Your infrastructure
never sleeps.
Neither does
your AI team.

Three things that used to require a human expert. Now automated, audited, and human-approved.

Every agent is tied to a platform, a schedule, a tool set, and a system prompt. Admins approve agents before they go live.

The AI investigates. Humans decide. Nothing executes without approval.

Every write operation in KaizenCore — restarting a service, running a kubectl command, executing a SQL query, pushing a commit — creates a Proposal. The proposal shows the reviewer:

The reviewer clicks Approve — the command runs, the result is captured. The reviewer clicks Reject — the command never runs, the agent adapts. No commands execute in the background. No surprises.

If optional TOTP 2FA is enabled, high-risk approvals require a 6-digit authenticator code before execution. Multi-step proposals let the agent propose a complete remediation plan — restart service, verify health, roll back if needed — as a sequential series of steps reviewers see in full before approving step one.

A multi-step proposal: the AI describes its full remediation plan. Step-by-step execution only begins after the reviewer approves.

Six platforms. One interface. One AI team.

KaizenCore connects to the platforms you already run. Add credentials once; every agent and investigation you create automatically has access to the tools for that platform.

Connect Kubernetes, Linux, Windows, PostgreSQL, AWS, and GitHub — all from the same UI.

Describe the problem. Watch the AI work. Get an answer.

You type a problem description — "nginx is returning 502 errors" or "the nightly ETL job failed" — and select one or more platforms. KaizenCore creates a run, builds a context-aware agent, and starts executing.

You watch it happen in real time. Every tool call streams to the timeline as it happens — the exact input, the exact output, no black box. You see the AI read your logs, query your database, check your pods, search your code.

The tool permission system extends investigations into territory the AI can't read by default. If the agent needs to run netstat -tlnp or kubectl exec, it pauses, tells you exactly what it wants to run and why, and waits. You approve or deny. Follow-up chat keeps investigations alive after the initial run — type a follow-up and a new run picks up with full context of the previous investigation.

An active investigation: tool calls stream in real time. Each call shows its exact input and output. A permission request banner appears when the AI needs elevated access.

Alert fires. Investigation starts. Results waiting before you finish reading the alert.

KaizenCore exposes an inbound webhook endpoint. Point your alerting system at it and connect an investigation to fire automatically every time an alert arrives. Supported sources: Prometheus AlertManager, Grafana, PagerDuty, and any generic JSON webhook.

By the time your on-call engineer opens the Slack notification, KaizenCore has already spent 3–5 minutes reading logs, checking services, and investigating the blast radius.

Webhook source configuration: map alert labels to platforms, choose your AI model, and write a description template using alert data.

The AI knows your infrastructure's custom commands too.

Every environment has tools that aren't in any default toolset. A custom kubectl plugin. An internal health check script. A deployment trigger API. KaizenCore's custom tools let you teach the AI about these one time, and it uses them naturally alongside built-in tools.

Custom tools extend the AI's built-in capabilities. Command tools run shell or kubectl commands; webhook tools POST to your endpoints.

Bring your own AI. Switch models without reconfiguring anything.

KaizenCore is provider-agnostic. Configure named provider profiles once; use them everywhere. Different agents can use different models — routine health checks on a cost-efficient model, critical incident investigations on the most powerful one available.

Create a named profile (e.g., "Production — Claude Sonnet" or "Cost-Optimized — GPT-3.5"). Assign it to agents and investigations. When you want to switch the model, update the profile — every agent using it updates automatically.

Named AI provider profiles: configure once, assign to any number of agents and investigations. Switch models in one place.

Enterprise-grade security. Nothing trusts nothing.

What this looks like when your team actually uses it.

AI that augments engineers. Not AI that replaces them.

"The goal isn't to remove the engineer. It's to remove the 45-minute investigation phase so that by the time the engineer looks at the incident, all the context they need to make a decision is already assembled."

KaizenCore is built around one conviction: the human must always be in the loop for writes.

Investigations are fully autonomous — the AI reads everything, diagnoses everything, explains everything. That's safe. Reading production logs and querying databases doesn't break anything.

But when it comes to changing something — restarting a service, running a command, pushing code, executing SQL — a human sees exactly what will run, understands why the AI thinks it should run, and makes the call. This isn't a limitation. It's the feature.

The AI does the work. The engineer does the thinking. Everyone goes home on time.

Running in under 10 minutes.

Complete capability overview.