---
title: "Why Agent Demos Keep Failing in Production"
canonical: "https://current.tinyfish.ai/issue/2/foundations/article/4077/why-agent-demos-keep-failing-in-production"
publication: "TinyFish"
publisher: "TinyFish"
language: "en-US"
guide: "https://current.tinyfish.ai/llms.txt"
issue: 2
section: "foundations"
section_name: "Foundations"
published: 2025-11-04
word_count: 155
source_links: 0
---

# Why Agent Demos Keep Failing in Production

*November 4, 2025*

[Read on the website](<https://current.tinyfish.ai/issue/2/foundations/article/4077/why-agent-demos-keep-failing-in-production>)

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.

## More

- [Foundations — Issue 2](<https://current.tinyfish.ai/issue/2/foundations/markdown>)
- [Issue 2](<https://current.tinyfish.ai/issue/2/markdown>)
- [Publication guide](<https://current.tinyfish.ai/llms.txt>)