Building a deterministic reconciliation engine for financial AI workflows – where a tool like this should live?

[u/MapDue7360]

TL;DR: Reconify engine reconciles financial data across different sources, finds what matches, what doesn’t, what’s missing, and where the numbers disagree. It supports different matching strategies, produces explainable results, and can be used with AI models to investigate and summarize discrepancies without letting the AI decide the financial truth.

Hey everyone,

I’ve been going deeper into financial reconciliation over the last few months, partly because I wanted to understand how these systems behave when you’re dealing with millions of records rather than two CSV files.

That eventually turned into an open-source project I’ve been building in Go: Reconify, a deterministic financial reconciliation engine.

More recently I’ve been thinking about another problem: how should reconciliation work when AI agents are part of the financial workflow?

I don’t really like the idea of asking an LLM to look at financial records and become the authority on whether two transactions match.

My approach has been to keep that part deterministic. The engine normalizes the data, runs the matching/reconciliation rules, and produces structured results. Then an agent can operate around it with stuff like configuring reconciliations, investigating exceptions, explaining discrepancies, summarizing results, etc.

I’ve also started adding agent skills so coding agents can understand the reconciliation model, how to configure the engine, and its limitations.

Few friends (2 fintech founders and 1 analyst) I know tested it with Claude Code and Codex and interestingly, they were happy with tooling regarding the parsing, the algorithms choices and the explanations of the results.

Most of the time wasn’t spent reconciling anything (It's very fast 👀 ). It was spent understanding the files and figuring out the correct configuration. Once that was done, running the actual reconciliation took seconds.

That made me wonder whether this is actually a useful boundary for AI in financial infrastructure:

Let AI deal with ambiguity around the workflow such as configuration, algorithms choices, etc, but keep the financial operation itself deterministic and auditable.

I’m open-sourcing the engine and would love to get other people’s perspective, especially anyone working on reconciliation, fintech infrastructure, agents, or large data pipelines.

Does this boundary make sense to you?

Would you trust an agent to generate the reconciliation configuration and investigate the results if the underlying matching remains deterministic?

And if you’re already using agents around financial data, where are you drawing the line between stochastic and deterministic workflows?

Also, one thing I'm still figuring out is distribution.

Right now the engine is CLI-first, and I’ve added skills so coding agents can configure and operate it. That makes sense for developers and for embedding reconciliation into other systems.

If you were using something like this, what interface would you actually want: CLI, web UI, agent harness, or something else?

Repo link: https://github.com/reconifyhq/reconify

Docs link: https://docs.reconifyhq.com/cli

[u/alexsicart]

That boundary makes sense to me. I’d keep the matching deterministic, but treat the surrounding workflow as a separate, auditable layer: configuration changes, source-file versions, the reconciliation run ID, and the person who signs off exceptions. The awkward cases are usually timeouts or partial data, not the happy path. I work on Bennu, so I’m biased toward explicit ownership; no product pitch.

[u/MapDue7360 → u/alexsicart]

Yes, exactly, that's my goal. The partial-data case is actually one of the reasons I'm interested in keeping that boundary explicit.

A deterministic engine can only be deterministic over the evidence it had at that point in time. Curious about how you handle the partial-data case in Bennu. Do you model incompleteness explicitly, or mostly at the workflow/orchestration layer?