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ORP is an open-source tool that wraps AI agent processes and automatically captures failures, then converts them into three executable artifacts—a retrievable Lesson, a regression test (Eval), and a preventative rule (Guardrail). In a 10-trial, 100-run experiment, agents without ORP succeeded 14% of the time; with ORP lessons delivered via a protocol standard called MCP (Model Context Protocol), the same agents achieved 100% success and zero repeat failures.
Why it matters
Agent builders today have no standard way to learn from failures or share lessons across different AI systems. ORP fills that gap by distinguishing observed facts (tool output, test results) from unproven agent claims, then encoding lessons as runnable tests rather than text. This makes failures reusable and measurable, reducing the cost of debugging and improving reliability.
What to watch
ORP extends OpenTelemetry (an industry trace standard) and delivers lessons through MCP tools, meaning compatible agents can automatically query and apply lessons without modification. The tool is available now via pip install open-reflection-protocol and requires Python 3.10+; the full demo and 58 unit tests are in the public GitHub repository.
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