How do you know an autonomous agent got better rather than just got lucky? Agent behavior is not deterministic, so the usual software testing instincts quietly mislead you.
Three pillars: a benchmark of real enterprise integration work, a production-like IDE environment to run in, and an evaluation layer that reads execution traces rather than final answers. The unit of measurement becomes the distribution over repeated runs, not the run — and evaluating agents differs from evaluating software because what matters is how they reason, recover, and stay consistent.
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Building generative AI features has become routine, yet the overwhelming majority of pilots never produce measurable business value. Something between the demo and production is being skipped.
Validity and correctness are different problems, and correctness is the one that matters. A generated flow can be syntactically perfect and still miss the business logic completely. Grounding, validation, error correction and an LLM judge move roughly 20% and 17% to roughly 90% and 80%.
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How do you generate correct code in a low-resource proprietary language that a general model has barely encountered, where the user often cannot describe the transformation precisely anyway?
Sample input and output pairs are a better specification than schemas, and they double as an executable oracle: compile the script, run it on the input, compare against the expected output. That turns error correction into a closed loop over a taxonomy of failure modes rather than a blind retry.
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How does a vague, multi-turn conversation become a working integration flow across an ecosystem of hundreds of connectors and thousands of operations?
Intent has to be consolidated before retrieval, not after. Summarize the whole conversation into one coherent prompt, then ground it by retrieving the relevant connectors, operations and examples — because retrieval quality is bounded by how well you understood the ask in the first place.
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