Archive 15.10.2025

We’re rolling out significant updates to Veo that give people even more creative control.

From sorting objects in a warehouse to navigating furniture while vacuuming, robots today use sensors, software control systems, and moving parts to perform tasks. The harder the task or more complex the environment, the more cumbersome and expensive the electronic components.

The global demand for temperature-controlled logistics continues to grow, and with it comes an increase in operational complexity. These facilities require substantial investment, precise temperature regulation, and the capacity to adapt to market shifts quickly.

The 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025) will be held from 19-25 October in Hangzhou, China. The programme includes plenary and keynote talks, workshops, tutorials, forums, competitions, and a debate.

Plenary talks

There are three plenary talks on the programme this year, with one per day on Tuesday 21, Wednesday 22, and Thursday 23 October.

• Marco Hutter – The New Era of Mobility: Humanoids and Quadrupeds Enter the Real World
• Hyoun Jin Kim – Autonomous Aerial Manipulation: Toward Physically Intelligent Robots in Flight
• Song-Chun Zhu – TongBrain: Bridging Physical Robots and AGI Agents

Keynote talks

The keynotes this year fall under eleven umbrella topics:

• Rehabilitation & Physically Assistive Systems
  • Patrick Wensing – From Controlled Tests to Open Worlds: Advancing Legged Robots and Lower-Limb Prostheses
  • Hao Su – AI-Powered Wearable and Surgical Robots for Human Augmentation
  • Lorenzo Masia – Wearable Robots and AI for Rehabilitation and Human Augmentation
  • Shingo Shimoda – Science of Awareness: Toward a New Paradigm for Brain-Generated Disorders
• Bio-inspired Robotics
  • Kevin Chen – Agile and robust micro-aerial-robots driven by soft artificial muscles
  • Josie Hughes – Bioinspired Robots: Building Embodied Intelligence
  • Jee-Hwan Ryu – Soft Growing Robots: From Disaster Response to Colonoscopy
  • Lei Ren – Layagrity robotics: inspiration from the human musculoskeletal system
• Soft Robotics
  • Bram Vanderborght – Self healing materials for sustainable soft robots”
  • Cecilia Laschi – From AI Scaling to Embodied Control: Toward Energy-Frugal Soft Robotics
  • Kyu-Jin Cho – Soft Wearable Robots: Navigating the Challenges of Building Technology for the Human Body
  • Li Wen – Multimodal Soft Robots: Elevating Interaction in Complex and Diverse Environments
• Al and Robot Learning
  • Fei Miao – From Uncertainty to Action: Robust and Safe Multi-Agent Reinforcement Learning for Embodied AI
  • Xifeng Yan – Adaptive Inference in Transformers
  • Long Cheng – Learning from Demonstrations by the Dynamical System Approach
  • Karinne Ramírez-Amaro – Transparent Robot Decision-Making with Interpretable & Explainable Methods
• Perception and Sensors
  • Davide Scaramuzza – Low-latency Robotics with Event Cameras
  • Kris Dorsey – Sensor design for soft robotic proprioception
  • Perla Maiolino – Shaping Intelligence: Soft Bodies, Sensors, and Experience
  • Roberto Calandra – Digitizing Touch and its Importance in Robotics
• Human Robot Interaction
  • Javier Alonso-Mora – Multi-Agent Autonomy: from Interaction-Aware Navigation to Coordinated Mobile Manipulation
  • Jing Xiao – Robotic Manipulation in Unknown and Uncertain Environments
  • Dongheui Lee – From Passive Learner to Pro-Active and Inter-Active Learner with Reasoning Capabilities
  • Ya-Jun Pan – Intelligent Adaptive Robot Interacting with Unknown Environment and Human
• Embodied Intelligence
  • Fumiya Iida – Informatizing Soft Robots for Super Embodied Intelligence
  • Nidhi Seethapathi – Predictive Principles of Locomotion
  • Cewu Lu – Digital Gene: An Analytical Universal Embodied Manipulation Ideology
  • Long Cheng – Learning from Demonstrations by the Dynamical System Approach
• Medical Robots
  • Kenji Suzuki – Small-data Deep Learning for AI Doctor and Smart Medical Imaging
  • Li Zhang – Magnetic Microrobots for Translational Biomedicine: From Individual and Modular Designs to Microswarms
  • Kanako Harada – Co-evolution of Human and AI-Robots to Expand Science Frontiers
  • Loredana Zollo – Towards Synergistic Human–Machine Interaction in Assistive and Rehabilitation Robotics: Multimodal Interfaces, Sensory Feedback, and Future Perspectives
• Field Robotics
  • Matteo Matteucci – Robotics Meets Agriculture: SLAM and Perception for Crop Monitoring and Precision Farming
  • Brendan Englot – Situational Awareness and Decision-Making Under Uncertainty for Marine Robots
  • Abhinav Valada – Open World Embodied Intelligence: Learning from Perception to Action in the Wild
  • Timothy H. Chung – Catalyzing the Future of Human, Robot, and AI Agent Teams in the Physical World
• Humanoid Robot Systems
  • Kei Okada – Transforming Humanoid Robot Intelligence: From Reconfigurable Hardware to Human-Centric Applications
  • Xingxing Wang – A New Era of Global Collaboration in Intelligent Robotics
  • Wei Zhang – Towards Physical Intelligence in Humanoid Robotics
  • Dennis Hong – Staging the Machine: Not Built for Work, Built for Wonder
• Mechanisms and Controls
  • Kenjiro Tadakuma – Topological Robotic Mechanisms
  • Angela P. Schoellig – AI-Powered Robotics: From Semantic Understanding to Safe Autonomy
  • Lu Liu – Safety-Aware Multi-Agent Self-Deployment: Integrating Cybersecurity and Constrained Coordination
  • Fuchun Sun – Knowledge-Guided Tactile VLA: Bridging the Sim-to-Real Gap with Physics and Geometry Awareness

Debate

On Wednesday, a debate will be held on the following topic: “Humanoids Will Soon Replace Most Human Workers: True or False?” The participants will be: XingXing Wang (Unitree Robotics), Jun-Oh Ho (Samsung and Rainbow Robotics), Hong Qiao (Chinese Academy of Sciences), Andra Keay, (Silicon Valley Robotics), Yu Sun (EiC, IEEE Trans on Automation Science and Engineering), Tamim Asfour (Professor of Humanoid Robotics, Karlsruhe Institute of Technology), Ken Goldberg (UC Berkeley, Moderator).

Tutorials

There are three tutorials planned, taking place on Monday 20 and Friday 24 October.

• Enhancing Digital Accessibility in Higher Education
• Design and Development of Rehabilitation Robotic Devices: Healthcare Challenges
• Retico: A Framework for Robot-ready Spoken Dialogue with Robots

Workshops

You can find a list of the workshops here. These will take place on Monday 20 and Friday 24 October.There are 83 to choose from this year.

Find out more

• Conference webpage
• Forums
• Competitions

Introducing X1: The world's first multirobot system that integrates a humanoid robot with a transforming drone that can launch off the humanoid's back, and later, drive away.

What truly sets the SPT-1500UL apart is its patented E-type fork structure, which allows seamless compatibility with a wide variety of pallet types - including open pallets (nine-feet, stringer-style), as well as closed pallets, cages, and customized racks.

UMass Amherst engineers have built an artificial neuron powered by bacterial protein nanowires that functions like a real one, but at extremely low voltage. This allows for seamless communication with biological cells and drastically improved energy efficiency. The discovery could lead to bio-inspired computers and wearable electronics that no longer need power-hungry amplifiers. Future applications may include sensors powered by sweat or devices that harvest electricity from thin air.

I wrote last month that AI has made it easier than ever to produce code—and just as easy to produce insecure code. Development velocity has exploded. So have vulnerabilities. We’re now writing, generating, and deploying software faster than most organizations […]

The post How AI and Integration Are Transforming Software Security appeared first on TechSpective.

Rishi Jangale and Derek Pravecek with RoboBall III. Image credit: Emily Oswald/Texas A&M Engineering.

By Alyssa Schaechinger

While working at NASA in 2003, Dr. Robert Ambrose, director of the Robotics and Automation Design Lab (RAD Lab), designed a robot with no fixed top or bottom. A perfect sphere, the RoboBall could not flip over, and its shape promised access to places wheeled or legged machines could not reach — from the deepest lunar crater to the uneven sands of a beach. Two of his students built the first prototype, but then Ambrose shelved the idea to focus on drivable rovers for astronauts.

When Ambrose arrived at Texas A&M University in 2021, he saw a chance to reignite his idea. With funding from the Chancellor’s Research Initiative and Governor’s University Research Initiative, Ambrose brought RoboBall back to life.

Now, two decades after the original idea, RoboBall is rolling across Texas A&M University.

Driven by graduate students Rishi Jangale and Derek Pravecek, the RAD Lab is intent on sending RoboBall, a novel spherical robot, into uncharted terrain.

Jangale and Pravecek, both Ph.D. students in the J. Mike Walker ’66 Department of Mechanical Engineering, have played a significant part in getting the ball rolling once again.

“Dr. Ambrose has given us such a cool opportunity. He gives us the chance to work on RoboBall however we want,” said Jangale, who began work on RoboBall in 2022. “We manage ourselves, and we get to take RoboBall in any direction we want.”

Pravecek echoed that sense of freedom. “We get to work as actual engineers doing engineering tasks. This research teaches us things beyond what we read in textbooks,” he said. “It really is the best of both worlds.”

Robot in an airbag

At the heart of the project is the simple concept of a “robot in an airbag.” Two versions now exist in tandem. RoboBall II, a 2-foot-diameter prototype, is tuned for trial runs, monitoring power output and control algorithms. RoboBall III has a diameter of 6 feet across and is built with plans to carry payloads such as sensors, cameras or sampling tools, for real-world missions.

Upcoming tests will continue to take RoboBall into outdoor environments. RAD Lab researchers are planning field trials on the beaches of Galveston to demonstrate a water-to-land transition, testing the robot’s buoyancy and terrain adaptability in a real-world setting.

“Traditional vehicles stall or tip over in abrupt transitions,” Jangale explained. “This robot can roll out of water onto sand without worrying about orientation. It’s going where other robots can’t.”

The factors that create the versatility of RoboBall also lead to some of its challenges. Once sealed inside its protective shell, the robot can only be accessed electronically. Any mechanical failure means disassembly and digging through layers of wiring and actuators.

“Diagnostics can be a headache,” said Pravacek. “If a motor fails or a sensor disconnects, you can’t just pop open a panel. You have to take apart the whole robot and rebuild. It’s like open-heart surgery on a rolling ball.”

RoboBall’s novelty means the team often operates without a blueprint.

“Every task is new,” Jangale said. “We’re very much on our own. There’s no literature on soft-shelled spherical robots of this size that roll themselves.”

Despite those hurdles, the students find themselves surprised every time the robot outperforms expectations.

“When it does something we didn’t think was possible, I’m always surprised,” Pravecek said. “It still feels like magic.”

Student-led innovation

The team set a new record when RoboBall II reached 20 miles per hour, roughly half its theoretical power output. “We didn’t anticipate hitting that speed so soon,” Pravecek said. “It was thrilling, and it opened up new targets. Now we’re pushing even further.”

Ambrose sees these reactions as proof that student-led innovation thrives when engineers have room to explore.

“The autonomy Rishi and Derek have is exactly what a project like this needs,” he said. “They’re not just following instructions — they’re inventing the next generation of exploration tools.”

Long-term goals include autonomous navigation and remote deployment. The team hopes to see RoboBall dispatched from a lunar lander to chart steep crater walls or launched from an unmanned drone to survey post-disaster landscapes on Earth. Each ball could map terrain, transmit data back to operators and even deploy instruments in hard-to-reach spots.

“Imagine a swarm of these balls deployed after a hurricane,” Jangale said. “They could map flooded areas, find survivors and bring back essential data — all without risking human lives.”

As the RoboBall project rolls on, student-driven research stands on full display.

“Engineering is problem solving at its purest,” Ambrose said. “Give creative minds a challenge and the freedom to explore, and you’ll see innovation roll into reality.”

Rishi Jangale and Derek Pravecek with RoboBall III. Image credit: Emily Oswald/Texas A&M Engineering.

By Alyssa Schaechinger

While working at NASA in 2003, Dr. Robert Ambrose, director of the Robotics and Automation Design Lab (RAD Lab), designed a robot with no fixed top or bottom. A perfect sphere, the RoboBall could not flip over, and its shape promised access to places wheeled or legged machines could not reach — from the deepest lunar crater to the uneven sands of a beach. Two of his students built the first prototype, but then Ambrose shelved the idea to focus on drivable rovers for astronauts.

When Ambrose arrived at Texas A&M University in 2021, he saw a chance to reignite his idea. With funding from the Chancellor’s Research Initiative and Governor’s University Research Initiative, Ambrose brought RoboBall back to life.

Now, two decades after the original idea, RoboBall is rolling across Texas A&M University.

Driven by graduate students Rishi Jangale and Derek Pravecek, the RAD Lab is intent on sending RoboBall, a novel spherical robot, into uncharted terrain.

Jangale and Pravecek, both Ph.D. students in the J. Mike Walker ’66 Department of Mechanical Engineering, have played a significant part in getting the ball rolling once again.

“Dr. Ambrose has given us such a cool opportunity. He gives us the chance to work on RoboBall however we want,” said Jangale, who began work on RoboBall in 2022. “We manage ourselves, and we get to take RoboBall in any direction we want.”

Pravecek echoed that sense of freedom. “We get to work as actual engineers doing engineering tasks. This research teaches us things beyond what we read in textbooks,” he said. “It really is the best of both worlds.”

Robot in an airbag

At the heart of the project is the simple concept of a “robot in an airbag.” Two versions now exist in tandem. RoboBall II, a 2-foot-diameter prototype, is tuned for trial runs, monitoring power output and control algorithms. RoboBall III has a diameter of 6 feet across and is built with plans to carry payloads such as sensors, cameras or sampling tools, for real-world missions.

Upcoming tests will continue to take RoboBall into outdoor environments. RAD Lab researchers are planning field trials on the beaches of Galveston to demonstrate a water-to-land transition, testing the robot’s buoyancy and terrain adaptability in a real-world setting.

“Traditional vehicles stall or tip over in abrupt transitions,” Jangale explained. “This robot can roll out of water onto sand without worrying about orientation. It’s going where other robots can’t.”

The factors that create the versatility of RoboBall also lead to some of its challenges. Once sealed inside its protective shell, the robot can only be accessed electronically. Any mechanical failure means disassembly and digging through layers of wiring and actuators.

“Diagnostics can be a headache,” said Pravacek. “If a motor fails or a sensor disconnects, you can’t just pop open a panel. You have to take apart the whole robot and rebuild. It’s like open-heart surgery on a rolling ball.”

RoboBall’s novelty means the team often operates without a blueprint.

“Every task is new,” Jangale said. “We’re very much on our own. There’s no literature on soft-shelled spherical robots of this size that roll themselves.”

Despite those hurdles, the students find themselves surprised every time the robot outperforms expectations.

“When it does something we didn’t think was possible, I’m always surprised,” Pravecek said. “It still feels like magic.”

Student-led innovation

The team set a new record when RoboBall II reached 20 miles per hour, roughly half its theoretical power output. “We didn’t anticipate hitting that speed so soon,” Pravecek said. “It was thrilling, and it opened up new targets. Now we’re pushing even further.”

Ambrose sees these reactions as proof that student-led innovation thrives when engineers have room to explore.

“The autonomy Rishi and Derek have is exactly what a project like this needs,” he said. “They’re not just following instructions — they’re inventing the next generation of exploration tools.”

Long-term goals include autonomous navigation and remote deployment. The team hopes to see RoboBall dispatched from a lunar lander to chart steep crater walls or launched from an unmanned drone to survey post-disaster landscapes on Earth. Each ball could map terrain, transmit data back to operators and even deploy instruments in hard-to-reach spots.

“Imagine a swarm of these balls deployed after a hurricane,” Jangale said. “They could map flooded areas, find survivors and bring back essential data — all without risking human lives.”

As the RoboBall project rolls on, student-driven research stands on full display.

“Engineering is problem solving at its purest,” Ambrose said. “Give creative minds a challenge and the freedom to explore, and you’ll see innovation roll into reality.”

New tool from MIT CSAIL creates realistic virtual kitchens and living rooms where simulated robots can interact with models of real-world objects, scaling up training data for robot foundation models.

Vast amounts of valuable research data remain unused, trapped in labs or lost to time. Frontiers aims to change that with FAIR² Data Management, a groundbreaking AI-driven system that makes datasets reusable, verifiable, and citable. By uniting curation, compliance, peer review, and interactive visualization in one platform, FAIR² empowers scientists to share their work responsibly and gain recognition.

As we close in on the third anniversary of ChatGPT’s release to the world – November 30, 2022 – the chatbot, along with others like it, is well on the way to transforming the world as we know it.

My only hope is they don’t screw it up.

On the plus side, after nearly three years of turning to ChatGPT for writing, brainstorming – and research that can be confirmed with hotlinks – the magic of ChatGPT is still as fresh as the day it was born.

No matter how many times I type a question or prompt into ChatGPT or similar AI, its ability to respond with often incredibly insightful and artfully written prose still feels fantastical to me.

Unfortunately, AI makers have taken to mixing that verifiable magic with a healthy dose of smoke and mirrors, leaving many users wondering: What’s real and what’s snake oil?

As the new year unfolded, for example, we were promised that 2025 would be the ‘Year of the AI Agent,’ a wondrous new AI application that would work autonomously on our behalf, completing multi-step tasks for us without the need of supervision.

Instead, we were given AI’s version of vaporware: Extremely unreliable applications that often only get part of the job done, if we’re lucky — or worse, report back to us that the task we assigned was simply too difficult to complete.

Meanwhile, the release of ChatGPT-5, pre-packaged as ‘Beyond AI’s Next Big Thing,’ landed with a thud, sporting an AI personality so bland and off-putting, its maker raced to reinstate the earlier version it was supposed to replace – ChatGPT-4o – lest scores of ChatGPT users jumped ship.

The problem with repeatedly burning consumers with those kinds of empty promises is that they often walk away in complete disgust, characterizing the companies behind the digital head-fakes as charlatans not worth dealing with on any level.

And with AI, that’s the real crime.

OpenAI, Google, Anthropic, xAI and similar – they truly have come up with incredibly dazzling technology that if released in the late 1600s probably would have been seen as the work of witches.

But if they continue to mix the proven magic of AI with ‘wouldn’t it be nice’ ideas portrayed as ‘finished products you can trust,’ they risk discrediting the entire industry — and setting back the widespread adoption of AI by business and society by years.

As an avid, daily user of AI who deeply appreciates what AI can actually do, I truly hope that does not happen.

In the meantime, here are the stories that emerged in Q3 that helped drive the aforementioned trend – as well as a number of bright spots:

*ChatGPT’s Top Use at Work: Writing: A new study by ChatGPT’s maker finds that writing is the number one use for the tool at work.

Observes the study’s lead researcher Aaron Chatterji: “Work usage is more common from educated users in highly paid professional occupations.”

Another major study finding: Once mostly embraced by men, ChatGPT is now popular with women.

Specifically, researchers found that by July 2025, 52% of ChatGPT users had names that could be classified as feminine.

*Bringing in ChatGPT for Email: The Business Case: While AI coders push the tech to ever-loftier heights, one thing we already know for sure is AI can write emails at the world-class level — in a flash.

True, long-term, AI may one day trigger a world in which AI-powered machines do all the work as we navigate a world resplendent with abundance.

But in the here and now, AI is already saving businesses and organizations serious coin in terms of slashing time spent on email, synthesizing ideas in new ways, ending email drudgery as we know it and boosting staff morale.

Essentially: There are all sorts of reasons for businesses and organizations to bring-in bleeding edge AI tools like ChatGPT, Gemini, Anthropic, Claude and similar to take over the heavy lifting when it comes to email.

This piece offers up the Top Ten.

*ChatGPT-Maker Brings Back ChatGPT-4o, Other Legacy AI Engines: Responding to significant consumer backlash, OpenAI has restored access to GPT-4 and other legacy models that were popular before the release of GPT-5.

Essentially, many users were turned-off by GPT-5’s initial personality, which was perceived as cold, distant and terse.

Observes writer Will Knight: “The backlash has sparked a fresh debate over the psychological attachments some users form with chatbots trained to push their emotional buttons.”

*ChatGPT Plus Users Get Meeting Recording, Transcripts, Summaries: Users of ChatGPT Plus can now use the AI to quickly record meetings – as well as generate transcripts and summaries of those meetings.

Dubbed ‘Record Mode,’ the feature was previously only available to users of higher-tier, ChatGPT subscriptions.

Observes writer Lance Whitney: The AI “converts the spoken audio into a text transcript. From there, you can tell ChatGPT to analyze or summarize the content — and ask specific questions about the topics discussed.”

*New Claude Sonnet 4.5: 61% Reliability in Agent Mode: Anthropic is out with an upgrade to its flagship AI that offers 61% reliability when used as an agent for everyday computing tasks.

Essentially, that means when you use the Sonnet 4.5 as an agent to complete an assignment featuring multi-step tasks like opening apps, editing files, navigating Web pages and filling out forms, it will complete those assignments for you 61% of the time.

One caveat: That reliability metric – known as the OSWorld-Verified Benchmark – is based on Sonnet 4.5’s performance in a sandbox environment, where researchers pit the AI against a set of pre-programmed, digital encounters that never change.

Out on the Web – where things can get unpredictable
very quickly — performance could be worse.

Bottom line: If an AI agent that finishes three-out-of-every-five tasks turns your crank, this could be the AI you’ve been looking for.

*Skepticism Over the ‘Magic’ of AI Agents Persists: Despite blue-sky promises, AI agents – ostensibly designed to handle tasks autonomously for you on the Web and elsewhere – are still getting a bad rap.

Observes writer Rory Bathgate: “Let’s be very clear here: AI agents are still not very good at their ‘jobs’ — or at least pretty terrible at producing returns-on-investment.”

In fact, tech market research firm Gartner is predicting that 40% of agents currently used by business will be ‘put out to pasture’ by 2027.

*AI Agents: Still Not Ready for Prime Time?: Add Futurism Magazine to the growing list of naysayers who believe AI agents are being over-hyped.

Ideally, AI agents are designed to work independently on a number of tasks for you – such as researching, writing and continually updating an article, all on its own.

But writer Joe Wilkins finds that “the failure rate is absolutely painful,” with OpenAI’s AI agent failing 91% of the time, Meta’s AI agent failing 93% of the time and Google’s AI agent failing 70% of the time.

*Coming Soon: ChatGPT With Ads: If you’re a ChatGPT user who has oft-looked wistfully at the platform and fantasized, “If only this thing had ads,” you’re in luck.

Observes writer Andrew Cain: “OpenAI is building a team to transform ChatGPT into an advertising platform, leveraging its 700 million users for in-house ad tools like campaign management and real-time attribution.

”Led by ex-Facebook exec Fidji Simo, this move aims to compete with Google and Meta, though it risks user trust and privacy concerns.

”Rollout is eyed for 2026.”

*Google’s New ‘Nano Banana’ Image Editor: Cool Use Cases: The fervor over Google’s new image editor continues to rage across the Web, as increasing numbers of users are entranced by its power and surgical precision.

One of the new tool’s most impressive features: The ability to stay true to the identity of a human face – no matter how many times it remakes that image.

For a quick study, check-out these videos on YouTube, which show you scores of ways to use the new editor – officially known as Gemini 2.5 Flash Image:

–Google Gemini 2.5 Flash Image (Nano Banana) – 20 Creative Use Cases

–15 New Use Cases with Nano Banana

–The Ultimate Guide to Gemini 2.5 Flash (Nano Banana)

–New Gemini 2.5 Flash Image is Insane & Free

–Nano Banana Just Crushed Image Editing

*Grammarly Gets Serious Chops as Writing Tool: Best known as a proofreading and editing solution, Grammarly has repositioned itself as a full-fledged AI writer.

Essentially, the tool has been significantly expanded with a new document editor designed to nurture an idea into a full-blown article, blog post, report and similar – with the help of a number of AI agents.

Dubbed Grammarly ‘Docs,’ the AI writer promises to amplify your idea every step of the way – without stepping on your unique voice.

Share a Link:  Please consider sharing a link to https://RobotWritersAI.com from your blog, social media post, publication or emails. More links leading to RobotWritersAI.com helps everyone interested in AI-generated writing.

–Joe Dysart is editor of RobotWritersAI.com and a tech journalist with 20+ years experience. His work has appeared in 150+ publications, including The New York Times and the Financial Times of London.

The post Top Ten Stories in AI Writing, Q3 2025 appeared first on Robot Writers AI.

The real future of work isn’t remote or hybrid — it’s human + agent. 

Across enterprise functions, AI agents are taking on more of the execution of daily work while humans focus on directing how that work gets done. Less time spent on tedious admin means more time spent on strategy and innovation — which is what separates industry leaders from their competitors.

These digital coworkers aren’t your basic chatbots with brittle automations that break when someone changes a form field. AI agents can reason through problems, adapt to new situations, and help achieve major business outcomes without constant human handholding.

This new division of labor is enhancing (not replacing) human expertise, empowering teams to move faster and smarter with systems designed to support growth at scale.

What is an agent workforce, and why does it matter?

An “agent workforce” is a collection of AI agents that operate like digital employees within your organization. Unlike rule-based automation tools of the past, these agents are adaptive, reasoning systems that can handle complex, multi-step business processes with minimal supervision.

This shift matters because it’s changing the enterprise operating model: You can push through more work through fewer hands — and you can do it faster, at a lower cost, and without increasing headcount.

Traditional automation understands very specific inputs, follows predetermined steps (based on those initial inputs), and gives predictable outputs. The problem is that these workflows break the moment something happens that’s outside of their pre-programmed logic.

With an agentic AI workforce, you give your agents objectives, provide context about constraints and preferences, and they figure out how to get the job done. They adapt when circumstances and business needs change, escalate issues to human teams when they hit roadblocks, and learn from each interaction (good or bad). 

	Legacy automation tools	Agentic AI workforce
Flexibility	Rule-based, fragile tasks; breaks on edge cases	Outcome-driven orchestration; plans, executes, and replans to hit targets
Collaboration	Siloed bots tied to one tool or team	Cross-functional swarms that coordinate across apps, data, and channels
Upkeep	High upkeep, constant script fixes and change tickets	Self-healing, adapts to UI/schema changes and retains learning
Adaptability	Deterministic only, fails outside predefined paths	Ambiguity-ready, reasons through novel inputs and escalates with context
Focus	Project mindset; outputs delivered, then parked	KPI mindset; continuous execution against revenue, cost, risk, or CX goals

But the real challenge isn’t defining a single agent — it’s scaling to a true workforce.

From one agent to a workforce

While individual agent capabilities can be impressive, the real value comes from orchestrating hundreds or thousands of these digital workers to transform entire business processes. But scaling from one agent to an entire workforce is complex, and that’s the point where most proofs-of-concept stall or fail. 

The key is to treat agent development as a long-term infrastructure investment, not a “project.” Enterprises that get stuck in pilot purgatory are those that start with a plan to finish, not a plan to scale. 

Scaling agents requires governance and oversight — similar to how HR manages a human workforce. Without the infrastructure to do so, everything gets harder: coordination, monitoring, and control all break down as you scale. 

One agent making decisions is manageable. Ten agents collaborating across a workflow needs structure. A hundred agents working across different business units? That takes ironed-out, enterprise-grade governance, security, and monitoring.

An agent-first AI stack is what makes it possible to scale your digital workforce with clear standards and consistent oversight. That stack includes: 

• Compute resources that scale as needed
• Storage systems that handle multimodal data flows
• Orchestration platforms that coordinate agent collaboration
• Governance frameworks that keep performance consistent and sensitive data secure

Scaling AI apps and agents to deliver business-wide impact is an organizational redesign, and should be treated as such. Recognizing this early gives you the time to invest in platforms that can manage agent lifecycles from development through deployment, monitoring, and continuous improvement. Remember, the goal is scaling through iteration and improvement, not completion.

Business outcomes over chatbots

Many of the AI agents in use today are really just dressed-up chatbots with a handful of use cases: They can answer basic questions using natural language, maybe trigger a few API calls, but they can’t move the business forward without a human in the loop.

Real enterprise agents deliver end-to-end business outcomes, not answers. 

They don’t just regurgitate information. They act autonomously, make decisions within defined parameters, and measure success the same way your business does: speed, cost, accuracy, and uptime.

Think about banking. The traditional loan approval workflow looks something like:

Human reviews application -> human checks credit score -> human validates documentation -> human makes approval decision 

This process takes days or (more likely) weeks, is error-prone, creates bottlenecks if any single piece of information is missing, and scales poorly during high-demand periods.

With an agent workforce, banks can shift to “lights-out lending,” where agents handle the entire workflow from intake to approval and run 24/7 with humans only stepping in to focus on exceptions and escalations.

The results?

• Loan turnaround times drop from days to minutes.
• Operational costs fall sharply.
• Compliance and accuracy improve through consistent logic and audit trails.

In manufacturing, the same transformation is happening in self-fulfilling supply chains. Instead of humans constantly monitoring inventory levels, predicting demand, and coordinating with suppliers, autonomous agents handle the entire process. They can analyze consumption patterns, predict shortages before they happen, automatically generate purchase orders, and coordinate delivery schedules with supplier systems.

The payoff here for enterprises is significant: fewer stockouts, lower carrying costs, and production uptime that isn’t tied to shift hours.

Security, compliance, and responsible AI

Trust in your AI systems will determine whether they help your organization accelerate or stall. Once AI agents start making decisions that impact customers, finances, and regulatory compliance, the question is no longer “Is this possible?” but “Is this safe at scale?”

Agent governance and trust are make-or-break for scaling a digital workforce. That’s why it deserves board-level visibility, not an IT strategy footnote. 

As agents gain access to sensitive systems and act on regulated data, every decision they make traces back to the enterprise. There’s no delegating accountability: Regulators and customers will expect transparent evidence of what an agent did, why it did it, and which data informed its reasoning. Black-box decision-making introduces risks that most enterprises cannot tolerate.

Human oversight will never disappear completely, but it will change. Instead of humans doing the work, they’ll shift to supervising digital workers and stepping in when human judgment or ethical reasoning is needed. That layer of oversight is your safeguard for sustaining responsible AI as your enterprise scales.

Secure AI gateways and governance frameworks form the foundation for the trust in your enterprise AI, unifying control, enforcing policies, and helping maintain full visibility across agent decisions. However, you’ll need to design the governance frameworks before deploying agents. Designing with built-in agent governance and lifecycle control from the start helps avoid costly rework and compliance risks that come from trying to retrofit your digital workforce later. 

Enterprises that design with control in mind from the start build a more durable system of trust that empowers them to scale AI safely and operate confidently — even under regulatory scrutiny.

Shaping the future of work with AI agents

So, what does this mean for your competitive strategy? Agent workforces aren’t just tweaking your existing processes. They’re creating entirely new ways to compete. The advantage isn’t about faster automation, but about building an organization where:

• Work scales faster without adding headcount or sacrificing accuracy. 
• Decision cycles go from weeks to minutes. 
• Innovation isn’t limited by human bandwidth.

Traditional workflows are linear and human-dependent: Person A completes Task A and passes to Person B, who completes Task B, and so on. Agent workforces let dynamic, parallel processing happen where multiple agents collaborate in real time to optimize outcomes, not just check specific tasks off a list.

This is already leading to new roles that didn’t exist even five years ago:

• Agent trainers specialize in teaching AI systems domain-specific knowledge. 
• Agent supervisors monitor performance and jump in when situations require human judgment. 
• Orchestration leads structure collaboration across different agents to achieve business objectives.

For early adopters, this creates an advantage that’s difficult for latecomer competitors to match. 

An agent workforce can process customer requests 10x faster than human-dependent competitors, respond to market changes in real time, and scale instantly during demand spikes. The longer enterprises wait to deploy their digital workforce, the harder it becomes to close that gap.

Looking ahead, enterprises are moving toward:

• Reasoning engines that can handle even more complex decision-making 
• Multimodal agents that process text, images, audio, and video simultaneously
• Agent-to-agent collaboration for sophisticated workflow orchestration without human coordination

Enterprises that build on platforms designed for lifecycle governance and secure orchestration will define this next phase of intelligent operations. 

Leading the shift to an agent-powered enterprise

If you’re convinced that agent workforces offer a strategic opportunity, here’s how leaders move from pilot to production:

1. Get executive sponsorship early. Agent workforce transformation starts at the top. Your CEO and board need to understand that this will fundamentally change how work gets done (for the better).
2. Invest in infrastructure before you need it. Agent-first platforms and governance frameworks can take months to implement. If you start pilot projects on temporary foundations, you’ll create technical debt that’s more expensive to fix later.
3. Build in governance frameworks from Day 1. Put security, compliance, and monitoring frameworks in place before your first agent goes live. These guardrails make scaling possible and safeguard your enterprise from risk as you add more agents to the mix.
4. Partner with proven platforms that specialize in agent lifecycle management. Building agentic AI applications takes expertise that most teams haven’t developed internally yet. Partnering with platforms designed for this purpose shortens the learning curve and reduces execution risk.

Enterprises that lead with vision, invest in foundations, and operationalize governance from day one will define how the future of intelligent work takes shape.

Explore how enterprises are building, deploying, and governing secure, production-ready AI agents with the Agent Workforce Platform. 

The post The agent workforce: Redefining how work gets done  appeared first on DataRobot.

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