AI agent

How Generative and Agentic AI Shift Concern From Technical Debt to Cognitive Debt

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Generative and agentic AI shifts focus from technical debt (issues in code) to cognitive debt (loss of shared understanding among developers). Cognitive debt accumulates as teams rush, leading to confusion about design decisions and system functionality. It's crucial to recognize that speed without comprehension is unsustainable. Teams must establish strategies to mitigate cognitive debt, such as ensuring at least one team member understands AI-generated changes, documenting reasoning, and promoting shared knowledge through regular reviews. Recognizing signs of cognitive debt is essential for long-term software health, especially as AI becomes more integrated into development processes.

https://margaretstorey.com/blog/2026/02/09/cognitive-debt/

How the Growing AI Workforce Is Changing the CIO Role

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CIOs are evolving to manage hybrid teams comprising humans and AI agents, shifting from tech managers to workforce orchestrators amidst the rise of AI in businesses. AI agents help automate repeatable tasks in IT and operations but require clear governance and careful implementation to ensure accountability and effectiveness. CIOs must strategically assess which tasks suit AI, focusing on low-risk, high-effort responsibilities. Measuring AI agent productivity involves more than cost—considering accuracy, reliability, and overall value is crucial. Challenges include governance, talent management, and fostering organizational change to embrace AI integration.

https://www.cio.com/article/4126383/how-the-growing-ai-workforce-is-changing-the-cio-role.html

Stop Managing Your AI ‘workforce’, Start Allocating AI Capabilities

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The rise of AI agents capable of complex workflows presents a pivotal moment for organizations. While many view these agents as digital co-workers, this framing limits their potential. Instead, organizations should focus on how these capabilities can reshape workflows and decision-making structures, leading to greater productivity and competitive advantage.

https://www.kyndryl.com/us/en/institute/2026/01/ai-workforce

Аgentic AI Security Measures Based on the OWASP ASI Top 10

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The OWASP Foundation released a playbook outlining the top 10 risks of deploying autonomous AI agents, including goal hijacking, tool misuse, and privilege abuse. These risks arise from the agents’ ability to make decisions and process data without human oversight. Mitigation strategies include enforcing least autonomy and privilege, using short-lived credentials, and requiring human confirmation for critical actions.

https://www.kaspersky.com/blog/top-agentic-ai-risks-2026/55184/

Who Approved This Agent? Rethinking Access, Accountability, and Risk in the Age of AI Agents

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AI agents boost productivity by automating tasks, but their rapid deployment complicates accountability, creating security risks. They bypass traditional access models, accumulating broad permissions without clear ownership. Three types of agents exist: personal (user-owned, low risk), third-party (vendor-owned, moderate risk), and organizational (shared, high risk). Organizations must rethink risk management, establish clear ownership, and map user-agent interactions to avoid authorization bypass problems. Unmanaged AI agents represent significant risks due to their autonomous nature and unclear responsibilities.

https://thehackernews.com/2026/01/who-approved-this-agent-rethinking.html

8 CIO Recommendations for ERP Implementation in 2026

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Agentic AI is transforming ERP systems, enabling new operating models and improving agility, efficiency, and customer responsiveness. By 2026, CIOs should develop a business plan for agentic AI adoption, engage with ERP vendors, and define a human-AI collaboration strategy. They must also address risks, readiness gaps, and talent needs while planning for change management and communication strategies.

https://www.informationweek.com/software-platforms/8-cio-recommendations-for-erp-implementation-in-2026-think-agentic

Securing Agentic AI: Architecture, Patterns, and Governance for Enterprise Adoption Part-1

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Agentic AI systems perform actions beyond just returning text, introducing operational risks. Key concepts include levels of autonomy, risks associated with agent actions, and the importance of monitoring and governance. Agents operate on a loop of perceiving, reasoning, acting, and observing, making security critical at each step. There are various trust boundaries when interacting with tools and data. To mitigate risks, architectures should implement a “Guarded Agent Loop” with layers for input processing, policy awareness, tool proxies, and output validation. Real-world examples illustrate the need for strict controls to prevent unauthorized actions and ensure compliance.

https://www.subhashdasyam.com/2025/12/securing-agentic-ai-architecture.html

How to Make AI Agents Reliable

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AI agent reliability requires focusing on simple, constrained tasks rather than complex, autonomous functions. Most failures stem from agents' unpredictability, making them unsuitable for enterprise use. To improve reliability, enterprises should establish limited scopes, enforce governance, and maintain strict memory controls. Successful AI applications in enterprises are those that augment human work, not replace it, thereby gradually building trust and enhancing usability. Focusing on reliable and “boring” engineering ensures scalability and effectiveness in AI deployments.

https://www.infoworld.com/article/4112542/how-to-make-ai-agents-reliable.html

How Microsoft Is Betting on AI Agents in Windows, Dusting Off a Winning Playbook From the Past

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Microsoft is reviving Windows as a platform for AI agents, similar to its past strategy that established dominance in the PC market. A new framework called Agent Launchers allows developers to integrate autonomous assistants into Windows, facilitating tasks like scheduling and document management. However, this initiative raises security concerns and operates in a more fragmented tech landscape compared to the past. Despite challenges, Microsoft aims to leverage these AI capabilities to boost Windows' relevance and revenue amid competition from mobile and cloud platforms.

https://www.geekwire.com/2025/how-microsoft-is-betting-on-ai-agents-in-windows-dusting-off-a-winning-playbook-from-the-past/

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