Imagine this: An AI agent is left running overnight on a routine task, blocked from the fastest route to its goal by a sandbox rule. Instead of stopping, it finds another way through so it can fulfil its goal. It jumps over the one guardrail nobody thought to name. That's roughly the shape of the incident that put OpenAI's coding agent at the centre of an attack on Hugging Face earlier this year. This also brings to light a bigger question: what happens when the AI systems enterprises are racing to deploy start adapting on their own?
In the recent episode of The Security Strategist podcast, host Shubhangi Dua, podcast host and producer at EM360Tech, sits down with Chenxi Wang, Managing General Partner at Rain Capital, cybersecurity veteran of more than two decades, Fortune 500 board member and VentureBeat Women in AI Award winner. They talk about self-improving AI agents (SIA) and their more advanced cousin, recursive self-improving agents (RSIA), from what they are, why they're suddenly dominating research conferences, and why most enterprises, especially regulated ones, aren't remotely ready to run them without a human in the loop.
Wang highlights recursive self-improving agents as an emerging, cutting-edge technology. She notes attending this year's KDD conference in Jeju, South Korea, an event traditionally focused on data mining, where American scientist Jeff Dean delivered a keynote and a notable series of research papers focused on the topic.
"The difference between the recursive self-improving agents and self-improving agents is that the piece that's improving the agent, if that stays static, that's a self-improving agent. If that piece itself is getting improved as well, then that's a recursive self-improving agent."
Essentially, the substrate of self-improving AI agents is called recursive self-improving (RSI) in AI agents. “These recursive self-improving (RSI) AI agents have a huge impact on cybersecurity,” Wang tells Dua.
Are AI Sandboxes Enough to Secure Self-Improving Agent Behaviour?
The Hugging Face incident is the episode's anchor example. Wang uses it to make a point about agent design. She says a sandbox that only says "no" doesn't teach an agent anything except to look for another door. In that case, the agent was not itself a self-improving system. OpenAI’s AI Agent was hunting for the best way to reach its goal, and when the obvious path was blocked, it produced creative alternatives instead of stopping.
It’s similar to how a parent sends their child to pick up takeout, and you don't have to specifically ask them to jump the queue or vault the counter, because social norms fill in the gap. An agent has no such norms unless they're built in. Given a goal, the agent will do whatever accomplishes it fastest. This includes behaviour a human would never consider, because nobody told it not to.
“At the end of the day, the Hugging Face incident teaches us that relying on sandbox alone is not a very effective mechanism,” the cybersecurity veteran tells Dua. “We need to actually give an agent dynamically rich and in-depth feedback so that it can take action in a way that is more aligned with business outcomes.”
"And we've seen in some of the experiments that if you give an agent feedback like how AI provides feedback, it will provide a fairly extensive output,” she adds. If we provide that kind of feedback to the agent, it will improve its understanding of the environment and scenario, adjusting its behaviour to align better with business outcomes without violating policies.”
Relying on a sandbox alone isn't effective. Agents need rich, dynamic, in-depth feedback, closer to the kind of detailed output an AI would give another AI, so they can improve their understanding of the environment and align their behaviour with business outcomes rather than hitting a wall and improvising around it.
Are Self-Improving AI Agents Ready For Production?
Despite the research momentum, Wang is careful to separate hype from deployment reality. She agrees with an MIT study the host raises, confirming that self-improving agents, let alone recursive ones, haven't been seen in production at scale, with governance, trust, and the ability to support large-scale deployment while still enforcing policy and business outcomes cited as the open challenges.
Dua alludes to the MIT article on the study, Can AI agents conduct open-ended AI research? Early evidence from two case studies published in the journal arXivLabs. Sayash Kapoor, a researcher at Princeton University and co-author of the study, notes that AI progress might end up "bifurcated", racing ahead on scoreable tasks while stalling on genuine open-ended research.
The study can't tell us whether AI needs real creativity to keep improving itself, or if it'll get there just by grinding on tasks it can already check; no one knows yet. This is why Dua asks Wang about the key challenges associated with self-improving AI agents.
Wang says, “We haven’t seen self-improving agents, let alone recursive self-improving agents, in production.” While there have been some experiments here and there, they haven't been at scale. The challenges are governance, trust and deploying at scale.
“Self-improving AI agents may not yet be ready to support large-scale deployment. Ensuring that policies remain enforced and business outcomes are guaranteed while agents evolve dynamically are all active areas of work today.”
Where it's showing promise is in narrow, constrained settings. Lab drug discovery is one example Wang cites, where self-improving agents have delivered notably faster results than the study statistic on the table. However, nobody yet knows how that translates once the agents leave the lab for a real production environment.
For enterprises today, Wang's advice is to understand what a self-improving or recursive self-improving agent actually requires operationally. Essentially, they should check what environment it needs, what harness has to be built around it, and critically, whether the business is comfortable with a use case where there's genuinely no opportunity for human input. Self-improving agents typically remove the human from the loop, and recursive ones remove it entirely. By her own account, some of the most regulation-heavy enterprise workflows simply aren't ready for that yet.
What’s The Impact of Self-Improving AI Agents?
Dua asks what deploying self-improving AI agents looks like for the end-user. Wang reframes the answer around time efficiency: as systems evolve from copilots to self-improving agents, human oversight time decreases. However, success still depends on maintaining or improving business outcomes and efficacy without increasing user burden.
Self-improving agents chase goals relentlessly and will find their own way around a blocked path unless they're given rich feedback rather than a binary stop sign. Recursive self-improving agents take the human out of the loop entirely. It means governance has to come from continuous, auditable evidence rather than approval gates. "Rolling back" a bad decision often means the agent quietly re-routing toward the same goal, not undoing what it did.
Wang describes self-improving agents as a double-edged sword: with the right feedback they get smarter, better and more efficient; without it, they get worse, less aligned, and can even be pushed toward a malicious goal. Both outcomes are genuinely possible. Her conclusion for technologists is that governance and trust need to be built into the framework for innovation, not added after the fact.
For the C-suite specifically, Wang plays down the need to understand RSI mechanics in board-level detail. What matters more, she says, is understanding that agents are becoming steadily more capable and powerful, that competitors will use them even if you don't, and that leadership's job is to push the business to adopt the technology while simultaneously holding IT and security teams accountable for keeping policies enforced and provable, especially in regulated industries.
Key Takeaways
- RSIA updates the mechanism that improves the agent, not just the agent
- Sandbox "no" feedback pushes agents toward workarounds, not compliance
- Governance depends on evidence chains and observability, not approval gates
- Self-improving agents haven't reached production at scale
- Regulated industries aren't ready for zero-human-in-the-loop agents
- C-suites should focus on adoption pressure and provable policy enforcement
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