Our @SkylightMarine team is rolling out Shippy, an AI agent for the people protecting our ocean in real time.
A wrong answer at these stakes can send a patrol vessel miles off course. Here's the architecture we built so analysts can trust it. 🧵
@SkylightMarine Shippy is an AI agent built on Skylight, our real-time maritime intelligence platform.
Ask a question in plain language & it fuses data sources, cites them, and delivers clear maritime intelligence in minutes.
Making it reliable came down to architecture.
@SkylightMarine Shippy queries Skylight through a purpose-built CLI with typed flags.
It handles auth, pagination, & structured output, preventing dropped results + misread responses. Writing to disk – not piping through the shell – keeps large queries from breaking downstream tools.
@SkylightMarine Serving Shippy to hundreds of agencies across 70+ countries meant building new infrastructure.
Mothership, our agent hosting platform, runs every conversation in its own ephemeral, isolated session, so no agency ever sees another's data.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.
Why it matters
A working pattern for high-stakes agents: give the AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → a typed CLI instead of raw shell, write large results to disk so downstream tools do not choke, isolate every session per tenant, and gate deploys on scored evalA repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.Full definition → scenarios run against live data.
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