51 posts tagged with "ai-agents"

Most agent demos are stateless. A user asks a question, the agent answers, the session ends — and the next conversation starts from scratch. That's fine for a calculator. It's not fine for an assistant that's supposed to know you.

The gap between a useful agent and a frustrating one often comes down to one thing: whether the system remembers what matters. This post breaks down how to architect durable, personalized memory into production AI agents — covering the four-phase lifecycle, layered precedence rules, and the specific failure modes that will bite you if you skip the engineering.

Most multi-agent systems fail not because the models are wrong, but because the plumbing is leaky. Agents drop context mid-task, hand off to the wrong specialist, or loop indefinitely when they don't know how to exit. The underlying cause is almost always the same: the system was designed around what each agent can do, without clearly defining how work moves between them.

Two primitives fix most of this: routines and handoffs. They're deceptively simple, but getting them right is the difference between a demo that works and a system you can ship.

Most teams building AI agents spend weeks on pre-deployment evals and almost nothing on measuring what their agents actually do in production. That's backwards. The metrics that matter—how long agents run unsupervised, how often they ask for help, how much risk they take on—only emerge at runtime, across thousands of real sessions. Without measuring these, you're flying blind.

A large-scale study of production agent behavior across thousands of deployments and software engineering sessions has surfaced some genuinely counterintuitive findings. The picture that emerges is not the one most builders expect.