AI Architecture / Workflow Automation / Autonomous Intelligence layer over daily life
Ignite Intelligence v15
A multi-agent autonomous operations platform — architected end-to-end by a Business Analyst and Product Owner. A team of specialist agents. Real-time telemetry. An Olympus briefing every morning.
Problem
There is a moment every serious practitioner reaches where the tools available do not match the demands of the work. For me, that moment became a build.
I needed a command centre that matched how I think — structured, contextual, always one step ahead of the decision that matters next. The market did not offer it.
My Role
I architected Ignite Intelligence end-to-end: agent registry, autonomy tiers, model selection, telemetry, briefing pipeline, quality gates. AI carried the typing; the architecture is mine.
My background is business analysis and product ownership, not engineering. The same discipline I use writing Business Requirements Documents and defining acceptance criteria designed every layer of this platform.
Decisions
Every agent runs a defined model with calibrated temperature and bounded scope. The architecture is model-independent — agents can swap to whichever open-weight model best fits the job as the field evolves. Each choice is intentional.
Three-tier autonomy: L0 Advisory Only, L1 Approval Required, L2 Auto-Execute. This is not about trust — it is about precision. High-stakes decisions require review. Routine intelligence can run unattended.
Local-first stack: Ryzen 7 server, FastAPI backend, MongoDB for operational context, Qdrant for vector memory, WebSocket for realtime. Operator data never leaves the device.
Before And After
Before: tasks lived in inboxes, context lived in memory, opportunities went unsurfaced, and every morning started by trying to remember what mattered yesterday.
After: tasks became missions. Inboxes became briefings. Manual execution became reviewed agent actions. The platform handles signal processing; I handle the judgment calls.
Outcome
Ignite Intelligence v15 is in production daily use. Multi specialist agents standing by. Chairman briefing every morning. Real-time telemetry on infrastructure, services, and agent health.
The platform is not smart because the models are capable — it is smart because the architecture is intentional. That is my contribution: turning ambiguous need into structured, releasable specification precise enough for AI to execute against. Ignite is the nickname I gave the AI I built — after me. After Ignatious.
Recreated Artifacts
Multi specialist agents — each with a role, model, temperature, autonomy tier, and bounded scope.
L0 Advisory · L1 Approval Required · L2 Auto-Execute. Encoded into the platform, not into trust.
Structured morning briefing surfaces runway, services, queued missions, recommended action.
Ryzen 7 server, FastAPI, MongoDB, Qdrant vector memory, WebSocket realtime channel.