AI on TCS Labs Academy

1. AI-Assisted Observability & Diagnostics

Mon, 01 Jan 0001 00:00:00 +0000

Modern cloud native systems produce more signal than any engineer can manually process: MeshSync continuously reconciles cluster state, Kubernetes events stream component lifecycle changes, and Prometheus scrapes hundreds of metrics every few seconds. The challenge is no longer collecting data - it is making sense of it quickly enough to matter.

This course shows you how to pair Meshery’s observability layer with a coding agent to close that gap. You will learn which signals Meshery surfaces and why they are well-suited to LLM analysis, how to feed logs and events to an agent in a grounded, context-bounded way, how to construct diagnostic prompts that yield ranked hypotheses rather than speculative fixes, and how to follow a disciplined symptom-to-hypothesis workflow that keeps the agent useful without letting it run ahead of the evidence.

1. Designing Infrastructure with AI + Kanvas

Mon, 01 Jan 0001 00:00:00 +0000

Platform and infrastructure engineers increasingly work alongside coding agents and LLMs to turn high-level requirements into deployable cloud native topologies. This course establishes the foundational workflow: writing a clear infrastructure brief, letting an LLM propose a design, collaborating with that design in Kanvas, and critically reviewing the result before it reaches your cluster.

You will learn what an LLM needs from you to produce a useful proposal, how the human-in-the-loop checkpoint inside Kanvas prevents silent errors from reaching production, and what a structured review checklist looks like for AI-generated infrastructure. Each lesson builds on the previous one, so by the end you can run the full intent-to-design cycle on your own projects.

1. How LLMs Work for Infrastructure Engineers

Mon, 01 Jan 0001 00:00:00 +0000

Large language models are probability engines that predict the next token in a sequence - not knowledge databases, not reasoning systems, and certainly not reliable sources of ground truth about your cluster state. Before you wire one into a Meshery workflow, you need a clear picture of the mechanism underneath.

This course strips away the marketing language and gives you the mental model that matters for ops work: how next-token prediction produces useful outputs, what tokens and context windows mean for how much infrastructure state you can pass in, where LLMs fail confidently and why that matters more than where they succeed, and how to match model capability to the actual task at hand - classification, generation, or structured reasoning.

1. MCP Fundamentals

Mon, 01 Jan 0001 00:00:00 +0000

The Model Context Protocol (MCP) defines a common interface that lets agents and large language models connect to tools, data sources, and services without bespoke glue code for every combination. For infrastructure engineers, MCP shifts the integration problem from “how do I wire this agent to my platform” to “how do I expose the right capabilities safely and consistently.”

This course covers the foundational concepts you need before building MCP integrations for Meshery. You will learn what MCP is and why it exists, what the three core primitives are, how the client/server transport model works, and how to decide when MCP is the right architectural choice versus a plain script or CLI wrapper.

Certified AI-Native Infrastructure Associate (CAINA)

Mon, 01 Jan 0001 00:00:00 +0000

The Certified AI-Native Infrastructure Associate (CAINA) is the entry-level credential of the TCS Labs Academy. It validates that you can put LLMs and coding agents to work on real cloud native infrastructure - turning intent into a Meshery design, deploying it, and validating it - rather than just talking about AI in the abstract.

How you earn it 🔗

The credential has two required parts:

A written exam - 50 multiple-choice and scenario questions across the five domains above (75 minutes, proctored). Pass mark 70%.
A hands-on lab - generate a design with an LLM, deploy it with a coding agent and Meshery, and validate it. Delivered as the CAINA Capstone challenge.

How to prepare 🔗

Work through Learning Paths 1-3, then attempt the Ship It with an Agent challenge as a warm-up. The competency blueprint above shows where to focus your study time.

LLM Basics

Mon, 01 Jan 0001 00:00:00 +0000

2. Building MCP Tools for Meshery

Mon, 01 Jan 0001 00:00:00 +0000

This course takes you from MCP theory to working tool implementations. You will design well-scoped tools that give an agent precise control over Meshery without exposing unsafe surface area, back those tools with Meshery’s REST and GraphQL APIs, and model live cluster state from MeshSync as first-class MCP resources an agent can fetch on demand.

Each lesson builds on the last: good tool design shapes the API mapping, the API mapping determines what state is available as resources, and solid testing practices ensure the whole stack is reliable before you connect a real agent. By the end of the course you will have a working MCP server that an agent can use to inspect workloads, list designs, and query cluster state - all without requiring the agent to understand Meshery’s internal APIs directly.

2. Generating Meshery Designs with LLMs

Mon, 01 Jan 0001 00:00:00 +0000

Meshery designs are valid Kubernetes YAML - multi-document files that Meshery imports, visualises, and deploys. Getting an LLM to produce designs that pass import and validation requires more than asking it to “write some YAML”. You need to understand the structure a design must have, give the model the right constraints and context, verify that generated resources align with Meshery’s component registry, and refine iteratively rather than starting from scratch on every failure.

2. Incident Response with Coding Agents

Mon, 01 Jan 0001 00:00:00 +0000

Incidents do not wait for business hours, and human responders cannot simultaneously gather telemetry, search runbooks, and coordinate remediation without dropping context. Coding agents can close that gap - not by replacing the on-call engineer, but by compressing the time between alert and diagnosis.

This course walks through the full incident lifecycle: detecting and triaging a failure, executing structured runbooks with agent assistance, working through a realistic crashloop recovery, and then using an LLM to draft a blameless postmortem from the incident timeline. Each lesson is grounded in real Meshery and Kubernetes tooling, with explicit checkpoints for human review and approval.

2. Prompt Engineering for Cloud Native Operations

Mon, 01 Jan 0001 00:00:00 +0000

A prompt is not a search query. When you attach an LLM to a live Kubernetes cluster through Meshery, the quality of your prompts determines whether the agent converges on a safe, correct action or drifts into hallucinated resource names and fabricated flags. This course treats prompt engineering as an operational discipline with the same rigor you would apply to any other systems interface.

You will move through five tightly scoped lessons: defining the role split between system and user instructions, grounding every prompt in real cluster state from kubectl or MeshSync, coercing the model into machine-readable YAML and JSON, reusing proven prompt patterns for common ops tasks, and building a small eval set so you catch regressions before they reach production.

2. Securing AI-Driven Pipelines

Mon, 01 Jan 0001 00:00:00 +0000

AI-driven pipelines introduce a new class of infrastructure actor: an autonomous agent that reads context, generates manifests, and applies changes to live clusters. That power demands a matching security discipline. A misconfigured agent can leak credentials, apply unreviewed code, or grant itself elevated permissions - at machine speed, without a human in the loop.

This course walks through the four pillars of secure agent pipelines: managing secrets safely so they never appear in prompts or generated designs; verifying the provenance and integrity of AI-generated manifests and images before trust is granted; isolating agent workloads so a compromise cannot spread; and designing minimal, auditable permissions for every automated identity. Each lesson provides concrete patterns you can apply immediately when operating Meshery and Kanvas in production.

Certified AI-Native Infrastructure Professional (CAINP)

Mon, 01 Jan 0001 00:00:00 +0000

The Certified AI-Native Infrastructure Professional (CAINP) is the TCS Labs Academy’s advanced credential. It validates that you can operate cloud native infrastructure with coding agents in production - observing, diagnosing, responding, performance-testing, and self-healing with Meshery - and that you can integrate agents safely with Meshery through the Model Context Protocol while governing the whole pipeline.

How you earn it 🔗

The credential has two required parts:

3. Agent-Driven Deployment

Mon, 01 Jan 0001 00:00:00 +0000

Platform engineers rarely deploy by hand anymore - the same coding agents that generate and refine a Meshery design can also commit that design to Git, run a dry-run diff, and push it to a cluster once a human approves the output. This course walks through every stage of that loop with concrete commands and realistic scenarios.

You will learn how to wire an agent into a GitOps pipeline so that mesheryctl is the enforced execution path, how to read a dry-run diff and reason about blast radius before applying anything, and how to recover cleanly when a deployment goes wrong. The four lessons move in order from architecture to practice: the big-picture loop, safe preview workflows, a hands-on deployment walkthrough, and finally rollback and recovery.

3. Coding Agents 101

Mon, 01 Jan 0001 00:00:00 +0000

Coding agents are the next step beyond chat-based LLM interaction. Instead of answering a single question, an agent can read files, execute commands, observe the results, and iterate - working through a task over multiple steps without requiring a prompt for each action.

For platform engineers, this shift is significant. The same workflows you run manually - inspecting cluster state, diffing a configuration, applying a design, validating output - can be delegated to an agent that understands context, uses tools, and asks for approval before doing anything destructive.

3. Integrating Coding Agents with Meshery

Mon, 01 Jan 0001 00:00:00 +0000

Modern platform engineering increasingly relies on coding agents to automate routine infrastructure tasks - reading cluster state, proposing design changes, and deploying workloads without manual intervention at every step. Meshery provides multiple programmatic surfaces - an MCP server, a REST API, a GraphQL API, and the mesheryctl CLI - that a coding agent can combine to act as a capable infrastructure operator.

This course walks through each surface in detail: how to authenticate and connect an agent, when to reach for GraphQL versus REST, and how to combine MCP tools with mesheryctl for tasks that require both structured reads and imperative actions. The course concludes with a complete end-to-end workflow in which an agent reads live cluster state, proposes a design change, deploys it through an approval gate, and verifies the outcome - a pattern directly applicable to production platform automation.

3. Performance Management with AI

Mon, 01 Jan 0001 00:00:00 +0000

Performance problems in cloud native systems surface in subtle ways - a p99 latency spike after a routine rollout, a drop in throughput after a config change, or a memory leak that only appears under sustained load. Catching these issues early requires repeatable, automated testing tied directly to the change lifecycle.

This course walks you through Meshery’s performance management capabilities: defining profiles that capture service-level objectives, selecting the right load generator for each workload type, and feeding raw results to a coding agent that interprets the numbers and proposes targeted tuning actions. You will also learn how to compare runs against a stored baseline so that regressions are caught - and blocked - before they reach production.

3. Responsible AI for Operations

Mon, 01 Jan 0001 00:00:00 +0000

AI agents can propose, generate, and apply infrastructure changes at a speed and scale that outpaces traditional review. That capability is only safe when it is surrounded by deliberate controls - validation layers that catch hallucinated resources before they reach a cluster, audit trails that record every proposal and its rationale, cost budgets that prevent runaway token and cloud spend, and clear human ownership that ensures someone is always accountable for what the agent does.

4. Automated Remediation & Self-Healing

Mon, 01 Jan 0001 00:00:00 +0000

Kubernetes already self-heals within narrow boundaries - it restarts crashed containers, reschedules evicted pods, and maintains replica counts. But cluster-level self-healing stops there. Configuration drift, resource exhaustion, dependency failures, and cross-service cascades all require reasoning that goes well beyond what a controller loop can express.

Coding agents close that gap. Given the right tooling, an agent can detect a degraded condition, query state from Meshery and your observability stack, propose a concrete remediation action, submit it for human approval, execute it, and then verify the result - all in a structured, auditable loop. This course teaches you how to design, build, and govern that loop safely.

4. Retrieval & Context (RAG) for Infrastructure

Mon, 01 Jan 0001 00:00:00 +0000

An agent that can only reason over its training data is an agent that will confidently describe a cluster that no longer exists. The training cutoff is months or years in the past; your cluster changes daily. Retrieval-augmented generation (RAG) is the bridge between a model’s frozen weights and the live state of your infrastructure.

This course teaches you to think about context as a resource to be managed. You will learn why context windows are finite and why that constraint shapes every design decision in an agent-powered ops workflow. You will see exactly which sources of truth - kubectl, mesheryctl, MeshSync, and Meshery designs - contain the state an agent needs, and how to surface that state at query time rather than baking it into a prompt upfront.

4. Safe Automation

Mon, 01 Jan 0001 00:00:00 +0000

Handing control of live infrastructure to an autonomous agent is one of the highest-leverage moves available to a platform team - and one of the easiest ways to cause a serious outage. This course teaches the engineering disciplines that make agentic automation trustworthy: scoping identities to the minimum needed, requiring human sign-off on mutations, proving behaviors in safe environments before they touch production, and capping how much damage a misbehaving agent can do in a single run. Each lesson draws on real Kubernetes RBAC primitives, Meshery workspace controls, and operational patterns you can apply immediately.

4. Validating AI-Generated Infrastructure

Mon, 01 Jan 0001 00:00:00 +0000

An LLM can produce a syntactically valid Kubernetes manifest in seconds, but syntactic correctness is not the same as operational correctness. Missing resource limits, label selector mismatches, or absent NetworkPolicies pass a YAML linter yet cause outages or security gaps the moment they land in a cluster.

This course teaches you to treat validation as a first-class step in the AI-assisted infrastructure workflow. You will learn how Meshery’s model-driven policy engine catches semantic errors that plain schema checks miss, how to apply shift-left techniques to AI output before a single kubectl apply runs, and how to design durable guardrails that bound what generated infra can do. The course closes with a concrete review checklist you can carry into every pull request that contains AI-generated infrastructure.