AI Engineering
If you run a multi-tenant LLM platform in 2026, you already know the pain: one provider spikes their token pricing at 2 AM, another throttles your highest-tier tenants during peak hours, and suddenly your SLA dashboard lights up like a Christmas tree. The naive solution is to hard-code a fallback
AI Security
When Anthropic introduced the Model Context Protocol (MCP) in late 2024, most backend engineers treated it as a convenient plumbing upgrade: a standardized way to connect AI agents to tools, APIs, and data sources. By early 2026, MCP has become the de facto lingua franca of agentic AI infrastructure. Hundreds
AI Agents
If you work on the backend of any SaaS product that has shipped an AI agent feature in the past year or two, you have probably heard some version of this conversation: "Wait, our AI costs tripled last month. Which tenant is responsible?" Silence follows. Nobody knows. The
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
AI Agents
There is a quiet performance crisis unfolding inside most multi-tenant LLM platforms right now. It does not show up in your p50 dashboards. It rarely triggers an on-call alert. But your highest-value tenants feel it every single time they spin up a new agent session after an idle period: a
AI Agents
Imagine you are running a SaaS platform that serves dozens of enterprise clients. Each client, or "tenant," has their own data, their own regulatory obligations, and their own risk appetite. Now imagine that each of those tenants has been given access to an AI agent that can browse
Temporal
Imagine you are running a SaaS platform where every customer gets their own AI agent: a long-running, tool-calling, decision-making entity that can spend hours or even days autonomously completing tasks. Now imagine 5,000 of those agents firing simultaneously, each touching different data, calling different APIs, and operating under different
AI Agents
In January 2026, the engineering team at Meridian Analytics, a mid-size B2B SaaS company serving around 340 enterprise tenants, discovered something that kept their VP of Engineering awake for several nights in a row. A routine audit of billing reconciliation logs revealed that a non-trivial subset of tenants had been
AI Engineering
It's 2:47 AM. Your on-call phone buzzes. OpenAI, Anthropic, or one of the newer frontier model providers has just gone dark. Your multi-tenant LLM platform serves 3,000 paying customers, and every single one of them is about to hit a wall of 503 errors. Your enterprise
AI Governance
There is a quiet but widening fault line running through the engineering floors of SaaS companies right now. On one side, you have backend engineers doing what they have always done: treating per-tenant AI agent governance as an architecture challenge. Rate limits, token budgets, prompt isolation, data sandboxing. Clean, solvable,
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
multi-tenant LLM
There is a quiet architectural crisis unfolding inside every serious multi-tenant LLM platform right now. As agentic AI systems move from single-session demos into persistent, cross-session workflows serving thousands of tenants simultaneously, the question of where and how you store per-tenant agent memory has shifted from an engineering footnote to