Model Context Protocol
There is a particular kind of organizational irony that only surfaces in the middle years of a technology transition. It is not the irony of the early adopter who bet on the wrong horse. It is not the irony of the executive who ignored a trend until it was too
AI Agents
Picture this: it's 2:47 AM and your on-call engineer gets paged. A third-party CRM integration that powers your AI agent platform has started returning malformed responses. Within minutes, you discover that every tenant on your platform is now getting broken tool calls, hallucinated outputs, and failed workflows.
AI Agents
You've built a multi-tenant agentic platform. Your agents are running, your customers are onboarded, and everything looks great. Then, around month three, things start to get weird. Responses slow down. Agents start "forgetting" things they should know. Some tenants complain that their workflows feel sluggish, while
AI Agents
Here is a scenario that is becoming painfully familiar to platform engineers in 2026: your multi-tenant AI agent platform is humming along, serving dozens of enterprise customers simultaneously, when three things go wrong at once. Your primary foundation model hits its per-minute token rate limit. A high-priority tenant's
AI Agents
If you've spent any time in backend engineering Slack channels or engineering all-hands meetings in early 2026, you've probably heard some variation of the same panicked sentence: "We need to retrofit per-tenant memory eviction before this quarter ends." It's become one of
API Gateway
It has been a brutal few weeks for platform teams. The March 2026 wave of major AI model releases, from updated frontier reasoning models to a new generation of lightweight, edge-deployable agents, has done something no load test ever quite managed: it has exposed the quiet, compounding failures hiding inside
OpenTelemetry
If you are a backend engineer responsible for a production multi-agent LLM system in 2026, you have almost certainly hit the same wall: something broke in a pipeline that spans a planner agent, two tool-calling sub-agents, a retrieval step, and a final synthesis agent, and your observability stack told you
AI Infrastructure
There is a storm quietly forming at the intersection of cloud economics, agentic AI workloads, and distributed systems engineering. Most backend teams are not watching it closely enough. By Q3 2026, the per-tenant AI agent compute spot market will have matured to the point where preemption events are no longer
AI Agents
Long-running AI agent sessions are quietly bankrupting token budgets across multi-tenant LLM platforms. If you are operating a shared infrastructure where dozens or hundreds of tenants run concurrent agentic workflows, you have almost certainly hit the wall: a session that started as a focused task assistant has ballooned into a
AI Agents
There is a financial reckoning quietly building inside every SaaS company that embedded AI agents into their product in 2024 and 2025. It does not show up loudly in a single incident report. It accumulates slowly, invoice by invoice, sprint by sprint, until one day a VP of Engineering walks
LLM platforms
In the span of roughly three weeks this past March 2026, the AI industry did something it had never quite managed before: it released more than a dozen significant large language models simultaneously. Not sequentially. Not in a polite, one-per-month cadence that backend teams could absorb. All at once, in
LLM
Here is the uncomfortable truth that most platform engineers discover too late: when your shared GPU inference cluster hits 85% utilization at 2 AM on a Tuesday, your enterprise tier customers and your free tier users are, by default, fighting over the exact same queue. One badly-timed batch job from