I keep seeing the same thing: agents that shine in demos fall apart when you run them repeatedly. Superface's benchmarks tell the story. Simple CRM tasks like creating Salesforce leads or updating HubSpot pipelines fail 75% of the time when agents chain them together. Single actions might work half the time. String six together and you're looking at 10-20% success rates.
Carnegie Mellon found even the best models complete only 30% of office tasks autonomously. The arithmetic is brutal. A 20% error rate per action means a five-step workflow has a 32% chance of working end-to-end.
Companies are building agents without evaluation infrastructure. They're treating probabilistic systems like deterministic code. What actually works: build your eval framework before your agent. Design for graceful failure from day one. Use specialist agents handling 10-20 tools maximum instead of one super-agent trying to do everything. The demo-to-production gap isn't something to solve. It's a constraint to design around.