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A gallery of agent examples producing distinct artifacts such as audits, patches, dashboards, and incident timelines.
These examples show the intended feel of hosted agents: a user describes an outcome, the agent gathers context, uses the right tools, and turns the result into a reviewable next step. Some examples combine API resources that are available now with dashboard experiences that are still being productized.

Deploy a repository

1

You describe the outcome

Deploy github.com/acme/helpdesk to staging. It needs Postgres and should be reachable at helpdesk-staging.example.com.
2

The agent inspects the repository

It reads the project files, detects the runtime, finds required environment variables, and identifies the database dependency.
3

The agent asks for missing inputs

It renders a short form for the domain, environment variables, and deployment target instead of asking you to write a manifest.
4

The agent creates a reviewable change

It prepares the package, deployment configuration, and install changes, then asks for approval before applying anything sensitive.
Conversation

Pick cost-efficient compute

Agents can use external data tools when the decision depends on live market data. Akua’s Hetzner Value Auctions tool indexes dedicated server auctions, CPU benchmarks, memory, disks, locations, and price so an agent can recommend hardware before a user buys it.
Conversation
The user gets a recommendation in the same deployment flow. The agent can explain the tradeoff in plain language, or show CPU models, disk layout, datacenter, hourly cost, and benchmark scores for advanced users.

Investigate a failing install

Conversation
This is why agents need read access to existing repository change requests. They can avoid duplicate work, continue a previous attempt, or explain why a change request should be replaced.

Watch production signals

Ambient agents are useful when the right time to ask for help is before a human opens the chat.
Ambient event
The agent is always available, but it does not need to consume expensive runtime resources while waiting. It starts work only when configured signals match policy.

Communication style

See how the same agent adapts for beginners, advanced operators, and experts.

Platform MCP

Connect local AI tools to Akua through Code Mode.

Configure an agent

Define skills, policies, grants, and runtime behavior.

Agent limits

Understand sessions, retained runtimes, model budgets, and trigger limits.