@aarondfrancis: A good way to audit your codebase: • a strong orchestrator inventories every subsystem • it sends fresh read-only agent…
Summary
A method using a strong orchestrator and read-only agents with a DSA prompt to audit codebases, finding 93 opportunities across 55 subsystems overnight.
View Cached Full Text
Cached at: 08/17/26, 04:14 AM
A good way to audit your codebase:
• a strong orchestrator inventories every subsystem • it sends fresh read-only agents through with a DSA prompt • it validates, dedupes, and ranks
I ran this overnight and it found 93 opportunities across 55 subsystems! Audit only. https://t.co/KEs7QF7Z0I
Similar Articles
I built an open-source multi-agent SDLC harness that beats a cold Claude Code run on large repos, by learning the repo once. Real benchmarks (incl. where it loses) inside. [P]
AutoDev Studio is an open-source multi-agent AI coding harness that reduces costs by building a persistent repository knowledge base, outperforming cold-start Claude Code runs on large repos in most tasks.
@PaulSolt: https://x.com/PaulSolt/status/2073470146115490230
Paul Solt shares a detailed workflow for using Codex agents in a loop to autonomously build features overnight, including a manager thread, heartbeat, and automated PR review. The technique shifts from single prompts to designed agent loops, enabling continuous development with minimal human intervention.
Coding agents got good. Managing them didn't. I open-sourced the control room I built for it (MIT)
The author open-sourced o8, an MIT-licensed orchestrator that manages multiple coding agents in isolated git worktrees, with a merge gate, audit trail, and mobile approvals.
Best tools for monitoring and auditing autonomous AI agent behavior at runtime, what's actually working in prod?
A practitioner shares challenges and tools for monitoring autonomous AI agents in production, covering runtime prompt injection detection, tool-call auditing with reasoning traces, behavioral drift detection, and multi-agent authorization, while testing tools like Arize Phoenix, Protect AI Guardian, Metoro, Alice, Asqav, and Microsoft Agent Governance Toolkit.
@delba_oliveira: https://x.com/delba_oliveira/status/2062203743387459836
This article describes how to set up feedback loops and self-verification workflows in Claude Code, enabling the agent to independently check its work and reduce human oversight for ambitious tasks.