

micro1
1.1K posts

@micro1_ai
The AI platform for human intelligence












Most enterprises think non-deterministic AI outputs mean they can't trust agent workflows. Andrew Maas, VP of AI at @micro1_ai, disagrees and explains exactly how to engineer reliability into agentic systems on the latest Partner Podcast with our CTO @BenAtBox. Timestamps 02:54 What micro1 does and the role of human experts in AI systems 04:13 Rise of multi-step agentic workflows and domain-specific AI capabilities 07:48 Limits of current models and the need for deeper domain expertise 08:12 One-shot vs multi-step AI reasoning and why it matters 10:07 Composing multiple LLM steps to create reliable enterprise workflows 13:22 Variability in LLM outputs and concerns about enterprise reliability 18:54 Files as the new interface between humans and AI agents 22:24 Using evals and human review to improve AI systems in production 26:30 Experiment and challenge assumptions about AI limits






Introducing Prospera: a benchmark that tests AI agents on real federal tax returns, designed by our research team in collaboration with CPAs and industry-leading tax professionals. A complete federal return requires dozens of source documents, hundreds of interdependent calculations, and no room for errors. We evaluated GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro with no hints on which forms to file, scored against 20+ expert-authored criteria per return. Here’s the Results (Pass@3): -GPT-5.4: 28% -Gemini 3.1 Pro: 18% -Claude Opus 4.6: 16% To put those numbers in context, the tasks in Prospera weren't obscure edge cases. Filing a federal tax return is something millions of Americans do every year, yet 44% of evaluation criteria failed across all models. Full report linked in the comments.




