Tag: WebDev

For the past year, enterprise decision-makers have faced a rigid architectural trade-off in voice AI: adopt a "Native" speech-to-speech (S2S) model for speed and emotional fidelity, or stick with a "Modular" stack for control and auditability. That binary choice has evolved into distinct market segmentation, driven by two simultaneous forces reshaping the landscape.What was once a performance decision has become a governance and compliance decision, as voice agents move from pilots into regulated, customer-facing workflows.On one side, Google has commoditized the "raw intelligence" layer. With the release of Gemini 2.5 Flash and now Gemini 3.0 Flash, Google has positione

It's refreshing when a leading AI company states the obvious. In a detailed post on hardening ChatGPT Atlas against prompt injection, OpenAI acknowledged what security practitioners have known for years: "Prompt injection, much like scams and social engineering on the web, is unlikely to ever be fully 'solved.'"What’s new isn’t the risk — it’s the admission. OpenAI, the company deploying one of the most widely used AI agents, confirmed publicly that agent mode “expands the security threat surface” and that even sophisticated defenses can’t offer deterministic guarantees. For enterprises already running AI in production, this isn’t a revelation. It’s validation — and a signal that the gap between how AI is deployed and how it’s defended is no longer theoretical.None of this surprises anyone running AI in production. What concerns security leade

Researchers at Google and MIT have conducted a comprehensive analysis of agentic systems and the dynamics between the number of agents, coordination structure, model capability, and task properties. While the prevailing sentiment in the industry has been "more agents is all you need," the research suggests that scaling agent teams is not a guaranteed path to better performance.Based on their findings, the researchers have defined a quantitative model that can predict the performance of an agentic system on an unseen task. Their work reveals that adding more agents and tools acts as a double-edged sword: Although it can unlock performance on specific problems, it often introduces unnecessary overhead and diminishing returns on others.These findings offer a critical roadmap for developers

While Silicon Valley debates whether artificial intelligence has become an overinflated bubble, Salesforce's enterprise AI platform quietly added 6,000 new customers in a single quarter — a 48% increase that executives say demonstrates a widening gap between speculative AI hype and deployed enterprise solutions generating measurable returns.Agentforce, the company's autonomous AI agent platform, now serves 18,500 enterprise customers, up from 12,500 the prior quarter. Those customers collectively run more than three billion automated workflows monthly and have pushed Salesforce's agentic product revenue past $540 million in annual recurring revenue, according to figures the company shared with VentureBeat. The platform has processed over three trillion tokens — the fundamental units tha

Unrelenting, persistent attacks on frontier models make them fail, with the patterns of failure varying by model and developer. Red teaming shows that it’s not the sophisticated, complex attacks that can bring a model down; it’s the attacker automating continuous, random attempts that will inevitably force a model to fail.That’s the harsh truth that AI apps and platform builders need to plan for as they build each new release of their products. Betting an entire build-out on a frontier model prone to red team failures due to persistency alone is like building a house on sand. Even with red teaming, frontier LLMs, including those with open weights, are lagging behind adversarial and weaponized AI.The arms race has already startedCybercrime costs reached $9.5 trillion in 2024 and forecasts exceed $10.5 trillion for 2025. LLM vulnerabilities contribute to that traje

Presented by EdgeVerveArtificial intelligence (AI) has long promised to change the way enterprises operate. For years, the focus was on assistants, systems that could surface information, summarize documents, or streamline repetitive tasks. While valuable, these technological assistants were reactive: they waited for human prompts and provided limited support within narrow boundaries.Today, a new chapter is unfolding. Agentic AI, whose systems are capable of autonomous decision-making and multi-step orchestration, represents a significant evolution. These systems don’t just assist, they act. They evaluate context, weigh outcomes and autonomously initiate actions, orchestrating complex workflows across functions. They adapt dynamically and collaborate with other agents in ways that are beginning to reshape enterprise operations at large. For leaders, this shift carries both opportunity and responsibility. The potential is immense, but so are the go

João Freitas is GM and VP of engineering for AI and automation at PagerDutyAs AI use continues to evolve in large organizations, leaders are increasingly seeking the next development that will yield major ROI. The latest wave of this ongoing trend is the adoption of AI agents. However, as with any new technology, organizations must ensure they adopt AI agents in a responsible way that allows them to facilitate both speed and security. More than half of organizations have already deployed AI agents to some extent, with more expecting to follow suit in the next two years. But many early adopters are now reevaluating their approach. Four-in-10 tech leaders regret not establishing a stronger governance founda