Most enterprise AI deployments fail in production because the gap is context, not the model. The four motions of a context layer - ingest, organise, retrieve, refresh - explained for platform teams.

AI coding tools fail in production because they don’t have access to the context they need. This article explains the operational context layer your AI assistant is missing.

AI coding tools fail in production engineering orgs because they lack structured system-level context. Here's how to build the metadata infrastructure that produces reliable AI suggestions.

Context engineering is the architectural discipline that helps prevent enterprise LLM hallucinations. Here's what it covers, why most teams skip it, and how to audit your readiness before shipping.

August is a time for summer holidays and beaches and getting vaguely sunburnt but in a nice way. Or, if you're Roadie folks: building AI tools for Backstage. This month we integrated some MCP server tools, pushed out API token permissions for everyone, made mass updates possible, and moved OpenSearch to GA.

AI is already shaping internal developer portals, with platform teams testing RAG search, service ownership bots via Slack, and agent-driven automations. From the front lines, the same hurdles keep surfacing though: noisy context, hallucinations, fragmented tooling, and no governance. We take a look at how AI is affecting IDPs, and the challenges faced by the platform teams who run them.

AI cometh... In July we launched our MCP servers to help pull Roadie data into you LLM-powered IDEs and MCP Clients, took a step towards fully integrating RAG AI into Roadie Search, allowed customers to vibe code their own UIs and launched the Scheduler to improve transparency of the inner workings of Roadie. Oh, and updates to our Launch Darkly plugin.