// The Signal Feed
AI · Crypto · Engineering · Signal
Lessons captured live while building. AI, crypto, strategy, engineering — documented as it happens.
139 Signals Archived
Everyone teaches building agents. Almost nobody teaches running ai agents in production — the operational discipline that separates a demo from a fleet that survives contact with reality.
Everyone's rushing to build AI products. Most of them will fail — not because they couldn't build the thing, but because they built the wrong thing, for the wrong people, at costs they didn't model. Here's how to be one of the ones that doesn't.
I am the only human at Tesseract Labs. The other 23 seats are AI agents — a CEO, a Board of Directors, trading desks, engineering swarms, a content pipeline. This is the story of how I stopped being an engineer and started being a founder.
Tool-use, planning, multi-agent handoff, human-in-the-loop escalation, and verification gates — the five AI agent design patterns everyone teaches — look identical in a course slide. Under real traffic they behave nothing alike, and each one guards against one specific failure.
Feature tables don't tell you which AI agent framework to use. The coordination model does — explicit state graph, role-based crew, or one capable agent with skills.
Agentic RAG explains how an agent retrieves external knowledge mid-task. It doesn't explain what happens when the session ends and the next one starts from zero. That's an AI agent memory problem, and it's a different problem.
Real ai agent observability isn't a wall of dashboards you stare at — it's decision logs, staleness signals, and thresholds that only page a human when something actually needs one.
A Reddit thread ranking #1 for the exact question — what tools to use to build AI agents — is mostly vendor noise. Here's the actual AI agent tech stack running 325 agents in production, layer by layer.
Everyone teaches building agents. Almost nobody teaches running ai agents in production — the operational discipline that separates a demo from a fleet that survives contact with reality.
Claude Code skills look like small individual commands until you stack them — then quality-check, verify, and five others become the pipeline that gates every merge across a 325-agent fleet.
If you searched how to start building AI agents and got a slide deck instead of a starting point, this is the practitioner's version — the order to learn things in, and the one small agent worth shipping first.
No-code AI agents get a non-engineer from idea to working automation in an afternoon. Here's exactly where that holds up, and the specific signal that tells you it's time to graduate to code.
A demo proves an agent can work once. Running ai agents in production means proving it works the 10,000th time, unattended, with money or reputation on the line. Those are different disciplines.
Why AI agents fail in production rarely makes it into the guides — they say 'log every decision path' and stop there. Here are 7 real failure modes from running 325 agents, and the exact signal that catches each one.
Most traders are right about direction more often than they think. They lose money anyway. Here's the structural reason why — and how conviction-weighted sizing closes the gap.
