates to human error in or on a factory floor.

In this survey, from LNS, the respondents, 92% of them said they were looking at technology as a way to offset that skilled labor gap. Now, it’s not the only solution, certainly there are better hiring strategies, better training strategies, but certainly looking at technology as a big piece of offsetting that labor crisis. Just another statistic here from a study in Deloitte, even if every skilled worker, and this is just in America, even if every skilled worker was employed, there would still be a 35% gap in unfilled job openings in manufacturing. That’s how bad it is. And so Deloitte predicts by 2030, that it’s a $1 trillion problem in the US alone. And I think they forecasted $3 trillion globally, a problem that exists for production output and manufacturing.

So that brings us to what we’re talking about here today, AI agents and co-pilots. Everyone here has used ChatGPT or Gemini or Perplexity or whatever, chatbot you want to use today, fantastic results and fantastic opportunities when you think about consumer AI, but what I want to do is talk about the context of AI assistance, as well as agents, which there’s some blurring of the line there, but we’ll talk about that, and their applicability in industrial operations and why it’s quite a bit different from consumer AI.

So what is an AI co-pilot? Best example is ChatGPT, right? We’ve all used it. Natural language interface, the ability to use what they call a large language model, LLM, for those of you that might not be as technical, which has the ability for that agent or that assistant to understand vast amounts of data and it provides context assistance to users. On the flip side, what is an agent? An agent is an AI bot that acts more autonomously. They can operate based on a prompt like you would have with ChatGPT, but they don’t have to. So they can actually take autonomous action based on instructions you give it. Now, when you think about it, I’m going to use ChatGPT as an example today because I think we’ve all probably used it or used something similar. They’re starting to blur the lines a little bit with their, I think they’re calling it the ChatGPT operator, so that’s starting to blur the lines between autonomous and strictly prompt-based AI. But the idea is the same in the context of today, what we’re talking about in terms of an AI co-pilot or an assistant that is a prompt-based bot that that a user might be using. And from an agent standpoint, it is something that can act more autonomously. And a prerequisite to all this, when you think about manufacturing and you think about frontline workers, whether they are working in safety, quality, equipment and machine maintenance and repair or equipment operation, a prerequisite to all this is the ability to have a connected worker.

And by connected worker, what we like to talk about at Augmentir is a worker that is not only connected with a digital or a mobile tool, like a phone, a tablet, a wearable technology, a wearable augmented reality-based headset, for example, but also digitally connected into the business. So using that interface to not just connect them physically with a device, but connect them into HR systems, learning management systems, ERP systems, quality systems, and safety systems, systems that they use every day. But now that they’re connected, they can become human sensors on the shop floor. And there’s a vast amount of data that we can then capitalize on here, and AI can then act on.

So what I want to do now is talk about consumer AI, again, the example of ChatGPT compared to industrial AI. And in the case of today, I’m going to give some examples of manufacturing companies that are actually using this technology today. But when you think about industrial operations, you have to think quite a bit differently than how we might use Gemini or ChatGPT today. So I’m just going to walk through an example here. You have a frontline worker, an operator on a manufacturing floor, and their job every day is to operate the mixer. Okay? Part of their job is also to periodically do a clean, inspect, and lubricate on that piece of equipment, so it doesn’t go down or so that they can prevent failures from happening. So that’s a CIL. So now go back to the context that I started this conversation with. Let’s say you have a less experienced worker, maybe they are a novice worker.

 

See Augmentir in Action
Get in Touch for a Personalized Demo

Augmentir’s 6 Laws of AI Agents define the guardrails for safe, ethical, and accountable agents in manufacturing and industrial environments.

As AI agents become more deeply embedded in business operations, they carry tremendous potential—but also significant responsibility. At Augmentir, we believe that trust, accountability, and safety must form the foundation of every AI deployment. That’s why we developed our 6 Laws of AI Agents: guiding principles that ensure AI systems operate transparently, responsibly, and safely in real-world environments. These laws are designed not only to safeguard organizations and individuals, but also to help businesses realize the true value of AI without compromising integrity or safety.

6 laws of ai agents in manufacturing - by augmentir

The 6 Laws of AI Agents:

  1. Transparency in Execution
  2. Clear Ownership
  3. AI Origin Disclosure
  4. Persistent AI Disclosure
  5. Human-in-the-Loop for Impactful Actions
  6. No GenAI for Life-Critical Actions

 

1. Transparency in Execution

All agent activities must be observable. This includes what instructions were given, which tools were used, and what outcomes were produced. Transparency ensures traceability, making it clear how and why decisions were made.

Summary: AI must never be a “black box.” Clear visibility builds trust and accountability.

ai agents - transparency in execution

2. Clear Ownership

Every AI agent must have a clearly defined human or organizational owner responsible for its decisions and actions. This ownership must be explicitly documented to prevent ambiguity and ensure accountability at all times.

Summary: AI is powerful, but responsibility always rests with people, not machines.

ai agents - clear ownership

3. AI Origin Disclosure

Whenever an agent provides an answer, recommendation, or decision, it must clearly state that it was generated by AI—and acknowledge that AI can make mistakes. This sets proper expectations and reinforces responsible use.

Summary: Clear disclosure prevents overreliance on AI and keeps human judgment central.

4. Persistent AI Disclosure

If an agent’s AI-generated recommendation or content is shared outside its native system (e.g., posted in Microsoft Teams or another platform), the AI origin and disclaimer must remain attached. Transparency should travel with the content wherever the information is shared.

Summary: AI-origin labels must stay attached, ensuring clarity across platforms.

5. Human-in-the-Loop for Impactful Actions

Any action that creates, modifies, or deletes a data item that could affect operational outcomes must require human review and approval before completion. For example, a safety report notes oil on a walkway. If an agent attempts to close the issue without cleanup, a human must approve before closure.

Summary: AI can recommend actions, but humans must approve decisions with real-world consequences.

ai agents - human in the loop for impactful actions

6. No GenAI for Life-Critical Actions

Generative AI must not be used to perform actions that could physically harm a person, control equipment, or alter settings that impact human safety. These actions require deterministic, verifiable code and strict safety protocols.

Summary: AI can assist, but life-critical actions must always remain human-controlled.

Governing the Future of AI Responsibly

The 6 Laws of AI Agents provide a blueprint for deploying AI responsibly in the enterprise. By emphasizing transparency, ownership, disclosure, human oversight, and safety, organizations can embrace AI innovation without compromising trust.

At Augmentir, we believe AI should augment—not replace—human intelligence, and these laws ensure that principle is upheld.

 

See Augmentir in Action
Get in Touch for a Personalized Demo