AI Agent Examples Overview
This section provides example AI Agent configurations that showcase different ways to leverage AI-powered automation in your testing pipelines. Each example includes a ready-to-use prompt, the recommended MCP tools, and usage guidance.
The examples are grouped by primary audience, though many agents are valuable to both QA engineers and DevOps/platform engineers.
For QA & Test Engineers
These agents focus on test quality, failure analysis, and test authoring — helping QA teams understand why tests fail, improve test coverage, and maintain a healthy test suite.
| Agent | Description | Trigger Style |
|---|---|---|
| Remediation | Analyzes failed tests, inspects recent code changes, and opens a pull request with a suggested fix | Task (on failure) |
| Flakiness Analysis | Detects flaky tests by analyzing execution patterns across multiple runs and correlating with source changes | Interactive or triggered |
| Dependency Impact | When a test fails, analyzes which other workflows might be affected by the same root cause | Task (on failure) |
For DevOps & Platform Engineers
These agents focus on infrastructure reliability, pipeline efficiency, and operational governance — helping platform teams keep test execution fast, cost-effective, and well-managed.
| Agent | Description | Trigger Style |
|---|---|---|
| Infrastructure Triage | Classifies failures as infrastructure issues (OOM, timeouts, network) vs actual test failures | Task (on failure) |
| Incident Correlator | Groups simultaneous failures to determine if they share a common infrastructure root cause | Task (on failure) |
| Execution Cost Analyzer | Analyzes execution patterns to identify expensive workflows and recommend optimization strategies | Interactive or scheduled |
Cross-Functional
These agents serve both audiences equally:
| Agent | Description | Trigger Style |
|---|---|---|
| Onboarding Guide | Helps new team members understand the existing test infrastructure and create their first workflows | Interactive |
| Security & Compliance | Audits workflows for security best practices, secret handling, and operational compliance | Scheduled or interactive |
Enhancing Agents with External MCP Servers
All the examples above use only the built-in Testkube MCP Server. By connecting external MCP Servers, you can give agents access to additional context that dramatically improves their analysis and enables new automation capabilities.
| MCP Server | What it adds | Agent enhancements |
|---|---|---|
| GitHub / GitLab | Code changes, PRs, issues, file contents | Remediation can open fix PRs; Flakiness Analysis can correlate with source changes; any failure agent can auto-create issues |
| Kubernetes | Pod status, events, node conditions, resource quotas | Infrastructure Triage can check pod evictions and node pressure; Incident Correlator can query cluster-wide events |
| Grafana | Dashboard data, metrics, alerts | Infrastructure Triage can correlate test failures with application performance metrics; Performance Regression can compare against APM baselines |
| Slack / Teams | Channel messaging | Any triggered agent can post summaries to a channel; Incident Correlator can alert #incidents |
| Jira / Linear | Issue creation and tracking | Failure agents can create tickets automatically; Security & Compliance can file violation issues |
| Datadog / Prometheus | APM metrics, traces, error rates | Infrastructure Triage can check if the service under test had errors; Performance Regression can compare against application-level SLIs |
| PagerDuty / OpsGenie | Active incidents and alerts | Incident Correlator can check if there's an active infrastructure incident explaining test failures |
You can browse available MCP Servers in the MCP Server Registry directly from the Dashboard. For more external-MCP-powered agent ideas, see More AI Agent Ideas.
More Ideas
Looking for more inspiration? See More AI Agent Ideas for additional agent concepts covering performance monitoring, resource optimization, capacity planning, and more.
Getting Started
All examples use the built-in Testkube MCP Server. To create any of these agents:
- Follow the Creating an AI Agent guide
- Copy the prompt from the example page
- Enable the listed MCP tools
- Optionally set up an AI Agent Trigger for automated execution
These examples are starting points — customize the prompts, tools, and trigger conditions to match your team's specific needs. You can also combine multiple agents: for example, use the Infrastructure Triage agent to classify failures, then route infrastructure issues to an ops channel and test failures to QA. Connect external MCP Servers like Kubernetes, Grafana, or GitHub to give your agents deeper context and automation capabilities.