AI Engineering
11 primers covering the patterns, pitfalls, and external resources from this track.
Mesh LLMs: Building AI From Spare Compute
A future where compute, models, and agentic capability are commodities you can route around.
Beyond Forgetful Bots: Persistent, Proactive Agent Architectures
From reactive chatbots to enduring AI partners that stick around and act on their own.
Shipping Sandboxed Workers for AI Agents
Letting users extend agents with custom code without letting their code escape.
Close Your Agentic Loop
You are the feedback loop until you build automated evaluators. Stop being the loop.
How Many Agents Are Too Many? The Hidden Cost of Multi-Agent Systems
Multi-agent designs add cost, latency, and failure modes. Here's when they're actually worth it.
Kill the God Agent: Architectural Security for Multi-Agent Systems
Prompt injection isn't a guardrail problem. It's an architecture problem.
Agent Observability at Internet Scale
What to log, how to trace multi-step agents, and how to detect drift in production.
Fixing Production Hallucinations With Evals
The demo was easy; real traffic wasn't. Build the eval stack you wish you had on day one.
The Application Layer Is the New Research Lab
Agentic systems collapse the gap between product and research. Staff for it.
Matching Models to Tasks: Routing for Cost and Quality
Don't use Opus for a Bash script. Build a routing layer.
When Agent Memory Breaks in Production
Your benchmarks pass. Then real users arrive, and memory becomes the bug.