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Articles by Tag: Agentic AI

Your AI Bill Is Late Evidence
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Your AI Bill Is Late Evidence

Agentic AI cost control is moving past budget caps, usage dashboards, and generic FinOps reporting. The harder problem is that spend is generated inside the dynamic execution paths of context expansion, retrieval, tool calls, retries, verification loops, model routing, and human rework.
When AI Becomes a Metered Service, CIOs Need More Than a Budget Cap
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When AI Becomes a Metered Service, CIOs Need More Than a Budget Cap

A budget cap can stop a bill from crossing a threshold. However, it cannot tell a CIO which workloads should use premium models, which prompts are wasteful, when caching matters, whether long context is necessary, or which business unit is consuming AI because usage is easy rather than because it improves an operating result.
From Autonomy to Accountability: Managing Agentic AI Risks
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From Autonomy to Accountability: Managing Agentic AI Risks

Agentic AI shifts automation from single-task models to autonomous decision-makers, amplifying risks of misalignment, bias, and data leakage. OWASP’s new guidance equips SMEs with lifecycle security practices, ensuring governance, transparency, and resilience as autonomous agents move from experimentation into production. IT leaders and CISOs should read this article to learn how to secure agentic AI in production using OWASP’s guidance.
Unlocking Agentic Commerce: A New Revenue Shift for Retail SMBs
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Unlocking Agentic Commerce: A New Revenue Shift for Retail SMBs

Agentic commerce is shifting online purchasing from human-driven interfaces toward AI-mediated workflows. For SMEs, the opportunity lies in controlled agent access, not full automation. CIOs and CTOs should use this to guide early choices on agent access, operational controls, and governance as commerce workflows automate.
Collaborative Intelligence: Multi-Agent AI in Software Development
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Collaborative Intelligence: Multi-Agent AI in Software Development

AI coding assistants have provided great benefits for software development. Many developers have also turned to multi-agent workflows for coding that use specialized agents that collaborate to tackle complex tasks faster during software development. IT leaders and developers must carefully consider balancing complexity, cost, and strong governance when employing multi-agent workflows for coding; otherwise, this approach will fail.
The IAM Blind Spot: Governing Non-Human Identities at Machine Speed
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The IAM Blind Spot: Governing Non-Human Identities at Machine Speed

Agentic AI is exposing the limits of human-centric identity and access management. As non-human identities multiply and act autonomously, legacy IAM models break. For CIOs, CISOs, and senior IT leaders, the issue is no longer whether this shift matters, but whether existing IAM models can withstand autonomous agents operating at scale and speed.
Chatbots at Checkout: The Rise of LLM Commerce
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Chatbots at Checkout: The Rise of LLM Commerce

AI vendors and payment platforms are weaving checkout into LLMs so users can buy flights, clothes, and more without leaving the chat window. In the future, consumers will make retail decisions based on LLM results rather than web searches. Tech leaders must help their businesses get ahead of the LLM checkout wave or risk being left behind.
Fighting AI-Powered Threats with AI-Powered Defense
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Fighting AI-Powered Threats with AI-Powered Defense

As cyberattacks become faster and more AI-driven, security teams need new tools to keep up. Read this article to explore key use cases of Agentic AI in SOCs and gain practical guidance on how to integrate it into your security operations.
Machine Customers: Your Digital Concierge for Business Transactions
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Machine Customers: Your Digital Concierge for Business Transactions

Early adopters will use machine customers to conduct transactions semi-autonomously and autonomously for business by 2028 and 2032, respectively. If businesses selling goods and services continue to only focus on human customers then these machine customers will be missed and acquired by competitors. For retail businesses to stay competitive and increase profits, IT leaders and Customer Experience (CX) experts must plan to target machine customers.