AI Agents
Here is the uncomfortable truth about enterprise AI agent deployments in 2026: most teams are shipping multi-agent pipelines the same way they shipped monolith APIs in 2012. They push a new configuration, hope it works, and scramble when a prompt regression or a broken tool-call chain takes down a production
AI Agents
It is a quiet kind of disaster. A long-running AI agent workflow, one that has been executing for 47 minutes, coordinating tool calls, managing sub-agent trees, and accumulating expensive LLM-generated state, hits a transient network fault. And then, because of a fundamental misunderstanding baked into the checkpoint-and-resume architecture, the entire
AI Agents
There is a quiet crisis unfolding in enterprise backend infrastructure right now. AI agents are getting smarter, longer-running, and more deeply embedded in mission-critical workflows. But the infrastructure underneath them, specifically the ephemeral compute environments popularized by serverless functions, containerized microservices, and spot-instance orchestration, was never designed to hold state
AI Agents
It started with a support ticket that seemed almost too mundane to escalate. A sales operations manager at a mid-market manufacturing firm reported that her AI assistant "occasionally forgets what it just looked at." The support team at Velorix, a B2B SaaS platform providing AI-powered revenue operations tooling,
Workflow Orchestration
There is a moment every platform engineering team dreads: the Monday morning Slack message that reads, "The agent pipeline is backed up. We have 40,000 tasks queued and nothing is moving." In 2026, that moment arrives faster than ever. As enterprise AI systems graduate from single-model inference
Multi-Agent Systems
Long-running multi-agent pipelines are no longer a research curiosity. By 2026, enterprise backend teams are routinely deploying orchestration systems where dozens of specialized AI agents collaborate across hours or even days to complete complex workflows: financial audits, autonomous code review cycles, supply chain optimization runs, and multi-step document processing at
agentic AI
There is a specific kind of technical debt that does not announce itself. It does not show up in your sprint retrospectives, your architecture review boards, or your quarterly engineering health reports. It accumulates quietly, buried under layers of custom middleware, homegrown retry logic, and YAML files that only two
agentic AI
Agentic AI systems have moved from experimental sandboxes to production-critical infrastructure at an astonishing pace. In 2026, engineering teams are no longer asking whether to deploy multi-step agentic workflows; they are asking how to keep them from collapsing under their own weight. The orchestration layer, the central nervous system that
Temporal
Here is a scenario that is becoming increasingly common in 2026: your SaaS platform runs dozens of AI agents simultaneously, each one belonging to a different tenant, each one chaining together calls across GPT-5, Claude 4, Gemini Ultra 2, and your own fine-tuned domain model. An agent kicks off a
agentic AI
Something shifted in early 2026. It wasn't a single model release or a single product launch. It was the moment that agentic AI systems stopped being demos and started being deployed tenants inside production SaaS platforms. Across the industry, engineering teams are waking up to a problem that
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
agentic AI
In early 2026, a promising legal technology startup we'll call LexOrbit was riding the wave of agentic AI adoption. Their flagship product: a multi-tenant SaaS platform that deployed autonomous AI agent pipelines for law firms , automating contract review, case research, document summarization, compliance checks, and client intake processing.