Every payment provider has its own error vocabulary. Error 99-X means one thing on Pix, something else on a card processor. This agent searches provider docs, past confirmed incidents, and a self-improving fix library, then presents an interpretation with confidence score for human sign-off.
Concept stage. Built as part of a payment processing AI talk. Click any step to see what it does.
Confidence score and historical precedent, every time. The agent never guesses without evidence. A fix confirmed 21 times outranks documentation written two years ago. The fix library improves with every decision.
Monitors incoming decline codes per provider. When the same error code appears more than a defined number of times per minute from a specific provider, or when a new code appears with no match in the fix library, the pipeline starts.
A single unknown error is noise. A cluster is a signal. The threshold is configurable per provider, since some providers return a narrow set of codes frequently while others return diverse codes rarely.
Pulls the full context around the error: raw error string, provider identifier, payment method, country, transaction amount, and any relevant request or response payload fragments.
The same error code means different things across different providers and payment methods. Error 99-X on Brazil Pix is not the same as Error 99-X on a card processor. Context is what allows the search to narrow to the right provider's documentation rather than returning generic results.
Semantic search through ingested provider documentation: PDFs, API references, integration guides, error code tables. The search uses embeddings, not keyword matching, so related terms find each other even without exact string overlap.
Documents are chunked, vectorised, and stored in a vector database - Qdrant, Pinecone, or similar. The quality of results depends directly on the quality and coverage of ingested documentation. Better source material means better matches.
Queries the confirmed incident history for errors from the same provider that match the current pattern. This surfaces prior human-confirmed resolutions, not just what the documentation says, but what actually worked in production.
The history includes confirmation weights: an error confirmed 20 times carries more weight than one confirmed once. A provider-specific incident from three months ago that matches the current error is often the most useful signal in the entire pipeline.
The fix library is the growing database of approved corrections. Every approved fix gets promoted here with the engineer's name, timestamp, and a confidence weight. It is queried before documentation and incident history, because a confirmed fix from last month is more reliable than a documentation paragraph.
If a match is found here with high confidence, the agent's job is mostly verification rather than discovery; it checks whether the fix still applies given the current error context.
The only inference step. The agent receives the error context plus results from all three data sources - documentation, incident history, fix library - and produces a plain-language interpretation, a proposed fix, a confidence score, and the historical precedent it drew from.
If confidence is below a set threshold, the agent surfaces the ambiguity explicitly rather than presenting a low-confidence result as certain. 23 previous incidents matched this pattern, 21 confirmed as postal code formatting, 2 were unrelated is more useful than a confident wrong answer.
Human confirmation via Slack. Two paths.
A predefined wrapper is applied to the outbound request mapper. No AI writing code: the wrapper is pre-written normalisation logic that has been reviewed before being added to the library. The mapping is promoted to the confirmed fix library with the approving engineer's name and timestamp attached.
The engineer's correction is captured: what the error actually was and what the correct fix is. Stored as a labeled counter-example. A denied interpretation with a correct label is one of the fastest ways to improve accuracy for that provider specifically.
A predefined wrapper is applied to the outbound request mapper. No AI writing code: the wrapper is pre-written normalisation logic that has been reviewed before being added to the library. The mapping is promoted to the confirmed fix library with the approving engineer's name and timestamp attached.
The engineer's correction is captured: what the error actually was and what the correct fix is. Stored as a labeled counter-example. A denied interpretation with a correct label is one of the fastest ways to improve accuracy for that provider specifically.
On approval: the mapping is added to the fix library with a confidence weight of 1. Future identical errors from the same provider will be matched here first, before documentation or incident history search.
On denial: the counter-example is stored with the correct interpretation, reducing the weight of the incorrect mapping and preventing future recurrence. Both outcomes are equally valuable as training signals; a wrong answer with the right correction is data, not a failure.
As the fix library grows with confirmed entries for a specific provider, the confidence scores for that provider improve. An agent that has seen 50 confirmed Pix errors will give better interpretations than one seeing its first.
Confidence scores are per provider, per payment method, and per error pattern type. Over time this means the agent gets progressively more accurate on the providers that see the most traffic, which is exactly where accuracy matters most.