// The Signal Feed
AI · Crypto · Engineering · Signal
Lessons captured live while building. AI, crypto, strategy, engineering — documented as it happens.
135 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.
BlackRock's IBIT pulled in $693 million last week and crypto Twitter called it validation. Mastercard spent $1.8 billion on stablecoin plumbing and nobody noticed — which is exactly the point.
Circle got a bank charter and Wells Fargo built its own blockchain — Crypto Twitter called both a win. The fine print says something closer to an eviction notice.
A graph isn't more mature than a loop — it's more expensive than one. The only question worth asking is whether you're buying something with that expense, and most teams can't say what.
The industry spent a month arguing about which box-and-arrow diagram to draw. The engineers who actually shipped something were asking a completely different question the whole time.
Most teams shipping 'agentic AI' this year are running a fifteen-line while-loop with a fancy name. That's not a confession — it's the correct architecture.
One-shot LLM review produces confident approvals at machine speed, and confident approval is the most expensive output in software. The fix is structural, not a better prompt.
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.
