AI tools promise efficiency but deliver escalation. UC Berkeley researchers Aruna Ranganathan and Hatim Rahman found that AI creates a workplace Jevons Paradox: faster task completion leads to more tasks, higher standards, and burnout. 77% of AI-using employees say their workload increased. This post breaks down the research and offers concrete strategies to escape the trap.
Oracle released 16 purpose-built AI agents across marketing, sales, service, and field service inside Fusion Cloud CX, included at no additional cost. The pricing move directly undercuts Salesforce Agentforce’s $2-per-conversation model. Here is what the agents actually do, how they compare to the competition, and what the unified data layer means for enterprise CX stacks.
Professional services firms are adopting agentic AI faster than nearly every other sector. AI adoption in accounting firms jumped from 9% to 41% in a single year. 92% of legal professionals now use at least one AI tool daily. The reasons are structural: billable-hour economics make ROI obvious, document-heavy workflows are a natural fit, and clients are demanding it. Platforms like Intapp Celeste, Harvey, and Thomson Reuters CoCounsel are purpose-built for these regulated industries.
Every major RPA vendor rebranded around agentic AI in the past 18 months. UiPath posted $1.85 billion in ARR and its first full year of GAAP profitability. Automation Anywhere shipped a Process Reasoning Engine. Blue Prism is launching WorkHQ in April 2026. But Forrester says fewer than 15% of firms will activate agentic features this year. This post cuts through the vendor marketing to help enterprise buyers figure out what actually changed in the products, where the rebrand is just a label swap, and when the math favors switching.
AI avatars are moving beyond chatbots into visual, face-to-face interactions with candidates. D-ID Agents conducts real-time video interviews, HireVue has processed 70 million AI assessments, and Synthesia powers onboarding videos at Fortune 100 companies. But the ACLU has already filed discrimination complaints. This post covers how avatar-based HR agents work, who is deploying them, and what the legal landscape looks like in 2026.
AI-generated code still introduces a vulnerability in roughly 44% of security-relevant coding tasks: Veracode’s July 2026 report puts the average security pass rate at 56%, essentially flat since 2025. Georgia Tech has traced 74 published CVEs to AI-authored commits, and on the SusVibes benchmark the best coding agent wrote secure code only 11.8% of the time. This post breaks down the 2026 data, the vulnerability classes that keep showing up, and the controls that actually catch them.
A Cockroach Labs survey of 1,125 technology leaders found that 83% expect their data infrastructure to fail without major upgrades within 24 months, and two-thirds estimate AI-related downtime costs exceed $100,000 per hour. This post breaks down where systems break first, why traditional scaling approaches cannot keep up with agentic AI workloads, and what CIOs who are ahead of the curve are doing differently.
The European Data Protection Supervisor published its TechSonar 2025-2026 report identifying agentic AI as a top emerging technology risk. Combined with the EDPS risk management guidance and revised generative AI orientations, developers now face a concrete checklist from Europe’s highest data protection authority. This post breaks down the 12 risks the EDPS identified, the practical controls they imply, and how this guidance interacts with the EU AI Act deadline in August 2026.
Four payment protocols now compete to let AI agents spend money autonomously: Google AP2 for authorization mandates, Coinbase x402 for crypto micropayments, Stripe/OpenAI ACP for checkout flows, and Stripe/Tempo MPP for session-based billing. They are not competitors. They are layers in a stack. This post breaks down the architecture, explains what Google AP2 actually does differently, and gives builders a decision framework for picking the right protocol.
Keygraph’s Shannon is an open-source AI agent that autonomously finds and exploits web application vulnerabilities with a 96% success rate on the XBOW benchmark, outperforming XBOW’s own proprietary agent (86%). It uses a hierarchical multi-agent architecture with a planner and specialized executor sub-agents. This post breaks down the technical architecture, the benchmark results, the dual-use controversy, and what defenders need to do right now.