Claude office agents let Claude share context across Excel and PowerPoint through a single toggle. Here is what the feature actually does, what the Skills framework changes, and the security gaps you need to know about before enabling it.
Stop thinking 90 days will complete your COBOL to cloud migration. Utah took 18 months and AWS Transform cut Toyota timeline by 50%. Use that time to prove legacy modernization works, build organizational confidence, and create momentum for the multi-year transformation ahead.
Multi-agent AI systems promise specialized intelligence but deliver exponential complexity. Salesforce research shows agents achieve only 58 percent success on single tasks and adding orchestration doubles the failure rate. Most mid-size companies need one capable agent, not coordinated swarms.
Over 40% of agentic AI projects face cancellation by 2027 due to poor problem selection. Companies like Ciena and IBM show where autonomous agents deliver real value. Here are the specific use cases that work and when to skip agents entirely.
OpenAI GPT-4o failed 91.4 percent of office tasks in testing. Reliable AI agents require engineering discipline over model brilliance, with proven patterns like circuit breakers and error budgets that turn prototypes into trusted production systems.
Most AI roadmaps focus on capabilities and features when they should focus on reliability and failure modes. RAND Corporation found more than 80% of AI projects fail before production, and only 7% of organizations have achieved full AI scale. Your roadmap must prioritize reliable agent patterns over impressive demos. Start with constraints, measure operational health, and plan for continuous iteration.
OpenAI Assistants API packs stateful conversations, code execution, and document search into one package. Built production systems with it and found the complexity rarely justifies the cost. With deprecation coming August 2026, here is when it is worth using and when simpler alternatives win for chatbots and automation.
After multiple attempts at autonomous workflows, the pattern is clear - they work brilliantly for decisions, fail miserably for processes. Over 40 percent of agentic AI projects face cancellation. As Beazley Insurance and Uber show, prerequisites matter more than technology.
AI agents wreck environments in loops, as Solomon Hykes put it, while burning through thousands in tokens. But the real failure? Feedback systems that collect input then do nothing. Here is what actually works in production.