Every DevOps or platform engineer knows the story: patching, updates, and upgrades are never-ending tasks. Kubernetes, Istio, Keycloak, Cilium, Elasticsearch, Prometheus, cert-manager, Kafka—there’s always another version just around the corner. Each upgrade means reading endless release notes, worrying about breaking changes, editing complex Infrastructure as Code (IaC), and spending weeks validating before production.
We built Chkk Lifecycle Management (LCM) Agents to automate this work. These agents don’t just throw out suggestions—they observe your environment, reason with real source knowledge, and generate safe, reviewable IaC pull requests you can trust. What once took weeks can now be compressed into hours, even minutes, with humans still firmly in control.
And to let everyone try this firsthand, we’re opening access in the Free Tier with the cert-manager project. Paid plans include coverage across 300+ Cloud Native projects.
We’ve all seen how AI can accelerate knowledge work—summarizing documents, planning tasks, even writing code. These advances are real, but they alone leave much to be desired. For example, why can’t AI just figure out lifecycle management for a complex infrastructure running hundreds of interdependent components?
The reality is that it can’t without the relevant context. Dropping a brilliant young engineer into your team without context is setting them up for failure. They’re missing the organizational playbook, the policies that govern change, the tribal knowledge around past incidents, the integration points with downstream teams, and the cadence of maintenance windows. No matter how talented, without that context they’ll make avoidable mistakes. AI is no different.
Lifecycle management of cloud native infrastructure is deeply interconnected. It’s not just about upgrading a single service—it’s about understanding the ripple effects: API changes, client compatibility, compliance requirements, and custom IaC patterns unique to your environment. Without structure, agents fail.
Our insight is simple: AI agents can deliver—if context is redesigned around them. They need the right context at the right time, paired with deterministic tools and grounded in source truth.
In software development, we already know that structured workflows deliver results. Tools like Cursor, Claude Code, Codex, and others have shown how powerful this approach can be: read the code, propose a change, run the tests, refine, repeat—until the result is correct.
Infrastructure lifecycle management, however, is an entirely different domain. It is incredibly challenging and complex work. To date, organizations have relied on subject matter experts across networking, databases, schedulers, and other components simply to keep the lights on. This infrastructure is mission-critical for most enterprises. A change is not as simple as updating a script and running unit tests. Every change has to be validated in the context of the entire system. Even a minor configuration tweak can alter behavior in ways that disrupt applications and, ultimately, the business.
That’s why we rely on platform engineers, DevOps engineers, and infrastructure engineers to carry out this work. To validate even a small change, they need a detailed understanding of the running topology. They run smoke tests and regression tests to confirm nothing breaks downstream. They ensure changes work not only in development or sandbox environments but also in staging and, most critically, in production—where scale itself becomes another variable. The infrastructure is so interconnected that something as routine as moving to a new operating system version can disrupt applications if proper guardrails are not in place.
This level of complexity is why lifecycle management is less deterministic than coding, involves far more moving pieces, and demands a deep understanding of system state—configuration, intended, and running. The training data available to large language models is not sufficient to automate these workflows outright. What’s needed is organizational and environment-specific context.
That’s exactly what Chkk LCM Agents are built to provide. Instead of improvising, they follow well-defined, use-case-specific workflows that humans and agents can co-execute. They begin by observing state across your infrastructure—inventory, topology, deployment systems, and more. They then ground their decisions in reality by pulling from trusted sources such as migration guides, release notes, official documentation, and source code. With this foundation, they move into planning, assembling change steps that account for your exact environment and potential impacts on applications. Finally, they generate precise Infrastructure as Code (IaC) pull requests that preserve your existing customizations while keeping engineers in full control of every change.
On the surface, an upgrade can look deceptively simple—a version bump, a few commands, and you’re done. But anyone who has carried upgrades through in production knows that reality is far more complex.
The first challenge is situational awareness: what exactly is running today? Teams need to classify every add-on, service and project—whether Istio is deployed in sidecar mode, which Keycloak realm strategies are configured, whether Hubble is enabled with Cilium, how Elasticsearch shards are distributed, and much more. Equally important is understanding how each component is deployed: Helm, Kustomize, ArgoCD, Terraform, Pulumi, or raw kubectl. Without this knowledge, progress quickly stalls.
The next step is selecting a viable target version. That decision requires balancing end-of-life schedules, compatibility across the stack, and organizational policies. Once a target is chosen, the real work begins: sifting through release notes, migration guides, GitHub releases, and upgrade documentation—not only for the target version, but for each intermediate hop. The task is to separate noise from signal and isolate the changes that matter for your exact topology.
From there comes planning and coordination. Some changes are harmless; others break APIs, ripple into client applications, or demand schema migrations. This requires mapping impacts, booking maintenance windows, notifying stakeholders, and drafting change plans before anyone edits Infrastructure as Code.
Then comes the implementation challenge. Helm charts alone can involve dozens of templates, values files, and CRDs. Add customizations—forked charts, umbrella charts, values overrides, Kustomize overlays—and the work quickly turns into a delicate three-way merge between the current version, your modifications, and the target release. Each step introduces the risk of drift or breakage.
Finally, there is validation and rollout: linting, compiling, deploying to development, smoke testing, staging, and production. Rollback readiness must always be in place in case things go wrong.
And this is just for a single project. Most environments run 40–60 such projects, each with unique quirks and dependencies. Some are simple, while stateful systems like Keycloak or Kafka rival the complexity of self-managed Kubernetes. With five to ten upgrades per project per year, the workload compounds quickly. One upgrade can consume one to two months of effort; for a typical infrastructure, it becomes a permanent workstream.
At the heart of this launch are the Chkk Lifecycle Management Agents for cloud-native infrastructure and projects—purpose-built to handle the realities of upgrades in environments where reliability and precision are non-negotiable. Each focuses on a different phase of the work, and together they transform what is normally a fragile, manual process into something structured and predictable.
Planning is where it begins. This isn’t just one agent but a process that combines multiple agents, tools, and curated artifacts to give engineers a clear view of what lies ahead. It produces three key outputs:
Upgrade Assessments: high-level reports that map the scope, impact, and dependencies of upgrading your Cloud Native infrastructure—along with its add-ons, application services and OSS projects—to the next version. Assessments comprehensively map what needs attention (control plane, nodes, OSS projects), flag early risks like deprecated APIs or breaking changes, and surface potential blockers across both platform and application layers. They help teams understand complexity before committing effort, enable proactive issue resolution, and provide a foundation for requesting more detailed upgrade plans.
Upgrade Plans: environment-specific workflows that provide step-by-step instructions for safely upgrading Cloud Native infrastructure, add-ons, application services, and OSS projects. These plans are workflows that humans and agents can co-execute. They include justifications from authoritative sources, explicit breaking-change notes, application-client impacts, and previews of IaC diffs.
Upgrade Context: the minimal relevant, environment-aware context that powers both the Upgrade Agent. It includes compatible target versions, environment-specific diffs, reviewer notes, security fixes, notable features, and application-client changes—all grounded in the running topology.
With these artifacts in place, the Upgrade Agent takes over execution. It generates precise, environment-aware Infrastructure as Code pull requests that preserve customizations while explicitly handling schema or API migrations. Engineers stay in control: every change is reviewable, auditable, and tied back to source truth.
Supporting these phases are specialized sub-agents. They parse and condense upstream release information, detect breaking changes before they land in production, recognize IaC patterns, analyze dependency blast radius, and verify upgrades through techniques like digital-twin testing or policy enforcement.
Together, these capabilities allow upgrades to be carried out with a level of predictability that ad-hoc scripts or generic AI assistants cannot provide. The outcome is faster, safer, and more controlled upgrades—without sacrificing trust.
The difficulty of upgrades lies in the fact that so many steps are interdependent. Without a clear structure, the process becomes brittle and failure-prone. Chkk LCM Agents solve this by transforming upgrades into a repeatable, disciplined workflow where every stage is guided by context and anchored in source truth.
It starts with intent. A simple request—“upgrade Istio to a supported version”—becomes the entry point. Instead of blindly fetching the latest release, the system interprets the request against your classified inventory, deployment systems, and dependency graph. From that analysis, it assembles the context that matters: which workloads will be affected, what dependencies are involved, and which compatibility constraints or support timelines apply.
This process is powered by multiple agents and tools that produce Upgrade Assessments, Upgrade Plans, and Upgrade Context. Assessments map scope and risks, Plans define environment-specific workflows, and Context provides just-enough, just-in-time details such as compatible versions, IaC diffs, and reviewer notes. Together, they explain not just what needs to be done, but why—citing authoritative sources like end-of-life schedules, breaking changes, CVEs, and stability fixes.
Once a plan is approved, the Upgrade Agent executes. It generates environment-aware pull requests that preserve customizations, whether in Helm values, CRD definitions, or overlays. When schema migrations or API changes are required, they’re handled explicitly. The result is a pull request that looks like it came from one of your own engineers—only delivered in hours instead of weeks.
Before rollout, teams can opt for preverification. Digital twin testing, policy checks, and canary rollouts validate changes in controlled conditions. After rollout, automated health checks confirm system stability and leave behind a complete audit trail.
The effect is tangible. Instead of combing through noise, second-guessing compatibility, and wrestling with IaC merges, engineers get a structured path from request to result. The outcome: fewer false starts, fewer failed upgrades, and fewer operational fires sparked by brittle processes.
Behind the agents sits the Chkk Platform, the system that makes automation safe, precise, and trustworthy. It combines knowledge, artifacts, classification, and workflows into a foundation that both humans and agents can reliably operate on.
It begins with Collective Learning, Chkk’s always-on knowledge refinery. This layer continuously ingests upstream signals—release notes, migration guides, GitHub issues, container registries, cloud bulletins, and vendor blogs. Specialized AI agents process this messy, unstructured data through an ETL pipeline: extract relevant fragments, transform them into Chkk’s canonical schema, and validate them against the original sources. Every fact is tied back to authoritative documentation, eliminating hallucinations and ensuring auditability. From this emerge two critical data stores: the Risk Signature Database (RSig DB), which catalogs risks, their triggers, and mitigations, and the Knowledge Graph, which encodes compatibility edges, version metadata, component hierarchies, and end-of-life schedules. Together, they form a living, evolving model of the cloud-native ecosystem.
In parallel, Artifact Collection focuses on the customer’s environment. It gathers private configuration and metadata—kept separate from upstream data—to create a secure, auditable timeline of how the environment evolves. On top of this, Classification enriches the picture, resolving opaque signals like cluster ID, namespace, and hash into detailed inventory records. This is what connects what’s running in your environment with the broader ecosystem intelligence.
Once classification is complete, Contextualization and Deep Analysis add situational intelligence. Irrelevant upstream noise is pruned away, and only the deltas that matter for your environment are retained. Dependency graphs are traversed across control planes, nodes, add-ons, and services to reveal blast radii and ripple effects: API deprecations, IAM shifts, OS/kernel updates, and configuration drifts.
At the core are the Reasoning and Generation Engines, which reconcile two truths: what’s running in your environment and what’s happening upstream. These engines power the Upgrade Copilot, Artifact Register, and Risk Ledger.
Finally, upgrades and mitigations are carried out through Durable Workflows. Every action—whether it’s parsing release notes, or drafting IaC pull requests—can be executed by an agent, a human, or a combination of both. Engineers remain in control: they can choose to review, approve, or modify each step, while delegating repetitive or mechanical work to the agents. This co-execution model turns upgrades into a collaborative process where automation accelerates progress but never removes oversight.
The result is automation you can trust. Workflows are both automated and auditable, with every step linked back to source truth. Engineers decide where to intervene and where to delegate, blending human expertise with machine execution to achieve outcomes that are safe, predictable, and efficient.
When upgrades shift from manual projects to a structured, context-driven process, the outcomes speak for themselves.
The first and most obvious win is time. What once took weeks of effort per upgrade—combing through release notes, drafting IaC changes, validating across environments—can now be compressed into hours, sometimes even minutes. Engineers reclaim time that would otherwise be lost to repetitive maintenance work.
The second outcome is reduction in risk. Because every recommendation is grounded in upstream source truth and every change is preverified where possible, the number of failed rollouts and last-minute fire drills drops significantly. Dependency analysis ensures that ripple effects are considered before changes are made, and post-flight checks confirm that upgrades don’t just apply, but stick.
There’s also a direct cost impact. Staying on current versions without stretching engineers thin reduces the need for expensive extended-support contracts and emergency fixes. Organizations avoid paying a premium simply to buy themselves time.
And perhaps most importantly, Chkk builds trust into the process. Every plan, every recommendation, and every pull request is reviewable. Each one links directly back to authoritative sources, so you can see exactly why a change is being proposed. Human approval gates remain in place, ensuring nothing is applied without oversight. Instead of upgrades feeling like a leap into the unknown, they become auditable, transparent, and predictable.
For teams that have lived through failed migrations, brittle rollouts, or sleepless nights chasing version drift, these outcomes aren’t just incremental improvements—they’re a transformation in how lifecycle management feels. Upgrades become faster, safer, cheaper, and above all, more trustworthy.
Chkk LCM Agents are available today in the Free Tier for cert-manager—one of the most widely used cloud-native projects. With a single cluster or environment, you can experience discovery, planning, and IaC PR generation in action. It’s the fastest way to understand how the agents work end to end, without any upfront commitment.
When you’re ready to expand, the paid plans unlock coverage across more than 300 cloud-native projects. That means Istio, Keycloak, Cilium, Elasticsearch, Kafka, NGINX Ingress, Prometheus, Grafana, and many others that form the backbone of modern Cloud Native environments. Each project is handled with the same structured approach—observe, ground, plan, generate, preverify, apply, and verify—tailored to your topology and deployment system.
Getting started is straightforward. Sign up, follow the quick-start guide, and connect your environment. From there, you can ask the Planning Agent to surface upgrade tasks, review the generated plans, and let the Upgrade Agent open environment-aware IaC pull requests in your repo.
