Reconcile industry files and chase clarifications with agents
i mean have you used any coding agents? Task: i mean have you used any coding agents? if you’re getting slop code in 2026, that’s a smell and skill issue. fwiw i was pretty bearish on AI until i spent a month a couple months ago going deep into agentic workflows.use your imagination to solve problems people face and pay $$$ for today that is error prone and hard.i’ve got agentic workflows for the particular industry im building for, one of which that replaces the need to hire $500+/hr services.in this particular workflow (don’t want to reveal too much, sorry this is my competitive advantage but you can figure it out for your own workflows) a $4/1M model ingests a file that is currently used in a extremely complicated program that few people understand how to use.it parses the data, loads it into a database, and then spawns a bunch of other agents that check the data against work in flight. there’s checks for bad data. in that case, more agents are spawned that reach out to the involved people or parties for clarification. if it cannot figure something out it reaches out to the right contacts for more information. while this is happening, more agents begin doing work that involves continuous reconciliation against 100s or 1000s or more things in flight.as files are uploaded, or updates from people come in, agents do work to ensure things remain on track.people are able to work across languages and cultures, and my agents ensure that while people can make mistakes, it will catch them in real time and ensure continuously monitor the situation.it’s pretty nuts how much inefficiency agents today can solve. it takes patience to run tests and tweak shit until it works.*** the really cool thing is that more capable agents can continuously monitor how things are going and improve the workflow itself… so all i need to do is maintain the actual tests. ****i loved writing tests back in the day to ensure i built good software. today we write tests to ensure the business can run.; tools: custom-built or unnamed agent. Human role: The original post does not say how a person stayed involved.; limitations: Prerequisites include a custom agent framework, database integration, and API access to stakeholder communication channels. The source is a self-reported Hacker News comment with no independent verification, and the author admits to withholding specific implementation details as competitive advantage. Proposed advice: Validate agent autonomy in edge cases before deploying to production, as the claim of 'infinite game loop' improvement is unproven..
Tools
custom-built or unnamed agent
Published on Agent Work Atlas:
Overview
i mean have you used any coding agents?
Workflow
i mean have you used any coding agents? if you’re getting slop code in 2026, that’s a smell and skill issue. fwiw i was pretty bearish on AI until i spent a month a couple months ago going deep into agentic workflows.use your imagination to solve problems people face and pay $$$ for today that is error prone and hard.i’ve got agentic workflows for the particular industry im building for, one of which that replaces the need to hire $500+/hr services.in this particular workflow (don’t want to reveal too much, sorry this is my competitive advantage but you can figure it out for your own workflows) a $4/1M model ingests a file that is currently used in a extremely complicated program that few people understand how to use.it parses the data, loads it into a database, and then spawns a bunch of other agents that check the data against work in flight. there’s checks for bad data. in that case, more agents are spawned that reach out to the involved people or parties for clarification. if it cannot figure something out it reaches out to the right contacts for more information. while this is happening, more agents begin doing work that involves continuous reconciliation against 100s or 1000s or more things in flight.as files are uploaded, or updates from people come in, agents do work to ensure things remain on track.people are able to work across languages and cultures, and my agents ensure that while people can make mistakes, it will catch them in real time and ensure continuously monitor the situation.it’s pretty nuts how much inefficiency agents today can solve. it takes patience to run tests and tweak shit until it works.*** the really cool thing is that more capable agents can continuously monitor how things are going and improve the workflow itself… so all i need to do is maintain the actual tests. ****i loved writing tests back in the day to ensure i built good software. today we write tests to ensure the business can run.
Reported result
The source claims this workflow replaces $500+/hr services and handles files with thousands of line items, but provides no measured outcomes, error rates, or verification of autonomous clarification success. Proposed acceptance check: Run a dry run on 10 historical files and compare agent-generated reconciliation logs against manual audit results to measure accuracy and false positive rates.
Human role
The original post does not say how a person stayed involved.
Limitations
Prerequisites include a custom agent framework, database integration, and API access to stakeholder communication channels. The source is a self-reported Hacker News comment with no independent verification, and the author admits to withholding specific implementation details as competitive advantage. Proposed advice: Validate agent autonomy in edge cases before deploying to production, as the claim of 'infinite game loop' improvement is unproven.
Try it
Act as an agent orchestrator for a file reconciliation workflow. Input: [replaceable: sample industry file URL, database schema, stakeholder contact list]. Steps: 1) Ingest the file and parse data into a temporary database. 2) Spawn agents to check data against [replaceable: current work-in-flight dataset]. 3) For bad data, generate clarification requests but DO NOT send them; output draft messages for human review. 4) Log all reconciliation actions. Output: A JSON report with parsed data, flagged errors, and draft clarification messages. Measure success by comparing flagged errors against a known ground truth set. Handle missing inputs by requesting the file and schema. Require human confirmation before any real message sending or database writes. Do not claim this was tested; this is a suggested implementation based on the described workflow.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.