Category robots in business

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Small aquatic robots that assemble into reconfigurable structures on the water

By in News, robotics, Robotics Classification, robots, robots in business, Robots Podcast Tag
Most people think of the waterfront as the edge of the city. A team of MIT researchers sees it as a dynamic, Lego-like construction site. Their new system, called "FloatForm," is a swarm of small square robotic boats that assemble themselves into larger structures on the water, break apart and reassemble into something new, all with minimal human direction.

Harvard scientists turn a silicon chip into a DNA writing machine

By in News, robotics, Robotics Classification, robots, robots in business, Robots Podcast Tag
Scientists have created a silicon chip that can write dozens of DNA sequences simultaneously using electricity and water-based enzymes, offering a cleaner alternative to conventional DNA manufacturing. The breakthrough could eventually support portable DNA-writing devices and even massive DNA data storage, although new chemistry will be needed to scale the technology further.

Scientists used AI to crack one of water’s biggest mysteries

By in News, robotics, Robotics Classification, robots, robots in business, Robots Podcast Tag
Water’s odd behavior becomes even more dramatic when it is supercooled, but scientists have struggled to compare the many different ways of describing its microscopic structure. Researchers at the University of Osaka used an AI model trained on computer simulations to evaluate 16 different structural descriptors. The system identified the most effective ways to distinguish between water’s two competing liquid states, providing a clearer framework for studying one of nature’s most mysterious substances.

Your identity stack was built for two kinds of actor. Agents are a third.

By in News, robotics, Robotics Classification, robots, robots in business, Robots Podcast Tag

Your identity stack was built for two kinds of actor. Agents are a third.

An engineer ships an agent to production this week. It needs to call an internal API, so it uses the key already sitting in the engineer’s environment. The agent runs. It also now holds every permission that engineer holds.

That is the default state of most agent deployments today. The agent has no identity of its own, so it borrows one. It works on day one, which is exactly why the problem ships unnoticed. A non-human process is now carrying a human’s full access, and nothing in your audit log can tell the two apart.

This is not a configuration mistake. It is a structural gap. Identity and access management assumes an actor is one of two things: a person, or a long-lived service account with a static permission set. Both are stable. Both do roughly the same thing every day. Your controls, your audit model, and your provisioning flows are all built on that stability.

Agents are neither. They act on behalf of people, so they are not service accounts. They spin up and tear down on their own schedule, so they are not people. They sit in the gap your IAM stack has no category for, and the gap is where the credential gets borrowed.

Why you cannot just patch this

The reason existing IAM cannot simply absorb agents is non-determinism. A service account calls the same endpoints at the same cadence every time. Give two agents the same permissions and the same goal, and they can take different actions, because each one picks its tool chain at runtime based on its prompt, its context, and the output of whatever called it.

The set of actions an agent will actually take is not knowable when you grant its permissions. That turns design-time least privilege into a design-time answer to a runtime problem. You are deciding in advance what an actor may do, when the actor decides what to do only once it is running.

That single fact is the spine of this series. It is why borrowed credentials fail, why scope has to be narrow, why a delegation chain has to stay inspectable, and why authorization cannot be a one-time grant. The question that matters is not “who is this actor.” It is “what is this actor authorized to do right now, for this task.”

If you do nothing else this week

Before the series goes deep, three checks you can run today against any agent already in production.

Look for human API keys being used by non-human processes. If an agent authenticates as the person who deployed it, you have privilege inheritance in production right now.

Check whether your audit logs can separate agent actions from human actions. If they cannot, your incident response and your compliance story both break at the same moment.

Confirm you can shut one agent off without rotating a human’s credential or breaking three other things. If revocation means collateral damage, you do not have an off switch. You have a hostage situation.

None of these is the full fix. They are the floor. Finding where they fail tells you where to start.

What solving this actually looks like

The rest of the series builds the answer in layers, each one resting on the one below it.

It starts with giving the agent a stable, verifiable runtime principal you can authorize against, attribute actions to, and revoke on its own. Then it has to survive contact with reality: agents call tools, tools call other agents, and identity has to stay intact across every hop so you can still answer who originally asked and which actor is making this specific call. The credential the agent uses to make those calls has to stay out of the model itself, where a single prompt injection could read it and send it anywhere. Above that sits the question of where the rules live, and who decides what an agent may do the moment it acts somewhere its own platform does not reach. Underneath all of it runs the lifecycle: an identity you provision, scope, revoke, and re-evaluate while the agent runs, not a record you write once and forget.

Identity is the foundation the rest depends on. Authorization, governance, and observability all sit on top of it. Get identity wrong and nothing above it holds.

Where DataRobot fits

The agent platform from DataRobot treats agent identity as first-class infrastructure, not an afterthought bolted on at deploy time. The direction is the one this series argues for: give each agent its own scoped identity, keep the delegation chain intact and auditable when agents call tools and other agents, govern that identity in one control plane, and federate trust outward to the identity providers and workload identity systems an enterprise already runs.

What is coming

Part one takes apart the borrowed credential. Why inheriting a human’s key is privilege escalation by default, and why the fix is authority decided at runtime, not a passport stamped once at the border.

Part two defines what a first-class agent identity actually is: a distinct principal, scoped permissions, a clear owner, and a kill switch. It also shows how you make that principal trustworthy through attestation, then engages the question a good engineer is already asking. Is this just workload identity? It depends on three invariants, and where they break is where most real fleets live.

Part three follows identity through a delegation chain. RFC 8693 token exchange, the confused deputy you create when you flatten that chain, and the two protocol surfaces where it is preserved or destroyed in practice: MCP and A2A.

Part four keeps the secret out of the model. An agent’s context is attackable, so a prompt injection can read anything in it. That is why the raw credential should never reach the agent’s process. A broker holds it and injects auth at the boundary, and the agent only ever gets a scoped capability.

Part five is about where authorization and governance sit. Govern natively, federate outward, size controls to blast radius, and the frontier nobody has cleanly solved: what happens when an agent acts across trust domains its issuing platform does not control.

Part six treats identity as a lifecycle. Just-in-time, task-scoped credentials, no standing privilege, and continuous runtime authorization. It closes the loop back to non-determinism and leaves you with a six-question audit to run against one real agent.

Part one publishes next. If you want a head start, go find one agent in your environment right now and check whose credentials it is using. That answer is where the series begins.

The post Your identity stack was built for two kinds of actor. Agents are a third. appeared first on DataRobot.

#RoboCup2026 social media round-up

By in News, robotics, Robotics Classification, robots, robots in business, Robots Podcast Tag

This year, RoboCup took place in Incheon, South Korea, from 2-6 July. The event saw teams take part in competitions, training sessions, and a symposium. Take a look at what the participants got up to in our round up from social media.

Little Booster K1, trying to get up after falling mid football game!
#robocup2026

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— Raghav Arora (@ra-aurora.bsky.social) 6 July 2026 at 05:05

Straight from RoboCup 2026, at Incheon, Korea.
We added an external lidar, and a literal backpack to the booster T1 to achieve accurate indoor localisation and navigation.
@texasrobotics.bsky.social #Robocup2026

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— Raghav Arora (@ra-aurora.bsky.social) 6 July 2026 at 05:01

New AI model reveals how neutron star mergers forge heavy elements

By in News, robotics, Robotics Classification, robots, robots in business, Robots Podcast Tag
Researchers have created an AI-based simulation that makes it much faster to model how neutron star mergers produce many of the universe's heaviest elements. The new tool could improve predictions of these powerful explosions while helping scientists better connect observations in space with experiments on Earth.

An AI-powered control system for robots with legs

By in News, robotics, Robotics Classification, robots, robots in business, Robots Podcast Tag
Walking robots, such as quadruped robotic dogs, must be able to move safely through rough, often changing environments. Today, there are two main ways to program these walking, or legged, robots. The first is called model predictive control. This technique optimizes the robot's behavior but relies on accurate dynamics models, which are challenging to achieve in real-world settings and often require simplifying assumptions. The second is model-free reinforcement learning, which allows the robot to learn reliable but fixed behaviors, making them difficult to adapt after training.

Agentic Computers Redefine the Enterprise Workspace as AMD Initiates the Next Hardware Revolution

By in News, robotics, Robotics Classification, robots, robots in business, Robots Podcast Tag

We are standing on the precipice of a monumental shift in how humans interact with machines. For over four decades, the personal computer has been a fundamentally passive device. It waits for a keystroke, a mouse click, or a touch […]

The post Agentic Computers Redefine the Enterprise Workspace as AMD Initiates the Next Hardware Revolution appeared first on TechSpective.

A soft exoskeleton could restore hand function in people with motor impairments

By in News, robotics, Robotics Classification, robots, robots in business, Robots Podcast Tag
Recent technological advances have opened valuable possibilities for supporting people with motor impairments or who are recovering from injuries to the brain, spinal cord or nerves. Millions of people worldwide currently experience difficulty moving their hands or other parts of their body. Some of these motor impairments are associated with progressive neurodegenerative diseases, such as amyotrophic lateral sclerosis (ALS), while others are the result of neurological damage caused by an injury or a stroke.

The Two Mistakes Slowing Down AI Adoption (and How to Overcome Them)

By in News, robotics, Robotics Classification, robots, robots in business, Robots Podcast Tag

Companies are investing in AI at record levels, yet most are still struggling to translate it into measurable business value. The well-known MIT study, State of AI in Business 2025, concludes that 95% of enterprise GenAI pilots have failed to […]

The post The Two Mistakes Slowing Down AI Adoption (and How to Overcome Them) appeared first on TechSpective.

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