By L. I. Kumara A busy restaurant never asks the person taking your order to also cook your meal. Taking an order is fast: hear it, write it down, hand it off. Cooking is slow, and how long it takes depends on what was ordered and how many pans are free. Bundle both jobs into one role and a quiet…
By Yaala Labs Deploying AI agents in production comes with critical business risks: brand damage from inappropriate responses, regulatory penalties from data leaks, and customer trust erosion from security breaches. Today, we're announcing Guardrails for Agent Kernel - enterprise-grade content…
Rationale, structured knowledge, or codebase intelligence for coding agents? Three approaches to Git-native project memory for coding agents "Project memory for coding agents" is becoming a broad category. Three projects are especially interesting because all are repository-oriented, all preserve…
Here's a pattern I keep seeing. A team wires up an AI agent that can do real things — send emails, run commands, query and modify the database, call external APIs. The demo is magical. It reads a request, figures out the steps, takes them, reports success. Everyone's impressed, and it ships. Then…
I run an agent that writes me a morning brief before I'm out of bed. It tracks the repos I care about and stays quiet on slow days. It pays for the web searches it needs out of its own wallet. It does all of this on a schedule, in GitHub Actions, on my own copy of the aeon public repo. No server.…
An autonomous agent's memory design should answer a practical question: if the process stops now, what will the next run need to continue correctly? Start with a small, inspectable record of the goal, confirmed progress, unresolved decisions, and reusable lessons. Then test whether a new session…