AI Workflows
The differentiator isn’t using AI — it’s building the systems that make it reliable. Custom agents, Model Context Protocol servers, retrieval over real project knowledge, and orchestration that turns a single prompt into a verified result.
Agents answer from a linked, versioned knowledge graph of real project context — not guesses.
Findings survive adversarial, multi-perspective verification before they're trusted.
Deterministic pipelines fan out, verify, and synthesize — work too large for one context.
Purpose-built tooling — skills, MCP servers, executors — not off-the-shelf demos.
The toolkit
Dozens of custom skills, slash-commands, subagents, and hooks that encode repeatable engineering workflows — PR review, release notes, sprint reporting, scaffolding — so the rote work happens the same way every time.
10+ Model Context Protocol servers giving agents safe, read-scoped access to source control, data platforms, document stores, and a cache layer — including one that fetches its key at runtime over TLS so nothing is ever stored.
A mission-driven executor that plans, implements, and self-verifies changes end-to-end — assigning models by role (heavier reasoning for planning, faster models for execution) and exposing the whole lifecycle over a local API.
An execution mode that binds 100% of a spec's requirements to executable tests in a tracked contract file. The run only passes when every contract is green — no “looks done,” just proven done.
Reviews fan out across correctness, security, and performance lenses — then every finding is adversarially re-verified by independent agents before it's trusted.
Retrieval over a ~700-page linked, versioned wiki — a general vault plus isolated per-client vaults — so agents answer from grounded, source-cited context instead of guessing.
A learnings loop that persists corrections and failures to a local store, then injects the right reminders on the next prompt and auto-detects command failures — working across both Claude Code and Codex.
Scripted multi-agent pipelines — fan-out, pipeline, verify, synthesize — for migrations, audits, and sweeps too large for a single context to hold.
See how this shows up in delivered work.
View the work