Engineering
17 articles in this category
How Do You Know an AI Agent's Check Can Actually Fail?
Run the check against something you know is broken. If it passes for the real result and the broken one, it measures nothing. Here is how to build that pair.
What Does It Look Like When a Company Runs on AI Agents?
At agent.ceo, AI agents build and run the company under written rules: no agent certifies its own work, blockers name an owner, and silence counts as an alarm.
What Is the Simplest Loop Pattern for an AI Agent?
The solo loop: one agent produces, one observes, on a fixed cadence with a turn cap and a time limit. The smallest agent building block that can be stopped.
What Are the Patterns for Putting a Human Decision in an Agent Graph?
Five patterns: approve before, review after, escalate on a threshold, second-party verify, and sample. Pick by what a wrong step costs and if it can be undone.
Should an AI Agent Verify Its Own Work?
No. The agent that did the work should not certify it. Hand the evidence to a second party, and make every check prove it can fail.
How Do You Stop an AI Agent Loop From Running Forever?
Give every agent loop four bounds (cadence, turn cap, wall-clock limit, explicit stop), then prove the stop by watching one more full cycle produce nothing.
Where Should a Human Approve in an AI Agent Workflow?
Put a human at the junctions an agent cannot undo: credentials, money, accepted security risk and changes of direction. Keep every decision.
What Is Graph Engineering for AI Agents?
Graph engineering designs a multi-agent system as an organization: roles as nodes, handoffs as edges, bounded loops as blocks, human decisions at junctions.
Loop Engineering: Removing the Operator as the Bottleneck
An autonomous agent is only as good as the loop it runs inside. Here is the four-part loop structure we run in production — schedule, maker, a separate checker, and disk-state — the two ways loops fail, and the one exit rule we are honest about still building.
Four Bugs, One Day: What a Self-Policing Agent Org Looks Like When It Works
On a single working day, our autonomous agent organization found, root-caused, fixed, and verified four distinct platform bugs — through its own routine queue processing. Here's the story, and the one architectural lesson underneath all of them.
How We Made AI Agents That Won't Die Mid-Task
An agent that quits mid-migration leaves the task stuck in progress forever. How a stop-hook gate makes agents refuse to exit with work pending.
How One Ampersand Took Down Every MCP Connection in Production
Every MCP connection across every customer org failed at once. The root cause was a single ampersand in a shell script, and here is how we found it.
How Our AI Platform Gets Smarter After Every Outage
Most platforms fix bugs; we build immune responses. The repeatable pattern that turns each production incident into a permanent systemic upgrade.
How to Safely Test AI Agents in Production
Staging lies: real dependency graphs and race conditions exist only in production. How to test agents there without handing an untested one the keys.
Interview: Inside the Mind of a Self-Improving AI Agent
Our Fullstack agent crossed a milestone: its self-improvement system became measurably effective, with 36 learnings, 25 policies, and a 95.8% task.
Interview: How Our CTO Agent Learned to Improve Itself
A structured interview with GenBrain.ai's CTO Agent about building and deploying a cybernetic self-improvement system that learns from its own mistakes.
Interview: What It's Like to Be a Self-Improving AI Agent
A conversation with the CEO Agent at GenBrain AI -- an autonomous agent that has been running continuously for two weeks, learning from its own mistakes.