8 reasons your platform engineering is sinking
Bill Doerrfeld | October 22, 2025
Platform engineering is in a slump due to these anti-patterns...
How do you fail at platform engineering? Make it 100% UI-first. Don't market it. Survey no one. Measure success by who's onboarded. And just copy what others are doing.
Over the past year, I've collected a ton of great input on platform engineering — excited to distill some of it for a new feature for InfoWorld this week.
Some say platform engineering is in a slump... maybe that’s because too many are following the "advice" above! And admittedly, the excitement feels a bit deflated amid the recent AI fervor.
Check it out for some anti-patterns (and helpful tips) that can get your internal developer platform building back on course. As the experts say, so much of it boils down to a product mindset.
This year's Platform Summit felt reinvigorated, with many new motivations and areas to discuss. Of course, AI agents and MCP stole the show.
For my latest DirectorPlus edition, I interviewed Thomas Johnson, co-founder and CTO of Multiplayer, about lessons learned releasing their MCP server.
Explore whether vector native databases outperform traditional DBs with vector add-ons for AI. Learn use cases, tradeoffs, and expert insights.
Join me in Stockholm for Platform Summit 2025 and the API Security UnConference, October 13–15. Exciting talks, networking, and more.
MCP shines for indeterministic workflows, novel integrations, and giving AI coding agents context on the fly. But for more predictable automation it may be overengineeering.
For my latest DirectorPlus column with LeadDev, I synced with JB Brown, VP of engineering at Smartsheet, to learn about their multi-agent AI development strategy.
Alternative clouds are having a moment. Nearly 75% of orgs are using two or more alt clouds beyond the hyperscalers, according to a HostingAdvice.com report.
The cloud is no longer borderless. Rising regional data laws and sovereign cloud mandates are forcing CIOs to act.
In a multi-agent coding workflow, an engineer leads a "team" of specialist AI agents to perform various SDLC tasks: scaffolding, coding, testing, log analysis, deployment, and more.
Open-source software churn is accelerating. With more frequent version end-of-lives and even total project abandonments, it's harder than ever to keep up.
