AI Agents
Here is a scenario that should feel familiar to any backend engineer who has experimented with AI agents in the last year or two: you build a multi-step agent, it handles the first task beautifully, and then, on the very next turn, it acts like it has never met you
AI Agents
It started with a Slack message nobody wanted to send. A platform engineering lead at a mid-sized SaaS company opened their cloud billing dashboard on a Monday morning in early 2026 and found a $340,000 LLM API invoice for a single weekend. The culprit: a newly deployed agentic workflow
Model Context Protocol
There is a quiet crisis unfolding in the backend infrastructure of enterprise AI teams right now, and most organizations won't feel it until a production deployment breaks in the worst possible way. Anthropic's Model Context Protocol (MCP) has crossed a critical threshold in 2026: it is
multi-agent AI
Agentic AI is no longer a prototype in a sandbox. As of early 2026, enterprise platform teams are running multi-agent workflows that autonomously browse the web, write and execute code, call third-party APIs, mutate database records, send emails, trigger financial transactions, and provision cloud infrastructure. According to Deloitte's
Agentic Workflows
Here is a scenario that every platform engineering team running multi-tenant AI infrastructure has either already lived through or is about to: it's 9:07 AM on a Tuesday, three of your largest enterprise tenants simultaneously trigger high-volume agentic pipelines, and within 90 seconds your foundation model provider
Multi-Agent Systems
When the platform engineering team at a mid-sized fintech company (we will call them Meridian Financial Labs) first deployed their multi-agent orchestration layer in late 2024, everything looked fine on the surface. Pipelines completed. Dashboards were green. SLAs were being met. It was not until a routine audit of their
AI Security
If you have been building multi-step agentic workflows in the past year, you have almost certainly hit a new wall: a security review that stops your deployment cold and asks a question that did not exist two years ago. The question is something like: "Can this AI agent cryptographically
AI Agents
There is a silent crisis unfolding inside enterprise agentic systems right now, and most engineering teams are not catching it until it is far too late. Your long-running AI agents are losing tenant context. Not dramatically, not in ways that trigger alerts, but in small, compounding ways that corrupt the
AI Agents
It starts as a quiet anomaly. A tenant's long-running agentic workflow, one that had been reliably orchestrating document processing, tool calls, and memory retrieval for weeks, suddenly starts producing malformed outputs. No deployment happened. No configuration changed. The only thing that shifted was a silent foundation model upgrade
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
Something quietly broke in the standard AI infrastructure playbook the moment agentic workflows stopped being a novelty and became a production reality. In early 2026, teams are no longer asking whether their AI agents need durable memory. They are asking a far harder question: which backend can actually keep per-tenant
AI Observability
There is a quiet crisis unfolding inside the infrastructure teams of nearly every SaaS company that has shipped an AI-powered product in the last two years. The crisis is not a model accuracy problem. It is not a latency problem, exactly. It is a visibility problem, and it is getting