4 posts tagged with "agents"

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Part 1 of this series audited a live multi-agent box and found every network-exposure question worth asking. One line from that audit didn't get followed up: "the Postgres, ClickHouse and Redis behind Langfuse were published to 127.0.0.1 instead of the world — someone made a good decision there. Hold that thought."

Here's the other half of that thought: getting the network right says nothing about what happens after someone's already inside a container. If the process running in there is root, a compromise starts with the keys to the whole filesystem. So we checked — on the same box, the same Langfuse stack Part 1 already praised — whether "network correct" also meant "privilege correct." It didn't, for two of the six containers. Here's what fixing that actually looked like, including the part that broke.

You start with one agent. Then it needs a database. Then you add a second agent, a messaging bridge, an observability stack. Six months later a single WEC Instance is running five compose projects, twenty-something containers, and nobody remembers which ports are open to the world.

That's not a hypothetical — that's the box this tutorial was written on. So instead of theorizing, we probed it: can containers reach each other across stacks? Can they reach the databases? Is the firewall actually protecting anything?

Three of the answers surprised me. One of them was a database sitting there with no password. And the firewall — the firewall was lying.

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An agent that calls tools does two very different things: it reasons ("I should search the docs") and it acts (actually calls the tool). When something goes wrong, the first question is always which layer failed — did it think wrong, or did the doing break? A flat log can't answer that. A trace can.

In this tutorial you build a small tool-calling agent on WEC Inference, instrument it with Langfuse so every reasoning step and every tool call becomes an inspectable node, then debug two real failures from the trace tree — including the worst kind: a confident, wrong answer that never throws an error. Every command, error, and screenshot below is from a real run.

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Hermes Agent is Nous Research's open-source (MIT) AI agent — "the agent that grows with you." Its standout feature is persistent memory: it learns your projects and doesn't forget across restarts. This guide deploys it on the same WEC Instance you already use for OpenClaw, points it at a model, and proves the memory survives a full reboot.