Iceberg Framework was created to make LLM driven workflows predictable, validated, and safe to run in real systems. But every team and developer faces different challenges when working with AI.
I m curious to hear from the community:
What problems do you struggle with when building agentic workflows?
Where do LLMs behave unpredictably in your projects?
Do you need validation, reproducibility, or auditability in your AI pipelines?
Which parts of your current AI stack feel fragile or hard to control?
What would you want Iceberg to help you with next?
Your insights will help shape the roadmap and highlight real world use cases that matter most.
Hi everyone 👋
I’m Andrii, the solo maker behind Iceberg Framework.
What is Iceberg Framework?
Iceberg is a deterministic execution layer for LLM‑powered systems and agents. It makes AI workflows predictable, validated, and production‑ready — even when using non‑deterministic models like GPT or Claude.
Most AI systems break because LLMs hallucinate, produce inconsistent outputs, or silently fail. Iceberg solves this by enforcing:
structured execution (plan → validate → execute → verify → log)
typed, schema‑validated outputs
safety & governance layers
controlled autonomy
full auditability and reproducibility
In simple terms: Iceberg turns an LLM from a creative assistant into a reliable software component.
Who is it for? Engineers and teams building:
agentic workflows
automation pipelines
AI‑powered product features
internal tools
enterprise‑grade AI systems
If you need LLMs to behave predictably — not improvise — Iceberg is built for you.
Happy to answer questions, share examples, or dive deeper into how Iceberg works under the hood!