saltpath

6 ms·

1.
by saltpath 6mo ago
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 handle schema introspection? Do the tools auto-disc
2.
by saltpath 6mo ago
The parallel execution model makes sense for independent tickets but I'm wondering what happens when agent A is halfway through a PR touching
3.
by saltpath 6mo ago
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 narrow call trace. A thrashing one touches five
4.
by saltpath 6mo ago
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 the same operator who runs the agent. Spent the las
5.
by saltpath 6mo ago
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 allowlists. K8s is the right call, but invisible it is
6.
by saltpath 6mo ago
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 and what it received are mutable — if a provider disput
7.
by saltpath 6mo ago
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 fact? In the benchmark context this doesn&#x
8.
by saltpath 6mo ago
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 records. Sealing both the request hash and response ha