You don't need a better agent. You need a better debug loop.
I've been building agentic systems long enough to have a stack of burned-out GPU hours from watching agents fail in ways I couldn't reproduce. The pattern is always the same: the agent does something unexpected, the logs are too sparse, and by the time you add more logging the moment is gone. You end up guessing.
That's why the thing I actually reach for these days isn't a fancier model — it's a debugger that lets me replay the whole thing.
There's a new open-source desktop app called llm-space that does exactly this. You prototype an agent idea, run it, and then inspect every step of the harness — tool calls, outputs, state transitions — in a replayable timeline. When something fails, you don't squint at a wall of text. You step through the failure like you would a unit test.
The part that sold me: it replays failures. Not just logs them — replays them, so you can watch what the agent actually did, step by step, and figure out where the reasoning chain went off. That's the difference between "I think the agent is broken" and "I know the agent called the wrong tool because the prompt didn't specify the output format."
It's local-first, which means your data stays on your machine, and it has a cloud mode for managed agents when you need to scale. But the real value is the local loop. Fast iteration, no round-trip to a server, no waiting for a log aggregator to catch up.
Here's what I'd try first with it: take your most flaky agent — the one that works 80% of the time and you can't figure out why — and run it through llm-space. Replay the failures. I bet you find the pattern in under ten minutes.
The tooling around agents is finally catching up to the complexity of the systems we're building. We spent the last two years getting models to be good enough. Now we need to get them to be reliable. That starts with being able to see what they're doing.