You can't fix what you can't see.
You can't trust what you can't explain.
We've heard this from hundreds of AI engineers navigating the realities of production AI.
Three places production AI quietly breaks down — and why each one compounds.
Teams can see the logs, but not the context, rules, or evidence that shaped the decision.
Agent behavior degrades without an obvious failure — no error, no alert, just gradual drift.
Failures can come from a prompt, retrieved context, a failed tool call, an LLM session, or a handoff between agents.
Existing tooling wasn't built for this.
You can see what was sent to the model and what came back, which doesn't present a complete picture.
What's missing is the layer in between: the reasoning, the context and the accumulated instructions that connected those two points and shaped the path the system ultimately took.
A connected toolset that turns reasoning chaos into a structured loop.
Three surfaces, one purpose: give teams the data, the diagnosis, and the structured learning loop their existing tools don't.
Instrument your stack to capture decision records from agents, tools, and handoffs.
Inspect any run end-to-end — context, instructions, tools, handoffs, and outcomes — in one place.
Mark what went wrong, tag likely causes, and capture incidents worth turning into tests.
The people building this
Greg drives Prove AI's vision and architecture, with 20+ years of technology leadership across enterprise scale. He joined from Xendit, the Southeast Asian financial infrastructure startup, where he served as CTO, and has previously led teams at AWS and Experian.
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