Agentic AI's Dirty Secret: It's Generating a New Kind of "Slop"

From Automation to Agentic AI: Confronting the Next-Gen "Slop" Challenge

The era of autonomous AI agents, systems that can execute complex, multi-step tasks without human oversight, is rapidly approaching.

According to a recent McKinsey report, nearly 40% of organizations are experimenting with AI agents, and almost a quarter are already scaling their use.

However, in the rush to adopt this technology, companies risk creating widespread inefficiencies through a phenomenon known as "agent slop."

What is agent slop?

Agent slop refers to poor-quality, often useless output produced by poorly designed or inadequately managed AI agents. Unlike "workslop", shoddy AI-generated work from individuals, agent slop can occur continuously and at scale, potentially creating operational chaos.

Matt Aaronson of Tonkean explains that as more employees build and deploy agents, the risk of accumulating low-quality automated work grows significantly.

Why does agent slop happen?

The causes are twofold. First, the underlying AI models are still limited, prone to hallucinations, and require high-quality, real-time data to function accurately.

Second, there is often a human factor: business leaders may push for AI adoption without providing adequate training, governance, or support, leading employees to deploy agents without proper design or oversight.

Why should businesses be concerned?

Internally, agent slop can undermine trust in AI, with surveys showing that a third of workers doubt agents' ability to produce high-quality work.

Externally, it can damage a company’s reputation, customer relationships, and even lead to legal issues if incorrect AI-generated advice is relied upon, as seen in Deloitte’s report containing AI-generated fake citations for an Australian government department.

How can businesses prevent agent slop?

To mitigate these risks, organizations should take proactive steps:

The future of AI agents

Despite these challenges, AI agents remain a transformative force. Surveys indicate organizations using them report increased productivity, cost savings, and faster decision-making.

However, experts like OpenAI’s Andrej Karpathy caution that realizing the full potential of agents may take a decade or more, as the technology matures and current limitations are addressed.

In the long run, those organizations that implement structure, governance, and thoughtful integration of AI agents will be best positioned to harness their value, while those that neglect these steps may find themselves overwhelmed by the very tools meant to drive their progress.

Langham Rolls Out Trio of AI Agents to Personalize and Streamline Guest Stays

Langham Hospitality Group (LHG) has launched three specialized AI agents designed to improve both guest services and staff operations across its global portfolio of hotels.

The new system introduces an AI toolkit that expands digital service options for guests while providing employees with instant reference and analytical tools.

The agents are being rolled out gradually across LHG's 31 properties, which operate under brands including The Langham, Cordis, Eaton Workshop, and Ying’nFlo.

The three AI agents serve distinct functions:

According to LHG CEO Bob van den Oord, these tools reinforce the company's core focus on personalized guest care, colleague development, and informed decision-making by adapting to modern preferences for instant, digital information access.

This move continues LHG's long history of integrating new technologies for efficiency and guest support, which dates back to its adoption of innovations like electric lighting and hydraulic lifts at its flagship London property in the 19th century.

More recently, the group has implemented smart check-in systems, intelligent climate controls, and integrated management platforms.

Qualys Bets the Future on AI Agents for Proactive Cyber Defense

Qualys has announced two significant innovations to its Enterprise TruRisk Management (ETM) platform, positioning it as the industry's first Agentic AI-powered Risk Operations Center (ROC).

These updates are designed to automate and accelerate cyber risk management.

The first innovation is a marketplace of Cyber Risk AI Agents. This marketplace allows organizations to deploy pre-built or custom no-code AI agents that automate specific security tasks and deliver targeted outcomes.

These agents function as a skilled digital workforce to augment security teams. The core agents include:

• Agent Nova: Automatically discovers external assets and vulnerabilities without manual effort, prioritizing scans based on high-risk indicators.

• Agent Vikram: Continuously monitors and autonomously remediates exposure gaps across multi-cloud environments.

• Agent Chang: Automates continuous compliance by collecting evidence and mapping it to frameworks like NIST and PCI-DSS.

• Agent Nyra: Provides threat-informed risk prioritization by monitoring adversary behavior and triggering automated responses.

• Agent Sara: Specializes in streamlining the entire Patch Tuesday process, from detecting critical vulnerabilities to applying patches or mitigations.

• Agent Sophia: Acts as a self-healing, autonomous system to discover, prioritize, and remediate vulnerabilities across the IT environment.

• Organizations can also build their own custom, no-code AI agents for specialized tasks.

The second innovation is the Cyber Risk Assistant, an intuitive, prompt-based interface. This tool translates complex exposure data into clear, actionable insights and can autonomously execute recommended actions, helping teams navigate risk management more effectively.

According to industry analysts, this integration of Agentic AI represents a major shift from reactive security to real-time, autonomous risk reduction.

It enables faster remediation, more efficient resource use, and allows security teams to evolve from tactical responders to strategic orchestrators of an AI-augmented workforce.

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