2026 agentic AI enterprise trends: market size and growth, adoption rates, capability maturity, top use cases, spend, ROI, and the vendor landscape, with sourced benchmarks.
The Enterprise Agentic AI Market Size: 2026 Statistics, Growth, and Adoption Data
The enterprise agentic AI market reached $3.67B in 2025 and is on pace for $24.5B by 2030. See verified 2026 data on market size, adoption, and ROI.
Customizing Devcontainers Without Affecting Your Team
There’s no single “correct” way to customize a devcontainer, just like there’s no one right way to use most tools in software development. Devcontainers can be great for solo projects, to yield an isolated development workspace with no risks of polluting (or being polluted by) the external environment. They can also give teams a consistent, baseline environment for bootstrapping engineers, with integrations like GitHub Codespaces exposing that environment in a variety of ways. But the gap between those workflows is large, and managing it sometimes requires weighing a team’s needs vs. individual preferences.
It’s a balancing act, and getting it wrong can make your containers brittle, difficult to maintain, and cause enough overall frustration to outshine the benefits.
Yet there are options available for a more-granular approach. We don’t have to decide just between bare-minimum or include-everything – we can curate containers that are focused on the core tools used across the entire team, while still providing room for per-user expansions.
Today, we’ll explore a few approaches for customizing a shared devcontainer setup across different team scenarios, without having to abandon shared devcontainers altogether.
Beyond Coding: Engineering in an AI-Native SAFe World
Artificial Intelligence is changing where engineering value is created: less in producing artifacts by hand, and more in making better technical decisions. In SAFe, that change extends well beyond code generation. It affects how Agile Release Trains prepare for PI Planning, how architects manage the Architectural Runway, how teams validate quality, and how organizations learn from delivery data.
In my earlier three-part series on the Foundations of SAFe, I covered its values, core principles, benefits, drawbacks, and practical application. As SAFe evolves, the next question is how AI will change the way those principles are practiced in real engineering organizations.
This article looks at what AI practices mean for engineering work inside SAFe. Rather than just faster coding, the real gains are in better planning, stronger architecture, faster feedback, and more accountable engineering decisions across the Scaled Agile Framework.
Digital Transformation Statistics 2026: Market Size, Industry Adoption, ROI, and the Agentic AI Acceleration Angle
2026 digital transformation statistics: market sizing across leading research firms, industry adoption rates, ROI benchmarks, enterprise vs. mid-market patterns, and how agentic AI is changing transformation delivery economics.





