Category Robots Podcast

Inside a giant autonomous warehouse, hundreds of robots dart down aisles as they collect and distribute items to fulfill a steady stream of customer orders. In this busy environment, even small traffic jams or minor collisions can snowball into massive slowdowns. To avoid such an avalanche of inefficiencies, researchers from MIT and the tech firm Symbotic developed a new method that automatically keeps a fleet of robots moving smoothly.

A team led by Worcester Polytechnic Institute (WPI) researcher Nitin J. Sanket has shown that ultrasound sensors and a form of artificial intelligence (AI) can enable palm-sized aerial robots to navigate with limited power and computation through fog, smoke, and other challenging conditions during search-and-rescue operations.

Performance does not increase linearly as additional robots are deployed. Beyond a certain threshold, traffic conflicts, task contention, and route overlap can degrade system efficiency if orchestration logic is not sufficiently advanced.

Deepfake X-rays created by AI are now convincing enough to fool both doctors and AI models. In tests, radiologists had limited success identifying fake images, especially when they didn’t know they were being shown. This opens the door to risks like fraudulent medical claims and tampered diagnoses. Experts say stronger safeguards and detection tools are critical as the technology advances.

What do autopilot and enterprise agentic AI have in common? Both can operate autonomously. Both require a human to set the rules, boundaries, and alerts before the system takes the controls. And in both cases, skipping that step isn’t bold. It’s reckless.

Most enterprises are deploying AI agents the same way early teams deployed cloud infrastructure: fast, with governance as an afterthought. What looked like speed at first turned into sprawl, security gaps, and years of technical debt.

AI agents that reason, decide, and act autonomously demand a different approach. Governance isn’t a constraint. It’s what keeps these systems reliable, secure, and under control.

As enterprises adopt AI agents as a new class of autonomous systems, DevOps teams are responsible for keeping them inside the guardrails. Right now, those agents are starting to route tickets, execute workflows, and make decisions across your systems at a scale traditional software never required you to manage.

This is your survival guide to the agentic AI lifecycle: what to plan for, what to watch, and how to build governance that accelerates deployment instead of blocking it.

Key takeaways

• Governance must be built into every stage of the agentic AI lifecycle. Unlike static software, AI agents evolve over time, so governance can’t be an afterthought.
• Agentic AI changes what DevOps teams need to monitor and control. Success depends on observing agent behavior, decisions, and interactions, not just uptime or resource usage.
• Identity-first security is foundational for safe agent deployments. Agents need their own credentials, permissions, and policies to prevent data exposure and compliance failures.
• Automation is essential to scale AgentOps responsibly. CI/CD, containerization, orchestration, and automated observability reduce risk while preserving speed.
• Governed agents deliver more business value over time. When governance is embedded in the lifecycle, teams can scale agent workloads without accumulating security debt or compliance risk.

Why governance matters in AI agent deployments

Ungoverned agents don’t just underperform. They trigger compliance failures, expose sensitive data, and interact unpredictably across the systems they touch. Once that happens, the damage is hard to contain.

Governance gives you visibility and control across the full agentic AI lifecycle, from ideation through deployment to retirement. It enforces policies, monitors agent behavior, and keeps deployments compliant, secure, and resilient. It also makes complex workflows easier to standardize, scale, and repeat across the business.

But governance for agentic AI is fundamentally different from governance for static software. Agents have identities, permissions, task-specific responsibilities, and behaviors that can change over time. They don’t just execute. They reason, act, and adapt. Your governance framework has to keep up across the full lifecycle, not just at deployment.

How to plan and design a secure AI agent lifecycle

Planning for static software and planning for AI agents are not the same problem. With software, you’re managing infrastructure. With agents, you’re managing behavior: how they make decisions, how they interact with existing systems, and how they stay compliant as they evolve.

Get this stage wrong, and everything downstream pays for it. Get it right, and you’re catching problems before they’re expensive, building agents that are reliable and scalable, and setting your team up to govern them without constant firefighting.

This section lays out the blueprint for getting that foundation right.

Determining organizational goals

No AI for the sake of AI. Agents should solve real business challenges, integrate into core processes, and have measurable outcomes attached from day one.

Start by identifying the specific problems you want agents to address. Then connect those problems to quantifiable KPIs. In traditional DevOps, that means tracking uptime and performance metrics. In agentic AI, that means tracking decision accuracy, task completion rates, policy adherence, and productivity impact.

The framework below gives you a starting point for aligning goals to the right metrics.

Governing agent behavior and compliance 

You’re not just governing what data agents can access. You’re governing how they reason over that data and what they do with it. That’s a fundamentally different problem from traditional software governance.

With traditional software, role-based access control (RBAC) is usually sufficient. With agents, it’s a starting point at best. Agents make decisions, generate answers, and take actions, none of which RBAC was designed to govern.

Agentic AI governance must include: 

• Auditing agent answers
• Monitoring for violations
• Enforcing guardrails
• Documenting agent behavior

Agents should only interact with the data needed to complete their specific tasks. Early compliance planning keeps agent behavior in check and helps prevent violations before they become incidents. 

Selecting tools and frameworks for agent management

Most teams try to manage AI agents by stitching together existing MLOps, DevOps, and DataOps tooling. The problem is that none of it was built to handle agents that reason, decide, and act autonomously. You end up with visibility gaps, compliance blind spots, and a fragile stack that doesn’t scale.

You need a unified platform built for the full agent management lifecycle.

Look for a platform that: 

• Integrates with your existing AI systems and data sources
• Provides real-time observability into agent decisions, behavior, and performance
• Scales to support growing agent workloads
• Supports compliance requirements and industry standards, such as HIPAA, ISO 27001, and SOC 2
• Demonstrates robust auditing capabilities 

How to deploy and orchestrate AI agents at scale

Deployment is where planning meets reality. This is where you start measuring agent performance under real-world conditions and validating that agents are actually solving the business challenges you defined earlier.

Orchestration is what keeps agents, tasks, and workflows moving in sync. Dependencies have to be managed, failures have to be recovered, and resources have to be allocated without disrupting ongoing operations.

Automation makes that possible at scale without introducing new risk:

• CI/CD pipelines accelerate testing and deployment while reducing manual error.
• Version control ensures consistency and traceability, so you can roll back changes when problems arise.

Configuring orchestration and scheduling

Orchestrating AI agents isn’t the same as orchestrating traditional workloads. Agents have dependencies, interact with other agents and tools, and can overwhelm downstream systems if not properly managed. In a multi-agent environment, one poorly configured agent can trigger cascading failures. 

Tools like Kubernetes help manage part of this complexity by handling container orchestration, scheduling, and recovery. If a service fails, Kubernetes can automatically restart or reschedule it, helping restore availability without manual intervention.

But agent orchestration goes beyond infrastructure management. It also requires structured execution: coordinating task flow, enforcing policy controls, managing retries and failures, and allocating resources as agent workloads grow. That is what keeps operations stable, scalable, and compliant.

Implementing observability and alert mechanisms

With traditional software, observability means tracking uptime and resource usage. With agents, you’re monitoring behavior, decisions, and interactions in real time. The signals are different, and missing them has different consequences.

Observability for agentic AI covers logs, metrics, and traces that tell you not just whether an agent is running, but whether it’s behaving as expected, staying within policy boundaries, and interacting with other systems as intended.

Proactive alerts close the loop. When an agent violates policy or behaves unexpectedly, your team is notified immediately to contain the issue before it affects downstream systems or triggers a compliance incident. The goal isn’t to watch every decision. It’s to catch the ones that matter before they become problems.

Monitor, observe, and improve

Deployment isn’t the finish line. Agents evolve, data changes, and business requirements shift. Continuous monitoring is what keeps agents aligned with the goals you set at the start.

Start by establishing baselines: the performance benchmarks you’ll measure agents against over time. These should tie directly to the KPIs you defined during planning, whether that’s response time, decision accuracy, or policy adherence. Without clear baselines, you’re monitoring noise.

From there, build a continuous improvement loop. Update models, prompts, and workflows as new data and operational insights become available. Run A/B tests to validate changes before rolling them out. Track whether iterative improvements are actually moving your core metrics. The agents that drive the most business value aren’t the ones that launched well. They’re the ones that continue improving over time.

Identity-first security and compliance best practices

In traditional security, you govern users, then applications. With agentic AI, you govern agents too, and the rules are more complex.

An agent doesn’t just need its own credentials, policies, and privileges. If that agent interacts with an employee, it must also understand and respect that employee’s access rights. The agent may have broader reach across data sources to complete its task, but it can’t expose information the employee isn’t entitled to see. That’s a security boundary traditional access controls weren’t designed to manage.

Identity-first security addresses this directly. Every agent gets unique credentials scoped to its specific tasks, nothing more. Core controls include:

• RBAC to restrict agent actions based on roles
• Least privilege to limit agent access to the minimum required
• Encryption to protect data in transit and at rest
• Logging to maintain audit trails for compliance and troubleshooting

Conduct quarterly access control audits to prevent scope creep and privilege sprawl. Inventory agent permissions, decommission unused access, and verify compliance. Agents accumulate permissions over time. Audits keep that in check.

Handling AI agent upgrading, transitions, retraining, and retirement

Unlike static software, agents don’t just become outdated. Their behavior can shift over time. They interact with new data, adapt their behavior, and can drift beyond the guardrails and logic you originally built around them. That makes retirement more complex than deprecating a software version.

Knowing when to retire an agent requires active monitoring and judgment, not just a scheduled update cycle. When an agent’s behavior no longer aligns with business goals, compliance requirements, or security boundaries, it’s time to decommission it.

Responsible AI retirement includes: 

• Data migration: archiving data from retired agents or transferring it to replacements 
• Documentation: capturing agent behavior, decisions, and dependencies before decommissioning
• Compliance verification: reviewing data retention and other security policies to confirm compliance 

Skipping end-of-life management creates exactly the kind of technical debt and security gaps that governed deployments are designed to prevent. Retirement isn’t the last step you get around to. It’s part of the lifecycle from day one.

Driving business value with fully governed AI agents

Governance isn’t what slows deployment down. It’s what makes deployment worth doing. Agents with governance embedded across their lifecycle are more consistent, more reliable, and easier to scale without accumulating security debt or compliance risk.

That’s how governed AI becomes a competitive advantage: not by moving faster, but by moving with confidence.

See how enterprise teams are operationalizing agentic AI from day zero to day 90.

FAQs

Why is governance more critical for agentic AI than traditional applications? Agentic AI systems make autonomous decisions, interact with other agents and systems, and change behaviorally over time. Without governance, that autonomy creates unpredictable behavior, security risks, and compliance violations that are expensive and difficult to remediate.

How is agentic AI governance different from traditional DevOps governance? Traditional DevOps focuses on infrastructure stability and application performance. Agentic AI governance must also cover agent decisions, task ownership, data usage, and behavioral constraints across the full lifecycle.

What should DevOps teams monitor for AI agents? In addition to system health, teams should monitor decision accuracy, policy adherence, task completion rates, unusual behavior patterns, and interactions between agents. These signals catch issues before they become incidents.How can organizations scale governed AI agents without slowing innovation? DataRobot embeds governance, observability, and security directly into the agent lifecycle. DevOps teams move fast while maintaining control, compliance, and trust as agent workloads grow.

The post The DevOps guide to governing and managing agentic AI at scale appeared first on DataRobot.

AI-powered robots are set to track across thousands of kilometers of baked, uneven ground, reducing the danger for maintenance workers on Australia's large-scale solar farms. A successful trial by CSIRO, Australia's national science agency, repurposed autonomous robots originally designed for the mining industry. Without robots, the work is done on foot, bringing significant cost and safety risks.

Google DeepMind researches AI's harmful manipulation risks across areas like finance and health, leading to new safety measures.

Introducing Lyria 3 Pro, which unlocks longer tracks with structural awareness. We’re also bringing Lyria to more Google products and surfaces.

While space structures and robotic arms require lightweight actuation devices capable of repetitive movement, conventional motor-based systems face limitations due to their heavy weight and complex structures. A KAIST research team has developed a smart material-based actuation technology that operates rapidly in less than a second without a motor, suggesting new possibilities for next-generation robotics and space deployable structures.

On paper, a robot is just a set of joints and motors. On a production floor, however, it becomes something entirely different.

The next time you're scrolling on your phone, take a moment to appreciate the feat: The seemingly mundane act is possible thanks to the coordination of 34 muscles, 27 joints, and over 100 tendons and ligaments in your hand. Indeed, our hands are the most nimble parts of our bodies. Mimicking their many nuanced gestures has been a longstanding challenge in robotics and virtual reality.

Amazon has acquired Fauna Robotics, just under two months after the startup introduced a humanoid robot called Sprout designed to be a friendly addition to social spaces like homes and schools.

A combination of infrared imaging, thermal imaging and color cameras on an uncrewed drone, along with an AI system to interpret the data, can help emergency responders and search-and-rescue teams locate, identify and track people who have gone missing in the wilderness. The experimental system helps responders pinpoint where a missing person is and determine whether they are hurt or even alive.

Multi-axis gantry platforms are central to high-throughput automation in fields like semiconductor inspection, laser processing, and advanced metrology. Yet, the difficulty of making them precise, reliable, and production-ready is often underestimated.

RoboCup is an international competition that promotes and advances robotics and AI through the challenges presented by its various leagues. We got the chance to sit down with Professor Manuela Veloso, one of RoboCup’s founders, to find out more about how it all started, how the community has grown over the years, and the vision for the future.

I think it would be very interesting to go right back to the beginning and hear how RoboCup got started. What was the initial idea, and how did it get set up?

So we are talking about the mid-90s. In terms of the research in those days, it was the beginning of the internet and many AI and computer science researchers were focused on the internet, first on sophisticated search algorithms, on natural language understanding, on information retrieval, and then on software agents and machine learning applied to digital information. From what I recall, there was a smaller group of researchers who were interested in actual, physical robots, and in particular in AI and robotics. I myself was specifically interested in the problem of creating autonomous robots with perception (get information from the world), cognition (select action to achieve goals), and then act (execute the planned actions). This combination of perception, cognition, and action is a very good framework for autonomous robots, because they have to get their information from their sensors, they have to reason about actions to achieve their goals, and then execute them. So, during the 90s, I was at Carnegie Mellon with this AI research goal of integrating perception, cognition, and action in autonomous robots.

Over in Canada there was Alan Mackworth, who jointly with his wonderful student, Michael Sahota, built a one-on-one little autonomous robot soccer set-up. Two robots ran on a small field that had a camera overhead, and aimed at scoring each in one of the two little goals. This work showed that this task of kicking a ball and defending and aiming at a goal could be done autonomously. So it was a tremendous demonstration that a robot soccer world could exist. Mostly at the same time, in Japan, Minoru Asada was showing that a big robot could learn with reinforcement learning how to push a ball into a goal. So you have these one-on-one, fully autonomous little robot cars that were pushing balls around in Canada, and then there was this effort of learning to score with a larger robot in Japan. The learning robot didn’t have a team, it was not a real game, but it was showing that reinforcement learning could learn the skill of aiming into a goal. And then there was also Hiroaki Kitano at Sony who was very interested in little humanoids.

So this is very beautiful because all these things came into play – all of us had different interests.

Alan Mackworth did not get involved with RoboCup, but he gave a demonstration of these one-on-one robots at AAAI in 1994. And in those days, I had a PhD student who had just joined – Peter Stone. And Peter was a serious and passionate soccer player. He saw this little game and he came to me and said, “this is what I want to do for my thesis research, robot soccer!” And for me, I was trying to find a research environment where autonomy was needed in the robot world. I had already a student, Karen Haigh, who was working with autonomous office robots, and learning to plan and execute. But with these soccer robots and Peter Stone’s interest everything came to play, and we started robot soccer research in my lab.

In 1996, there was also a robot soccer effort in South Korea, called MIROSOT, and that’s the first competition we participated in. So Peter Stone, myself, and the team we built at Carnegie Mellon – Sorin Achim and Kwun Han – went to South Korea to participate. From South Korea, we flew to a robot soccer workshop in Japan organized by Minoru Asada and Hiroaki Kitano. Also in attendance were Dominique Duhaut, Itsuki Noda, Silvia Coradeschi, and Enrico Pagello. And that’s where RoboCup really started – we decided to do a competition. And the good thing was that Kitano was the chair of IJCAI, which was going to happen in Osaka in the summer of 1997. So we are there in Osaka and literally we came up with this idea of having a robot soccer competition, RoboCup. It was a big moment for us as researchers. We had to come up with the rules of this competition so that people would be able to participate seven months later. We came up with the three leagues that we were interested in and had expertise in.

The small size league, building upon our Carnegie Mellon interests, would have a field with a camera overhead connected to a computer and then the computer would remotely control the robots through radio.

Then Minoru Asada had these bigger robots with wheels, and we created a league that we call the middle-size league to include the robotics research of Minoru Asada and others. And then Itsuki Noda was interested in creating a simulation environment. We thought that this would help get more people participating in this task of robot soccer.

So that’s how the three leagues started: the small size, the middle size, and the simulation. Hiroaki Kitano, Dominique Duhaut and I were in charge of the small-size league, Minoru Asada was in charge of the middle-size league, and Itsuki Noda ran the simulation league.

One of the challenges was to come up with the rules and define the robots and playing fields. I remember my own pragmatism in suggesting that we play on a ping-pong table for the small-size, as a ping-pong table is something that exists in the whole world. That meant that we would have a playing surface, with defined size and texture, anywhere in the world. We decided one ping-pong table for the small-size league, and nine ping-pong tables for the middle-size league.

In the summer of 1997, at IJCAI, when we all went to the actual first RoboCup competition, the space was gorgeous. Hiroaki Kitano had made these beautiful fields and white bleachers around the fields. It was a very beautiful space with an area with computers for the simulation league. There were 80 teams that had joined the simulation online. We were, I believe, five teams for the small size league and about eight to ten for the middle size league.

That’s how it all started. And 1997 it was in Osaka, in 1998 Dominique Duhaut organized RoboCup in Paris, at a modern Science Center, La Villette. And then in 1999, RoboCup was organized by Silvia Coradeschi, in Stockholm, again co-located with the IJCAI conference. In these three years 1997, ’98, and ’99, there were only these three leagues, small size, middle size, and simulation. It was the foundation of everything. RoboCup grew consistently every year in terms of the number of teams, the number of participants, and the number of participating countries.

Some of the pitches in the main soccer arena at RoboCup 2024, held in Eindhoven, The Netherlands.

So how did RoboCup expand and could you talk about the decisions to add extra leagues?

Well, nothing was necessarily planned, it was more related to the research interests that people had. So when we were in Melbourne in 2000, there were two things that were added. One was RoboCupJunior. There was a professor, Elizabeth Sklar, who had a tremendous interest in robotics education for children. She proposed the RoboCupJunior competitions for children K-12. The goal of RoboCupJunior was to train all these young people to do research in robotics. It was and it still is extremely successful. In Melbourne there were probably the same number of children as there were people competing in what we now call the major leagues. As well as the soccer leagues, Elizabeth also ran a dance competition. This was very impressive – the children would dance on stage with their designed, built, and programmed mobile robots.

I believe that the rescue league was also introduced in 2000. The reason why we came up with rescue was because in those days, there was a lot of research on robots in disaster environments. So many people in our groups had an interest in developing robots that were able to handle disaster environments. So, we included that interest. And later on, @home was also introduced because people had an interest in service robots.

The logistics league came later to cover robots inside factories. The reason for introducing new leagues was always to include the community. And we didn’t want anyone to feel they could not come to RoboCup because they did different research with their robots. And so it was a very intellectually inclusive environment for AI and robotics researchers that could perform tasks autonomously – with perception, cognition, and action mostly in teams for particular tasks.

Rescue league arena at RoboCup 2024.

Could you talk about the changes that were announced last year, and the decision behind those?

Back in 1997, Hiroaki Kitano came up with this goal of a robot soccer team being able to beat the human World Cup winners by 2025. (I later tried, in the early 2000s, to rephrase that goal so that it was not about the robots beating the humans, but instead having robots playing alongside human players by 2050.) However, for this to happen, we have started the humanoid league so that the robots would have legs, not wheels. Our roadmap hence moves towards humanoid robots. It also happens that currently there are several robot companies who are producing humanoid robots that are easy to acquire, so researchers do not have the design and build their own humanoid robots, which was a difficult task that was pursued only by a limited group of researchers. In parallel, events started aiming at including humanoid robot soccer. We believed that RoboCup should be visible and a reference for humanoid robot soccer. We then proposed and changed that, in the international RoboCup competitions, robot soccer will focus solely on humanoid robots. That doesn’t mean that the other RoboCup leagues aren’t valuable. But if they continue at a regional level, there will still be venues to foster these other types of interests. The RoboCup international competitions would be solely focused on humanoid robot soccer.

Action from the humanoid soccer league at RoboCup 2025. Image courtesy of Alessandra Rossi.

RoboCup was tremendously successful in terms of including a variety of different autonomous robot research interests from the community. But I also believe that now we should move into just humanoid soccer at the international level. This will help with visibility of the competition and also for consolidation. We can now buy humanoid robots that we didn’t have before. Previously, you had to build them in your universities, and that was not a research direction for many people. But there is not that excuse anymore. You can buy humanoid robots, and increasingly, you can talk with these humanoid robots using GenAI. So it became a different challenge, a much more accessible challenge than before. The challenge will be: can these platforms play soccer in the presence of another team? I think it’s a fascinating new direction to try to focus on this challenge of the game of soccer – multiple players, two teams in a large space, eventually coordinating, collaborating, or playing with and against humans. So that’s the rationale.

When people don’t know the story, they tend to wonder why we have all these other leagues, such as rescue and logistics. It was always driven by the interests of the community and a big heart for a research approach. And we always thought that because we were organizing an event with a venue, that we could include these people that have interests outside of soccer. That has always been our drive. However, it’s true that it diluted a little bit the goal of 2050 – the humanoid soccer robots. Now I think it’s time to go back to soccer as a main focus, and still keep supporting the other interests that we have fostered, more at a regional level.

There are still a lot of details to be sorted out. And, of course, people may be upset by leagues terminating at the international level. I have always participated in the small-size league myself, 20 years of participating, and I am very attached to it. I was always the trustee representing the small-size league, and I am not sure I am happy with the small-size not being in the international event. But I need to think beyond what my personal interests are and try to understand what would have a bigger impact for AI, robotics, and RoboCup. I think that that’s demonstrating a group of humanoids playing soccer. And if we don’t do it at RoboCup, maybe someone else will.

The way I think about it is that we have made this decision and we can reevaluate it in five or ten years. But not making a change and never having the courage to deviate from what we have been pursuing is less exciting. Things have stalled a bit, with the same number of teams, the same people, the same rules, the same type of intellectual and research accomplishments. We need to excite people about something major again. So I think that the community will greatly embrace this new RoboCup international. And we are so well organized locally, that we can support other leagues at the local events.

I do think that, from a scientific and research point of view, it’s the right moment to target the soccer humanoid robots, because of their availability and our RoboCup ultimate goal. I think it would be amazing if people knew RoboCup international as the humanoid robot soccer competition. Can you imagine in 2027 having 100 teams all playing soccer with humanoids? We’ll see.

About Manuela Veloso

Herbert A. Simon University Professor Emerita, School of Computer Science, Carnegie Mellon University 2018-2026 Founder and Head, JPMorganChase AI Research, Managing Director

For the last eight years, Manuela Veloso has been the founder and Head of JPMorganChase AI Research and Herbert A. Simon University Professor Emerita at Carnegie Mellon University, where she was faculty in the Computer Science Department and then Head of the Machine Learning Department.

At JPMorganChase, she built a team of 100 top talented members with graduate education (PhD and Masters) in AI and related disciplines. The team focused on pillar areas of AI in finance, including data-driven optimization, planning and search, document analysis, trustworthy AI, AI and mathematical reasoning, continual learning, and multiagent systems. The team published their research in academic venues and addressed and contributed to business needs and vision.

Veloso has a licenciatura degree in Electrical Engineering and an M.Sc. in Electrical and Computer Engineering from Instituto Superior Técnico, Lisbon, an M.A. in Computer Science from Boston University, and a Ph.D. in Computer Science from Carnegie Mellon University. Veloso has Doctorate Honoris Causa degrees from the Örebro University, Sweden, the Instituto Universitário de Lisboa (ISCTE), Portugal, the Université de Bordeaux, France, and the Universidade Católica of Portugal.

She served as president of the Association for the Advancement of Artificial Intelligence (AAAI), and she is co-founder and a Past President of the RoboCup Federation. She is a fellow of main professional organizations in her area, namely AAAI, IEEE, AAAS, and ACM. She is the recipient of the ACM/SIGART Autonomous Agents Research Award, the Einstein Chair of the Chinese Academy of Sciences, an NSF Career Award, and the Allen Newell Medal for Excellence in Research. Veloso is a member of the National Academy of Engineering with a citation “for contributions to artificial intelligence and its applications in robotics and the financial services industry.” She is also a member of the Academy of Sciences of Portugal.

Her research interests are in AI, including Multiagent Systems, Autonomous Robots, Continual Learning Agents, and AI in Finance. For further details, see Manuela’s webpage.