An agent is a model in a loop. Building AI Agents in Go has you build the loop: 17 chapters and 776 pages, from the first request to a crash-triage agent for a real Go web service, using only Go’s standard library. Every example runs offline with no API key.
Rigorous benchmark of AI agent code retrieval: 107 tasks, 5 repos, 5 languages, 4 competitors. grep precision: 2%. GitNexus: 7.6%. knowing: 23% (11.5x better, p<0.0001). Plus: 193x faster indexing, 28x less RAM, 48x more token-efficient than Repomix. The first statistically validated comparison of code intelligence tools for AI agents.
We built a reproducible experiment measuring how many tokens AI coding agents consume when navigating code with grep vs LSP. On HashiCorp Consul (319K lines), LSP uses 34x fewer tokens. On a TypeScript rename across 24 files: 1,441x fewer bytes. The experiment covers 4 codebases, 3 languages, 13 tasks covering 7 agent workflows.
MCP servers are the tools AI agents rely on. We tested 55 of them with mcp-assert, found 20 bugs across 9 servers, and submitted fix PRs. Grafana and Ant Group merged ours. Three days after launch, Ant Group’s visualization team asked us to integrate mcp-assert into their CI. The most common failure: servers throw unhandled exceptions instead of returning isError, leaving agents unable to recover.
I needed AI agents to reliably rename symbols, find references, and check diagnostics without silent failures. The existing MCP-LSP tools were stateless, feature-poor, and untested. So I built agent-lsp: a persistent runtime with 50 tools, 20 provider-agnostic skills, speculative execution, and an audit trail for every AI-driven edit.