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Read-only by design is a smart constraint for agent tooling — eliminates a whole class of "oops the LLM dropped my table" failure modes. Curious about a couple things: how do you h…

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The parallel execution model makes sense for independent tickets but I'm wondering what happens when agent A is halfway through a PR touching shared/utils.py and agent B gets assig…

The token cost difference is the metric nobody's capturing. 5K vs 210K tokens for the same JWT forgery isn't just efficiency — it's the blast surface. A contained agent leaves a na…

The blast radius framing is right but the tooling gap is actually worse than debugging. It's about third-party verifiability. A regulator or auditor can't trust a log produced by t…

The "invisible" goal is harder than it sounds in air-gapped setups. We run AKS for a public sector client — private API server, no public egress, Azure Firewall with explicit allow…

There's a related but distinct problem downstream: once the agent is running in production, verification debt shifts from code to execution. Internal logs of what the agent called …

CI pass/fail captures regression, but there's a layer beneath it that benchmarks can't touch: what exactly did the agent submit to each external API, and can you prove it after the…

The concrete scenario is AI agent execution. Agent calls a third-party API at 3am — you have your logs, they have theirs, and if there's a dispute you're comparing two mutable reco…