AI Code Review: How Agents Automate Pull Request Feedback
AI code review agents can scan pull requests, check coding standards, and flag issues faster than a manual process. Here's how they work and how to build one.
AI code review agents can scan pull requests, check coding standards, and flag issues faster than a manual process. Here's how they work and how to build one.
AI scheduling agents handle the back-and-forth of booking meetings, sending confirmations, and following up. Here's how they work and how to build one.
AI terminal tools have exploded in 2026. Here's what separates the different approaches and how to extend any AI terminal with external tools.
A practical comparison of Google's A2A (agent-to-agent) protocol and Anthropic's MCP (model context protocol). What each one solves, where they overlap, and how they fit together.
AI agent evals help you catch failures before production. Here's how to set up offline golden-set tests and online monitoring for agents that use external tools.
AI agent memory lets agents retain context across sessions. Here's how the four memory types work and when to use each.
A direct comparison of Google's Agent Development Kit (ADK) and Anthropic's Claude Agent SDK. Design philosophy, tool model, deployment, and where each one fits.
Use Reddit search in Claude Code to find real user discussions, track brand mentions, and do product research, all through AgentPatch.
Self-hosting MCP servers gets painful at scale. Here are the managed alternatives: AgentPatch, Toolhouse, Composio, and hosted registries.
Composio provides pre-built agent tools. Nango provides API auth infrastructure. Different layers of the stack for different needs.