AI Supervisor Adds Verification and Graph Memory Control to AI Coding Agents

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AI Supervisor is a control plane designed to manage AI coding agents on multi-step software tasks, addressing cases where agents report success without producing working results. The system turns a user's goal into tasks with dependencies and acceptance criteria, and requires human approval before execution begins. During runs it monitors progress, responds to failures, and checks outputs against the criteria, keeping 'completed' separate from 'verified'. FalkorDB serves as a graph memory layer that links missions, failures, supervisory decisions, handoffs, and verification evidence into a traceable history. The project is aimed at developers, AI engineers, and small teams, and is built with React, FastAPI, SQLite, and FalkorDB.

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Why CSV delimiter detection should be able to report ambiguity

A small file like 'id,name;label' can be read as two columns under either a comma or a semicolon, so picking the first delimiter in a list silently hides an unresolved choice. The article recommends parsing the text separately with each candidate delimiter and accepting only results that pass syntax and table-shape checks, such as quoting, escaped quotes, embedded newlines, and consistent field counts. A sample function selects the widest valid candidate and otherwise returns a 'needs-choice' status listing the tied delimiters. The author notes that this widest-candidate rule is only a heuristic, that a known export format or expected header schema gives stronger evidence, and that one-column files are valid rather than errors. After a user picks a delimiter, the importer should reparse the original text, show the headers and sample rows, and keep existing data until the choice is confirmed.

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ProgrammingDEV Community ·

AI Supervisor Uses FalkorDB Graph Memory to Track Agent Failures and Verification

A developer team built AI Supervisor, a control plane for coordinating AI coding agents on multi-step missions. They found that storing isolated logs made it hard to see how failures, supervisory decisions, and handoffs relate to one another. To address this, they selected FalkorDB as a graph memory layer alongside SQLite, modeling entities such as tasks, executions, decisions, and verification evidence with explicit relationships. A key design choice is that an agent's completion claim is not treated as verified; dependent tasks stay blocked until their prerequisite verification is accepted. The article presents this as a broader lesson that agent coordination requires explicit state and evidence rather than optimistic status messages.

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ProgrammingDEV Community ·

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ProgrammingDEV Community ·

Reject malformed UTF-8 in CSV imports before parsing with TextDecoder fatal mode

A CSV file can have correct columns yet contain corrupted text, because damage can occur before the parser ever sees the data. JavaScript's TextDecoder silently replaces invalid byte sequences with the U+FFFD replacement character by default, which can alter values like lookup keys without warning. Setting the fatal option to true makes decoding throw an error instead, allowing import workflows to reject damaged bytes. The article recommends checking file size before reading, keeping prior data and showing an actionable error on failure, and avoiding automatic retries under other encodings. It also notes that strict decoding only confirms byte validity, not correct encoding or valid CSV, so syntax and domain checks must follow separately.

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