System Reliability Map
Task to reviewable output
// intake → plan → tools → build → verify → receipt → reviewable output
Senior software engineer focused on the substrate around coding agents — plans, scoped tools, verification gates, and sanitized receipts. Three case studies follow.
Coding agents are fast at producing output and slow at producing reviewable output. The difference is not the model; it is the engineering system around it — the plans, scoped tools, verification gates, and receipts that turn agent activity into something a senior engineer can defend in review.
Plans are written before execution. Scope, non-goals, and confidence are explicit.
Agents act through scoped tools whose calls and outputs an engineer can audit.
Repeatable work runs hands-off through CLI recipes and gates, so automation reaches exactly as far as the audit trail does.
Scoped, economical context keeps agents fast and reviewable — token economy is a reliability property, not a cost trick.
Work is only complete after verification has run and its output is recorded.
Three sanitized systems, shown as proof cards first and deep technical narratives second.
Instead of loading every tool and rule into one agent, Pi routes repeatable work through CLI recipes, gives focused agents only the context they need, and requires isolated validation before QA handoff.
AGENT-TOOLS keeps skills, commands, sub-agents, and host policy in one git-backed control plane, so Codex, Claude Code, and OpenCode can share the same operating model across machines.
Public index · agent-tools-indexOne shared local MCP router instance for multiple coding-agent hosts, with cached capability lookup, isolated plugin runtimes, and measurable routed-vs-direct overhead.
AI, backend, frontend, infrastructure, and security are not separate pillars — each is evidence the same thesis holds under production constraints.
Start with the case studies for the long read, or book an open conversation for a 30-minute technical chat.