Multi-step AI agents with a human approval gate. Sasa parses unstructured documents, reasons against domain criteria, scores matches, and executes actions only after explicit human approval — the agent-decomposition and approval-workflow pattern that makes AI output auditable rather than a black box.
A meaningful AI agent is not a single prompt; it is a directed graph of reasoning steps, each with its own tool surface and context. Decomposing the work this way is precisely what produces a defensible, auditable system — every output traceable to the step and the source that produced it.
The workflow split into discrete steps — each with its own tool surface and context — so every output is traceable to the step and the source that produced it.
Layout-aware parsing of PDF, Word, HTML, and plain text into a normalised internal representation — the same problem shape as multi-format document ingestion.
Anti-fabrication verification against the source, and a review surface where every agent-drafted output is approved by a human before anything is submitted.
In production: 4,050+ opportunities scored (72–90% match confidence) across Greenhouse, Workable, Lever, and Ashby. usesasa.xyz →
Decomposed, auditable, and gated on human approval — automation you can defend.
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