AI Agents Need a New Observability Model
Why AI agent observability must measure intent, experience, outcomes, and cost alongside traditional application performance.
From Vibe Coding to Governed VibeOps: Fabrix.ai Targets the Next Phase of Enterprise Operations
Fabrix.ai is introducing a multi-vendor, governed VibeOps operational intelligence platform designed to help IT, network, security and site reliability teams create operational dashboards, agents and automation workflows using natural-language instructions. The company is also adding domain-specific small language models, or SLMs, intended to improve the accuracy, performance, privacy and economics of operational AI. The announcement […]
The New Measure of Cyber Recovery: Can the Business Get Back to Work?
A cyberattack is more likely to interrupt today’s enterprise than the natural disasters business continuity programs were originally built around. Enterprises still need to prepare for fires, floods, earthquakes, power failures and other physical events. But they are more likely to experience a cyber incident that disrupts business operations by affecting employee productivity, taking applications […]
324 | Breaking Analysis | From Tokenmaxxing to Sovereign Alpha: Who Controls Your AI Economics?
The AI industry wants enterprises to measure progress in tokens, model calls and usage. But those are largely vendor-revenue metrics—not enterprise-value metrics.
Canva shows why.
On August 6th, The Information reported that Canva cut its 2026 revenue-growth forecast from 30% to 20% because its AI features cost far more to run than expected.
Let that sink in: a company generating more than $900 million a quarter – and growing above 25% – lowered its outlook because of an input cost.
Canva said it had relied too heavily on expensive third-party frontier models. The fix was not a negotiated vendor discount. It rebuilt the stack with in-house models, Leonardo.AI and task-level routing – reportedly cutting the cost of an AI task by roughly 90%. Its video and image models were reportedly 17 and 30 times cheaper than frontier alternatives.
Your CFO is not buying tokens. The enterprise wants outcomes.
AI Customer Experience Depends on Orchestration, Not Automation
Why AI customer experience success depends on orchestrating automation, human expertise, governance, and workflows.
Black Hat 2026: Governing AI Agents From Access to Action
Enterprise AI is moving from answering questions to taking action. Agents can access data, invoke tools, call APIs and execute tasks across business processes. They can act on behalf of employees, interact with other agents and operate at a speed and scale that conventional human-centric security models were not built to handle. This requires a […]
323 | Breaking Analysis | Did Jensen just make the AI buildout too big to fail?
Nvidia is no longer just selling technology. It is helping create a financial asset class around AI compute. In our last Breaking Analysis, we argued that AI can be technologically transformative and still produce a capital bubble. Our thesis was simply that the bubble pops if deployable supply grows faster than monetizable demand – and […]
What Black Hat 2026 Revealed About the Future of Security Operations
What Black Hat 2026 Revealed About the Future of Security Operations Cybersecurity has always been a race against time. Black Hat 2026 made it clear that AI is putting new pressure on the clock. The industry’s answer to growing risk has long included more visibility, more detection and more specialized tools. Those investments generated valuable […]
Oracle APEX’s AI Bet: Generate Less Code, Deliver More Control
It’s well accepted that AI has changed the economics of software development. Code is plentiful. Leading coding agents can generate JavaScript, Python, Java and other software artifacts at remarkable speed at far lower costs. But more code does not necessarily produce better enterprise applications and outcomes.
We believe the enterprise challenge is shifting from code creation to application control. Organizations must determine whether AI-generated applications are secure, explainable, maintainable and consistent with existing governance practices. They must also decide who will maintain the generated software after the initial prototype becomes a production system.
Oracle’s new direction for APEX addresses this problem through architecture rather than through another proprietary AI assistant.