Outcomes
over
output
More code is not more value. We measure what ships to users, not what ships to the merge queue.
Context
over
tooling
The best AI tools fail without accessible knowledge. We invest in making our organization's context machine-readable.
Orchestration
over
execution
Our job is to direct, review, and decide. We delegate production to agents and focus human energy where judgment matters.
Continuous flow
over
fixed ceremonies
We ship when ready, not when the sprint ends. We plan around review capacity, not production capacity.
Governance as speed
over
governance as control
Clear rules and automated guardrails let agents move fast. We build trust through transparency, not through committees.
That is, while there is value in the items on the right, we value the items on the left more.
Principles
01
Our highest priority is to deliver value to users. Agents that produce output nobody uses are waste, not progress.
02
AI is an amplifier, not a miracle. It accelerates what's already there. If the foundation is weak, AI makes it worse faster. We invest in the foundation first.
03
We treat Context Debt like Technical Debt. Undocumented decisions, tribal knowledge, and stale documentation are liabilities. We pay them down systematically because our agents depend on it.
04
We measure outcomes, not velocity. Features shipped, problems solved, users served. Not PRs merged, lines generated, or story points completed.
05
The bottleneck has shifted. Code is cheap, judgment is scarce. We organize our team around review, verification, and decision-making, not around production.
06
Every agent has an identity, an owner, and a decision log. We don't deploy autonomous systems without accountability. Governance is infrastructure, not bureaucracy.
07
We delegate deliberately. The team has shared criteria for what gets delegated to agents, what stays human, and what's a collaboration. This is a team discipline, not an individual preference.
08
We recognize and reduce AI Waste. Overproduction, review bottlenecks, governance theater, context loss, over-prompting, wrong granularity, unused output, and manual work that should be automated. We audit regularly and fix relentlessly.
09
Small batches, continuous deployment, automated quality gates. Agent output follows the same engineering standards as human code. No exceptions, no shortcuts.
10
We build shared knowledge, not individual expertise. Prompt patterns, agent configurations, and delegation criteria belong to the team. When someone leaves, the knowledge stays.
11
We talk openly about how AI changes roles and identities. Senior engineers design systems instead of writing code. Juniors learn by reviewing agent output and understanding why. We create new narratives, not new anxieties.
12
We plan in short cycles with honest assessments, measurable goals, and named owners. Review, learn, adjust, repeat. Long-term roadmaps are speculation. Disciplined execution in tight loops is where value lives.