The most powerful application of this stack is dynamic capacity planning using GPU slicing. In a traditional setup, a single pod might monopolize an entire GPU even if it only needs a fraction of the compute power. This inefficiency leads to the resource contention we saw in our opening story. Our AI agent, equipped with the ability to manipulate NVIDIA MIG (Multi-Instance GPU) profiles, can solve this on the fly. When the Kiro-cli agent identifies that a high-priority inference job is being starved by a low-priority training job, it can command the cluster to reconfigure the MIG geometry. This effectively slices the physical GPU into smaller, isolated instances, ensuring that the critical workload gets dedicated bandwidth that the noisy neighbor cannot touch.
This level of automation goes beyond simple horizontal pod autoscaling. It involves reconfiguring the hardware abstraction layer itself. The agent can calculate the optimal slice size—say, 3g.20gb for the training job and 4g.40gb for the inference engine—and apply the configuration via a DaemonSet update or a dynamic resource claim. This capability is essential when managing expensive hardware; it maximizes utilization while guaranteeing Quality of Service. Furthermore, by integrating security scanning into this loop, the agent can ensure that the new configuration complies with Transparent Data Encryption (TDE) policies, verifying that the isolation extends to memory encryption keys as well.
apiVersion: v1
kind: ConfigMap
metadata:
name: nvidia-mig-config
data:
config.yaml: |
version: v1
mig-configs:
all-balanced:
- devices:
mig-enabled: true
mig-devices:
"1g.5gb": 7
ai-agent-optimized:
- devices:
mig-enabled: true
mig-devices:
"3g.20gb": 2💻 Conclusion
The convergence of n8n 2.0, Kiro-cli 1.23.0, and headless AI models is creating a new paradigm for infrastructure operations. We are moving away from static scripts and manual runbooks toward dynamic, intelligent agents that can reason about the state of a Kubernetes AI deployment. By delegating the complex tasks of monitoring, GPU slicing, and decision-making between runtimes like KServe vs Seldon to these automated systems, we free up human engineers to focus on architecture and strategy rather than firefighting. While tools like Lovable may offer a glimpse into the future of frontend generation, the heavy lifting of backend reliability is being revolutionized by these robust, agentic workflows. As NetworkChuck and Nick Puru have demonstrated, the technology to build a digital IT department is available today; the only limit is our willingness to trust the agents with the keys to the cluster.
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