The platform engineering stories enterprise IT leaders actually read.
Volume 343, Issue 2 · 6 articles
Shared platforms need one accountable owner per service and an incident commander separate from root-cause investigation.
When a developer platform fails, infrastructure, DevOps and SRE teams may each own part of it, leaving developers waiting while the teams debate who responds.
Sombra's Mykhailo Voitovych says ownership problems come from unclear, overlapping or undocumented responsibilities that only surface during outages.
He recommends a single accountable owner for every service, backed by service catalogs, ownership matrices and escalation paths, plus an assigned incident commander who sets priorities while specialists investigate the root cause in parallel.
After recovery, postmortems, updated runbooks and response drills should prevent confusion the next time.
Applying platform engineering and AI to RF and antenna design speeds simulation and validation as networks head toward 6G.
As telecom networks move toward 6G, radio frequency and antenna design involve repeated simulations and many parameters across distributed teams.
The article explains how platform engineering provides a standardized, automated development environment, with golden paths, containerized tools and CI/CD, that AI can use to optimize antenna performance and speed up design decisions.
It contrasts platform engineering with DevOps and maps open source tools such as 4NEC2, GNU Radio, openEMS, Docker, Kubernetes, Jenkins, PyTorch and TensorFlow to tasks like SDR development, beamforming and spectrum analysis.
Benefits include consistent builds, continuous validation and fewer signal drops.
When a single AI agent beats a multi-agent team for platform operations work, and how to benchmark the two.
Using a failed production deployment as the example, the article compares one agent that reads logs and suggests a rollback with a four-agent team of deployment, SRE, security and coordinator roles.
Giving models different titles does not create independent perspectives if they share the same model, context and assumptions, so they can agree on the same mistake.
Real benefit comes from specialized tools, distinct evidence sources, bounded responsibilities and clear permissions.
Platform teams are advised to benchmark both designs on accuracy, security findings, evidence quality, latency, token use and handoff overhead.
LLM features break platform assumptions on quality, cost and trust; a model gateway is the one place to enforce controls.
Drawing on consulting work, the author describes a logistics firm whose AI spend jumped from Rs 42,000 to Rs 1.1 million in two months because an agent looped without a retry ceiling, which went unnoticed until tracing was added.
Most of the platform (Kubernetes, ArgoCD, Prometheus) stays the same, but three things fail silently.
Quality fails because a well-formed 200 response can carry stale answers, cost shows up a month late on the bill, and trust erodes because prompts mix your instructions with untrusted text.
The fix is a model gateway such as LiteLLM in front of every model call to enforce budgets, routing and observability.
Which parts of Uber's MCP gateway governance model translate to platform teams running 10 to 100 MCP servers.
Uber's MCP Gateway fronts more than 800 MCP servers and 5,000 tools through a registry control plane and a proxy data plane that translates MCP calls into HTTP, gRPC or TChannel via the service mesh.
An AutoCrawler generates virtual MCP servers from Protobuf and Thrift definitions and registers their tools disabled by default.
The article argues that the governance model, not the scale, is what to copy: a tool registry with owners, new tools off until approved, and tool-level authorization.
It also recommends gradual tool discovery, response filtering and repeatable CLI workflows to cut context consumption.
Survey of 379 platform professionals finds teams that build platform features from scratch meet fewer goals and lack strategy.
Drawing on the 2026 Future of Platform Engineering report from Octopus Deploy, the authors examine how platform teams source each feature they own: built from scratch, built on open source, or bought.
Teams that predominantly build from scratch were less likely to have a clear platform strategy and met fewer of their platform goals.
The piece argues that AI agents make building internal tools fast, but they remove the effort of writing code, not the work of owning it.
Maintenance, upgrades and governance remain, which makes build-versus-buy a sharper strategic decision than before.
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