Drawing direct inspiration from the 2001 Agile Manifesto, the newly drafted document centers on five core values, such as valuing understanding over speed and better work over easier work. It formally characterizes "slop" as low-effort generation that demands disproportionately high effort from colleagues and users to interpret. To enforce standards, the framework urges developers to disclose agent-authored material and conduct rigorous human reviews tailored to the stakes of a project. It ultimately demands that technical metrics, including merged pull requests and token consumption, take a backseat to meaningful end-user outcomes.
Key points
Professionals must remain fully accountable for the intent, taste, and overall consequenc…: Professionals must remain fully accountable for the intent, taste, and overall consequences of the software they deploy.
Organizations should mandate transparent disclosures when artificial intelligence agents…: Organizations should mandate transparent disclosures when artificial intelligence agents are used to author workplace content.
Development success must be evaluated by end-user outcomes rather than vanity metrics suc…: Development success must be evaluated by end-user outcomes rather than vanity metrics such as merged pull requests or tokens spent.
Sources checked by our analysis pipeline: 1
Written by the speakers and attendees of AIE Code Summit 2026
In 2001, 17 software engineers and leaders got together at a ski lodge in Snowbird, Utah to understand the biggest challenge of their time: heavyweight, documentation-driven software development processes.
Out of this we got the Agile Manifesto, four values and twelve principles that changed how most of the industry builds software.
Today we convene to build our own manifesto - the No AI Slop manifesto.
AI increasingly helps us write our code, docs, plans, and messages. We've all been on the receiving end of slop, and most of us have sent some too. Through this work, we have come to value:
Outcomes over output
Understanding over speed
Our own thinking over a model's first draft
Better work over easier work
Improving the system over patching the output
The world moves especially fast these days, but the goal of these resolutions is to embrace our important human role in harnessing AI safely, responsibly, in a way that creates value for everyone, and that will be relevant for years to come.
The feature introduces agentic commerce by allowing artificial intelligence to execute financial transactions directly on behalf of consumers. To safeguard funds, the checkout process mandates biometric confirmation and masks sensitive credit card data through digital tokens. However, adoption hinges on whether external merchants update their fraud systems and refrain from blocking automated bot traffic on their platforms.
Investors are pouring fresh capital into developer infrastructure as enterprises transition from experimental machine learning models to production-ready autonomous systems. LangChain is positioning its management platform as a vendor-neutral safeguard that prevents businesses from being tethered to a single cloud or model ecosystem. This major cash infusion reflects surging corporate urgency to deploy and control autonomous workflows at scale.
The latest documentation centers on how the system undergoes preparation, testing, and implementation across various real-world tasks. It directs attention toward operational readiness and validation protocols rather than unrelated tooling adjustments. However, technical benchmarks and broader operational circumstances remain unspecified in the current disclosure.