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For a long time, building agentic applications meant working in Python. That’s where the ecosystem was, that’s where the tools were. But for the millions of developers working in TypeScript every day, this created friction. They had to switch languages, learn the abstractions, or build custom scaffolding just

As AI agents become more complex, integrating memory, calling external tools, and reasoning over multi-step tasks, debugging them has become increasingly difficult. Traditional observability tools were designed for simple prompt-response flows. But in agentic workflows, failures can occur at any point: a broken tool, stale memory, poor context interpretation, or