The Big Con of Agentic AI

The results were surprisingly good, with a few important caveats.

Site reliability engineering is entering a new phase. As incidents become faster-moving, more data-rich and more complex, SRE teams are exploring agentic AI to help with alert triage, root cause analysis, runbook execution and mitigation planning. But in production, the question is not whether an agent can act; it is whether people can trust it to act safely, consistently and transparently when the system is under stress. This blog argues that trust is an engineering outcome, not a marketing promise. Trustworthy agentic SRE systems are built on a foundation of grounded telemetry, explicit safety boundaries, progressive autonomy, auditability and evaluation against real incidents. Why trust matters Traditional automation works well when the world is predictable. SRE work is different because incidents are messy, partial and time-sensitive, with ambiguous symptoms, shifting dependencies and business context that rarely fits into a neat playbook. A fluent AI agent that lacks system contex

SANTA CLARA, Calif., July 9, 2026 — Cloudera today announced the next phase of its partnership with global humanitarian and development organization Mercy Corps by unveiling Verified Evidence & Research […] The post Cloudera and Mercy Corps Deepen Partnership with Agentic AI Built for Humanitarian Response appeared first on AIwire.

The post Beyond Agentic AI: The Emergence Of Cognitive AI Ecosystems appeared on BitcoinEthereumNews.com. Beautiful Robot woman standing at the interactive table with hologram of smart city. Cyborg work with virtual abstract spherical interface. AI or artificial intelligence controls systems of smart city getty In the next decade, AI will likely undergo more significant changes than only becoming more independent; it will also grow more cognitive. AI systems will act as interconnected ecosystems that are capable of contextual awareness, cooperative reasoning, ongoing learning, and adaptive decision-making in almost every facet of society, rather than isolated applications. Large language models of today are remarkable due to their ability to produce and anticipate information. Persistent memory, multimodal perception, long-term planning, causal reasoning, and self-directed learning within strictly regulated bounds will probably be characteristics of the AI of 2036. Similar to biologica

CEA Anantha Nageswaran feels that artificial intelligence raises the value of each working professional rather than replacing them. Says, “Our goal should be to use machines so that our people are freed to do more of what only people can do.”

Building an agent in an afternoon is now within reach of almost anyone in the enterprise with a credit card. The tools are accessible, the deployments are easy. The hard part is delivering the intended results. Gartner predicts that more than 40% of agentic AI projects will be canceled by 2027, and the EU AI Act Article 14 requirements for human oversight for high-risk AI systems take effect on August 2, 2026. The deciding factor for whether agentic AI reaches production isn’t the model, the framework, or the use case. It’s the infrastructure beneath the agent: the part the people building agents have never had to think about. Organizations are racing to deploy agentic AI to stay competitive, which means pressure-testing is often overlooked. Every agent project should be scrutinized by three executives asking three different sets of questions. The CISO asks whether we are exposed. The CFO asks whether we are overspending. The chief AI officer asks whether we are getting value. As a pr

The post AxBlade × AWS Hong Kong Summit Wraps: Defining Accountability for Physical AI appeared on BitcoinEthereumNews.com. AxBlade, the accountability layer for autonomous AI, co-hosted the exclusive side event “From Agentic AI to Physical AI: What Gets Funded After the Model Wave?” alongside AWS Summit Hong Kong Week. Held at the Hopewell Hotel, the invitation-only gathering brought together 100+ founders, researchers, enterprise leaders, and investors from AWS, NVIDIA, Y Combinator, Crypto.com, Roche, Pfizer, SNZ, and City University of Hong Kong to examine the critical infrastructure gap between AI demos and real-world deployment. From Models to Accountability: The Consensus The event opened with a keynote by Nick Hau, Founder of AxBlade, who argued that the next wave of AI funding will not go to larger language models, but to the infrastructure that makes autonomous AI accountable in physical environments. This was followed by a keynote from Ian Holtz, Head of Agentic AI at AWS, o

The leaders who keep their edge will be the ones who resist the temptation to hand over judgments to machines.