Learn how to wire Meshery’s rich observability signals into an LLM-backed coding agent that can summarize logs, cluster anomalies, and help you diagnose production issues without jumping straight to fixes.
Learn how to translate plain-language infrastructure requirements into concrete cloud native designs using an LLM and Meshery’s Kanvas visual designer. This course covers the full loop from intent specification through AI-assisted proposal to human review and validation.
Build a practical mental model of large language models - what they actually do, where they break, and how to choose one for infrastructure tasks. No hype, just the mechanics you need to use LLMs confidently with Meshery.
Understand the Model Context Protocol (MCP) as an open standard for connecting agents and LLMs to external systems. Learn the core primitives, the client/server model, and when MCP is the right integration choice for infrastructure automation.
How coding agents work, from the agentic loop and tool use to infrastructure applications and human-in-the-loop guardrails.
Design, implement, test, and debug MCP tools and resources that expose Meshery’s infrastructure management capabilities to coding agents.
Prove you can use LLMs and coding agents to design, generate, deploy, and validate cloud native infrastructure with Meshery and Kanvas. The associate-level credential of the TCS Labs Academy.
Close the loop from AI-generated design to live cluster using mesheryctl, GitOps, and human-in-the-loop checkpoints.
Walk the complete cycle from a plain-language requirement to a reviewed Kanvas design, using an LLM as your design collaborator.
Produce valid, importable Meshery designs using an LLM, from understanding YAML structure through registry alignment and iterative refinement.
Wire a coding agent to Meshery’s programmatic surfaces and build an end-to-end automated infrastructure workflow.
Core mechanics of large language models for platform and infrastructure engineers who need to use them without being misled by them.
Learn the Model Context Protocol from first principles - what it is, its three core primitives, how the client/server model works, and when to use it for infrastructure automation.
Build and use the observability pipeline that connects Meshery’s signals to LLM-assisted diagnostics.
Run, interpret, and act on Meshery performance profiles using load generators and LLM-assisted analysis.
Practical prompt engineering techniques for platform engineers operating cloud native infrastructure through LLMs and coding agents.
Design and operate safe, agent-driven remediation workflows from closed-loop detection through evidence-based autonomy.
A structured module covering the agent-assisted incident response lifecycle, from triage to postmortem.
How to ground infrastructure agents in live cluster state and operational knowledge rather than stale training data.
Operational patterns for running agentic automations against cloud-native infrastructure without catastrophic side effects.
Apply Meshery’s policy engine and Kubernetes admission controls to validate and bound AI-generated infrastructure designs before deployment.
Learn to design, implement, and test Model Context Protocol tools that expose Meshery’s management capabilities to coding agents - from wrapping REST and GraphQL APIs to surfacing live cluster state as agent-readable resources.
Learn to generate valid, importable Meshery designs with an LLM: understand the YAML structure, craft precise prompts, align output with the Meshery registry, and iteratively refine designs in Kanvas.
Learn how to integrate coding agents into your incident response workflow for cloud-native operations - from triage through postmortem. This course covers the boundaries of safe agent autonomy and where human judgment must lead.
Learn how to write prompts that produce reliable, parseable output from an LLM when your inputs are real infrastructure state and your outputs drive real changes to a Kubernetes cluster.
Prove you can run cloud native infrastructure with coding agents in production - agentic day-2 operations, Model Context Protocol integrations with Meshery, safe automation, and governance. The professional-level credential of the TCS Labs Academy.
Learn how to close the loop between AI-generated infrastructure designs and live clusters by having a coding agent drive the deploy pipeline through mesheryctl and GitOps tooling.
Understand what a coding agent is, how the agentic loop works, and how to apply agents safely to infrastructure operations with appropriate human oversight.
Learn how to wire a coding agent to Meshery using MCP, the REST and GraphQL APIs, and mesheryctl to build automated, approval-gated infrastructure workflows.
Learn to run structured load tests against cloud native services using Meshery performance profiles, interpret results with an LLM, and make performance a durable quality gate in your deploy loop.
Learn how to design and operate agent-driven remediation workflows for cloud-native infrastructure - from closed-loop detection through safe automation, approval gates, and evidence-based autonomy growth.
Learn how to ground infrastructure agents in current, specific state rather than stale training data by building retrieval pipelines over cluster state, Meshery data, and operational knowledge bases.
Learn how to run LLM-driven automation agents against production infrastructure safely, covering least privilege, human approvals, sandboxing, and blast-radius controls.
Learn how to rigorously validate AI-generated infrastructure designs in Meshery before they reach your cluster, using policy enforcement, shift-left checks, and reusable guardrails.