anup_sia

Yes, we have an SDK for interacting with cells. It's open source: https://github.com/oncellai/oncell

That's the whole reason I am building justcopy.ai. core philosophy is that just reuse existing product grade software.

LLMs are great at creating plans but terrible at following them. I've seen agents claim to create 5 files but only make 2, repeat API calls 3x, skip error handling, then report success anyway. The fix: treat execution like todo management—track every step, block the agent if it tries tools not in the current step, and verify completion (don't trust its word, actually check if the file exists). This plus guardrails and git-like versioning improved the reliability siginificantly

After shipping 100+ AI agents to production, we learned that making them reliable isn't about better LLMs—it's about better architecture.

The solution: task-driven design. Give agents mandatory todo lists with validation gates. Each task needs clear completion criteria and evidence. Agents cannot finish until every todo is verified complete.

This simple pattern prevents 80% of production failures. Structure beats intelligence.

see it in action at justcopy.ai

We built what should have been the dream team of AI agents—eight specialists covering the entire development lifecycle. But give them an open-ended project and they'd spin in circles asking the same questions. The solution wasn't in the models, but in how humans work best: with clear finish lines. After watching our agents flounder for months, we realized they needed the same thing effective teams do: concrete goals with defined boundaries. When we stopped saying "build an e-commerce site" and started assigning specific deliverables with measurable completion criteria, they transformed from constantly confused assistants into capable, autonomous specialists. The same systems with the same capabilities suddenly performed at an entirely different level, simply because we changed how we structured their work.