The Manifesto for AI & Agile Alignment
We are uncovering better ways to align agile teams with AI and each other, and to help others do so.
AI & Agile Alignment Values
eXponential markets
over incremental profits
Scientific experimentation
over big design up front
Continuous throughput accounting
over projects and budgets
Autonomy in alignment
over command and control
Learning-flow acceleration
over transformation frameworks
Ecosystems of mutual benefit
over one-way value streams
while items on the bottom have value, we value those on top more.
AI & Agile Alignment Principles
- Directly Responsible Individuals and Agents: one for each business, design, and technical goal. DRIs and AI agents must work together daily to learn and adapt to each others’ constraints.
- Capture and store learning in AIs: as small, self-organizing, cross-functional teams work to open the current constraint, they train AI agents to meet future ones.
- You Aren’t Gonna Need It: teams and AI agents continuously prioritize their work relative to the current throughput constraint, measure progress by it only, and defer all other work.
- Align small groups of teams and agents to form value streams that continuously adapt their goals and workflows to changing market constraints and to each other.
- Model rewards on mutual benefit across teams and streams to minimize silos, wastes, politics, and missed opportunities, and to promote shared resources, services and learning flows.
- Continuously refactor value streams: reuse, recycle or reduce all the resources each stream produces until all contribute to throughput and none to waste.
- Design breadth-first: use AI to proactively uncover repeating patterns in and between markets and value streams. These inform designs we can only detail as we learn more.
- Build factories, not products: business, design, devops and AI are only in the right relationships when their work aligns to open their mutual working and learning constraints.
- Simplify and automate: simple, automated solutions cost less to install, align, and maintain than complex manual ones, yielding more broadly applicable business outcomes faster.
- Use and value experimentation: evaluate experimental costs against competitive risks to innovate products that meet and create new market opportunities.
- Open platforms and APIs: under-served market segments and spaces between segments hold opportunities for efficiency and collaboration that new business will develop for itself.
- Transform to embrace change: continuously amplify your organization’s intelligence to meet changing market conditions and opportunities for new growth.
XSCALE.CO
For AI and Agile
Alignment at Scale
To align AI with humans we first have to align the humans with each other.
XP/BDD/ToC do that for a small team, enabling it to focus on its goals autonomously.
But how do multiple autonomous teams align to form an autonomous value stream?
How can these streams continuously align to VUCA markets and each other?
How do AI agents integrate into these teams and streams to form a self-aligning organization?
We can’t afford committees any more
Command & control committees multiply costs of change, quality, and delay 10x.
Big meetings strangle learning flow, force majority-rule compromises and lose focus on outcomes.
Doer-decider distance distorts and delays learning, making SNAFU and breaking autonomy.
Hierarchy slows lateral learning so the left hand doesn’t know what the right does.
KPIs and OKRs optimize focus on local goals, not on growing end to end business throughput.
We need community
Not bureaucracy
A mutual-benefit network aligning Business, DevOps and AI to the end to end throughput constraint.
Focused on opening the bottleneck today, not “what we’re gonna need.”
Pull-based change that works quicker, cheaper and safer – ie. less risky – than framework.s
Without pushing change into existing teams, we don’t wait for resistors or compromise principles
Instead, invite green people into mature teams to collaborate with their peers, not obey “masters”.