Production-Agents

Most AI agent frameworks are built to work. Dapr Agents is built to survive. That’s the core pitch behind the Dapr Agents v1.0 general availability announcement, made by the Cloud Native Computing Foundation (CNCF) at KubeCon + CloudNativeCon Europe 2026 in Amsterdam on March 23rd. While the rest of the agentic AI ecosystem debates which LLM to use and which reasoning framework is smarter, Dapr Agents has been solving a quieter but arguably more fundamental problem: what happens to your agent when the Kubernetes node it’s running on dies? ...

One of the persistent frustrations with AI agents in production is that nobody agrees on how to know if they’re working correctly. Solo.io is taking a shot at solving that with agentevals, an open-source project launched at KubeCon + CloudNativeCon Europe 2026 in Amsterdam. The premise is straightforward but the execution is non-trivial: continuously score your agents’ behavior against defined benchmarks, using your existing observability data, across any LLM model or framework. Not a one-time evaluation. Not a test suite that only runs before deployment. A live, ongoing signal. ...

Most of the AI agent conversation focuses on intelligence: which model, which framework, which prompting strategy produces the best results. Dapr Agents v1.0, announced generally available at KubeCon + CloudNativeCon Europe 2026 in Amsterdam, focuses on a different problem entirely: survival. What happens to your AI agent when a Kubernetes node restarts mid-task? When a network partition interrupts a long-running workflow? When your cluster scales down to zero overnight? For most frameworks, the answer is: the agent dies and you start over. ...

Full autonomy is the goal for many agentic workflows — but full autonomy is also where most production deployments fail their first risk review. The practical path to deploying AI agents in real organizations runs through human-in-the-loop (HITL) patterns: workflows where the agent does the work, humans approve the decisions, and the system handles the handoff cleanly. LangGraph has strong native support for HITL patterns through its interrupt primitives. This guide walks through the core patterns — interrupt points, approval gates, and reversible actions — with working code you can adapt for your own agent workflows. ...

Alongside Dapr Agents v1.0 and the CNCF AI Conformance Program updates, KubeCon Europe 2026 delivered a third piece of production AI agent infrastructure: agentevals, a new open-source project from Solo.io that brings continuous behavioral scoring to agent deployments. The problem agentevals addresses is deceptively simple to state and surprisingly hard to solve: how do you know if your production AI agent is still doing what it’s supposed to do? What agentevals Does Most AI agent evaluation today happens at development time — you run evals before deploying, decide the agent is good enough, and ship it. What happens after deployment is typically monitored through logs and user feedback, not through continuous automated assessment. ...