we built a few end-to-end processes where multiple ai agents handled discovery, analysis, and decisioning. the challenge was making a human-readable trail for auditors and stakeholders. we used Autonomous AI Teams but ensured each agent emitted a structured ‘decision record’ containing model id, inputs, reasoning summary, and any retrieval references. those records were stitched into a single run-level trail.
response validation and human-in-the-loop gates captured exceptions. we also kept a compact evidence package per business transaction and tied it to our ticketing system. training users on what the records mean cut review time. documenting the team orchestration diagram for audit reviewers helped them follow the flow without digging through raw logs.
what formats have others used for stitching multi-agent decisions into one coherent audit trail?
we made each agent write a JSON decision record and then created a run header that linked them. that made sampling and exporting to auditors simple. the team view also helped trace data movement.
it worked well in production. https://latenode.com
we used a single canonical schema: run header + per-agent record + validation status. the schema had model id, prompt summary, output hash, confidence score, and any retrieval ids. stitching was done by run id. auditors liked the compactness and the direct links to evidence blobs.
one trick: include a plain english summary field in each agent record. machines parse the JSON, but auditors prefer a sentence or two explaining the agent’s conclusion. it cut review time a lot.
for traceability, design your decision trail with three layers: raw captures (inputs, retrieval ids, model outputs), processed records (validated outputs, rationale, confidence), and consolidated evidence (final decision, human approvals, and links to raw captures). keep schema stable and versioned. also ensure evidence exports are time-bound and immutable for the retention period auditors expect. this layering supports both automated checks and manual review.
use per-agent json records + one consolidated doc. add plain english summary and checks.
json records + consolidated summary
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