Month: March 2026

We probably need a GPS for Agents

GPS: A Global Positioning System for AI Agents

Q: Every large engineering org has tribal knowledge. The people who know where to look, who to ask, which spreadsheet has the real numbers. What happens when agents inherit that same dependency on tribal knowledge?

Jeremy: They ask for help. Same as humans do, but faster and with less ability to recover independently.

The information that drives our decisions lives in five or more disconnected systems: ticketing, corporate directories, release schedules, and spreadsheets. Each one has its own login, its own query language, its own data format.

Right now, getting a straight answer to a question like “who owns the inference component and what’s their current bug load?” requires a person who knows that the team spreadsheet uses a unique naming convention, and that the component mapping lives somewhere else entirely. That person cross-references three systems in their head.

That tribal knowledge tax is real. Onboarding a new senior hire takes 12-16 weeks when tribal knowledge is high, versus 4-6 weeks on well-documented teams. Multiply that across enterprise development teams, and the cost is staggering.

Now put an agent in that same environment. An agent can’t walk down the hall. It can’t ping me about that thing from the meeting last week. It can only work with what it can access programmatically. If the data is siloed, inconsistent, and locked behind human-only interfaces, the agent is blind. It will either hallucinate an answer, refuse to answer (wasting time), or produce something subtly wrong that looks right.

Agents are only as good as the data they can reach. Tribal knowledge is, by definition, unreachable to them.

Continue reading “We probably need a GPS for Agents” →

Structure Dictates Behavior: golden signals for agentic development teams

A previous post described what a day looks like when your engineering system runs mostly on its own. This one is how to reason through the structure required to drive the behaviors we want.

Starting with a simple concept, the learning organization. Here is a system dynamics stock-and-flow diagram.

In the simplest terms, learning begets learning: 

What we’re actually tracking

Most engineering metrics measure what the system produced: PRs merged, tickets closed, features shipped. Those are fine for tracking work. They’re less useful for improving the system doing the work. For that, I want to use and augment the DORA metrics that were successful for my teams in the cloud services space.

Every metric we care about maps to one of the two loop types:

  • Balancing loops create friction and slow things down. The metric targets are always down.
  • Reinforcing loops create compounding improvement. The metric targets are always up.
Continue reading “Structure Dictates Behavior: golden signals for agentic development teams” →