Meet the Workflows: Tool & Infrastructure

Jan 13, 2026

Delighted to have you back on our journey through Peli’s Agent Factory! Now, prepare yourself for something quite peculiar - the room where we watch the watchers!

In our previous post, we explored testing and validation workflows that continuously verify our systems function correctly - running smoke tests, checking documentation across devices, and catching regressions before users notice them. We learned that trust must be verified.

But here’s a question that kept us up at night: what if the infrastructure itself fails? What if MCP servers are misconfigured, tools become unavailable, or agents can’t access the capabilities they need? Testing the application is one thing; monitoring the platform that runs AI agents is another beast entirely. Tool and infrastructure workflows provide meta-monitoring - they watch the watchers, validate configurations, and ensure the invisible plumbing stays functional. Welcome to the layer where we monitor agents monitoring agents monitoring code. Yes, it gets very meta.

Tool & Infrastructure Workflows

These agents monitor and analyze the agentic infrastructure itself:

MCP Inspector - Validates Model Context Protocol configurations - ensures agents can access tools
GitHub MCP Tools Report - Analyzes available MCP tools - 5 merged PRs out of 6 proposed (83% merge rate)
Agent Performance Analyzer - Meta-orchestrator for agent quality - 29 issues created, 14 leading to PRs (8 merged)

Infrastructure for AI agents is different from traditional infrastructure - you need to validate that tools are available, properly configured, and actually working. The MCP Inspector continuously validates Model Context Protocol server configurations because a misconfigured MCP server means an agent can’t access the tools it needs.

GitHub MCP Tools Report Generator has contributed 5 merged PRs out of 6 proposed (83% merge rate), analyzing MCP tool availability and keeping tool configurations up to date. For example, PR #13169 updates MCP server tool configurations.

Agent Performance Analyzer has created 29 issues identifying performance problems across the agent ecosystem, and 14 of those issues led to PRs (8 merged) by downstream agents - for example, it detected that draft PRs accounted for 9.6% of open PRs, created issue #12168, which led to #12174 implementing automated draft cleanup.

We learned that layered observability is crucial: you need monitoring at the infrastructure level (are servers up?), the tool level (can agents access what they need?), and the agent level (are they performing well?).

These workflows provide visibility into the invisible.

Using These Workflows

You can add these workflows to your own repository and remix them. Get going with our Quick Start, then run one of the following:

MCP Inspector:

gh aw add-wizard https://github.com/github/gh-aw/blob/v0.45.5/.github/workflows/mcp-inspector.md

GitHub MCP Tools Report:

gh aw add-wizard https://github.com/github/gh-aw/blob/v0.45.5/.github/workflows/github-mcp-tools-report.md

Agent Performance Analyzer:

gh aw add-wizard https://github.com/github/gh-aw/blob/v0.45.5/.github/workflows/agent-performance-analyzer.md

Then edit and remix the workflow specifications to meet your needs, regenerate the lock file using gh aw compile, and push to your repository. See our Quick Start for further installation and setup instructions.

You can also create your own workflows.

Learn More

GitHub Agentic Workflows - The technology behind the workflows
Quick Start - How to write and compile workflows

Next Up: Multi-Phase Improver Workflows

Most workflows we’ve seen are stateless - they run, complete, and disappear. But what if agents could maintain memory across days?

Continue reading: Multi-Phase Improver Workflows →

This is part 15 of a 19-part series exploring the workflows in Peli’s Agent Factory.