Three ways to work together. Each engagement maps to one phase of the flywheel — start where you need the most leverage, then compound from there.
Start with what exists. One system, one architecture review, one clear diagnosis — before anyone talks about strategy or roadmap.
Evidence-based direction that actually lands. Architecture reviews, infrastructure decisions, and whatever's most useful that week — not a scheduled status update.
Working system, proven approach — now operationalize. Design and ship a multi-agent system for whatever problem is costing you the most right now.
Each phase builds on the last. Strategy follows proof. Systems scale from the ground up, not from theory. The flywheel keeps turning.
Problem: Agent workflows fail the same way every time. Sessions disconnect. Browsers block. Setup takes hours. Knowledge vanishes with the tab. These are patterns, not edge cases.
Built: A provider-agnostic toolkit that catches ambiguity before code ships. SDLC pipeline skills gate each phase. Browser automation tested daily against real WAFs. CLI tools with zero dependencies that work wherever they land. Knowledge ingestion that turns a saved link into team-wide signal.
Outcome: Running daily across a six-agent squad with 20+ skills. Sessions survive disconnects. Browsers reach pages that block standard headless Chrome. Zero-setup tools on any machine.
Problem: Your knowledge is scattered across notes, voice memos, bookmarks, and chat messages with no AI layer tying them together. Sync is manual. Context evaporates. The brain dump hits a black hole.
Built: A React Native app with Firebase Cloud Functions, Firestore sync, and an OpenRouter AI proxy that categorises and connects thoughts in real time. Capture by text, voice, or link. The AI surfaces related ideas automatically. Subscriptions through StoreKit.
Outcome: In private beta with real user feedback driving development. StoreKit integration validates the subscription model. The capture-to-organization loop is replacing the fragmented workflow it was built to fix.
Problem: Plant owners juggle species IDs, care schedules, and health tracking across mental notes and forgotten apps. There is no central directory that knows your plants and tells you what they need.
Built: A React Native app with AI-powered plant identification from photos. Snap a leaf, get the species. Personalized care tips based on plant type and your environment. Location tagging for multi-plant homes. A searchable database that grows with every identification.
Outcome: Live at gardendex.app. Recognition accuracy improves with every submission. Personalized reminders keep plants alive longer. The directory that knows your collection.
Problem: Six specialized agents working independently creates chaos without structure. Duplicated work, missed handoffs, priority drift. Running a multi-agent team without orchestration means constantly cleaning up.
Built: Seven specialized agent profiles routed through kanban. PM, architect, QA, infrastructure, content, comms, and chief of staff each own their domain. Tasks dispatch automatically by type and workload. Long-running jobs send heartbeats. If one drops, it retries or escalates. Structured handoffs prevent context loss.
Outcome: Active pipeline delivering code changes, research, documentation, and operational maintenance on a daily cadence. Agents hand off work autonomously. The system recovers from failures without human intervention. What required constant coordination now runs itself.
The story of how a session failure, a blocked browser, and one too many setup sessions turned into a reusable toolkit.
One agent trying to be everything hits a ceiling that looks exactly like burnout. The 7-week journey from generalist amnesia to a crew of 7 that argues in ticket comments — and why the arguing is the point.
Headline API rates aren't the real cost. Cache hit rate, context window, and architecture implications matter more. Here's what to actually look at when evaluating inference at scale.
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