It started with a post-mortem that nobody on the team wanted to write. A customer-facing AI agent, deployed by a mid-sized fintech company, had been silently routing support ticket escalations to the wrong queues for eleven days before anyone noticed. The integration test suite had passed with flying colors. The
It's 2:47 AM. Your on-call engineer gets paged. A swarm of AI agents, deployed last quarter to automate customer order processing, has entered a runaway loop. One agent failed silently, another tried to compensate, and within minutes, a cascade of retries and redundant tool calls has saturated
Here is the scenario your team is about to face, if it hasn't already: a product stakeholder walks into a sprint planning meeting and asks for a feature powered by a multi-step AI agent. The agent needs to retrieve context, call an LLM, validate the output, conditionally invoke
Agentic AI systems are no longer a forward-looking concept. In 2026, multi-agent orchestration frameworks are running inside production environments at banks, healthcare networks, logistics companies, and SaaS platforms at a scale that would have seemed ambitious just two years ago. These systems make decisions, call external APIs, write to databases,
Your distributed tracing stack was built to follow a request from an API gateway through a chain of microservices and back out again. It does that job beautifully. But in 2026, your backend is no longer just running microservices. It is orchestrating AI agents that plan, delegate, reason, and retry,