What I Own
Product direction, system architecture, workflow design, requirements, risk boundaries, acceptance criteria, and release decisions.
Johnny Fawler
AI Product & Automation Systems Specialist
Designing Reliable AI Products and Agentic Workflows.
I'm Johnny Fawler, an AI product architect and agentic systems orchestrator. I translate product goals into system architecture, autonomous workflows, safeguards, acceptance criteria, and operational evidence. F3 AI Labs is my independent product lab and portfolio for documenting product architecture, agentic workflows, safeguards, and operational evidence across self-directed projects.
Seeking a full-time role in AI product development, automation implementation, technical product management, or cloud/AI solutions.
Product Systems Beyond the Demo
I focus on what it takes to move AI from an impressive prototype into a dependable product: system architecture, agent workflows, safeguards, acceptance criteria, observable runtime behavior, human approval boundaries, and honest readiness communication — not disconnected feature demonstrations.
Approach
How I Work
Transparent ownership from product direction through release — with AI-assisted implementation and accountable quality controls.
How Implementation Happens
AI-assisted engineering tools turn defined requirements into working software through structured implementation and revision cycles.
How Quality Is Controlled
Automated tests, independent verification, staged environments, observable runtime behavior, rollback controls, and qualified human review for high-risk releases.
Capabilities
Product and Systems Capabilities
What I own across product architecture, workflows, governance, and release — not commercial service packages.
AI Product Architecture
- Product vision and critical user journeys
- System requirements and acceptance criteria
- Product state and lifecycle design
- Data-flow and integration planning
- Human-approval boundaries
- Readiness and release standards
Agentic Workflows and Automation
- Multi-agent responsibility design
- Tool and permission boundaries
- Prompt and context architecture
- Structured outputs and validation
- Human-in-the-loop controls
- Autonomous workflow stopping conditions
Product Governance and Evaluation
- Evidence-driven release gates
- Failure-mode and recovery design
- Risk classification
- Product evaluation criteria
- Mode and environment separation
- Traceable operational decisions
Cloud and Operational Systems
- Deployment architecture
- Environment separation
- Observability requirements
- Health and degraded-state design
- Cost and resource governance
- Rollback and kill-switch planning
Product and Operator Experience
- Workflow-first information architecture
- Operator dashboards
- Explainable product states
- Responsive interfaces
- Error and degraded-state experiences
- Clear readiness communication
Technology Environment Used Across Projects
Common tools and platforms across portfolio projects — listed as environment context, not a claim of sole hands-on implementation for every layer.
Next.jsTypeScriptPythonFastAPIPostgreSQLSupabaseDockerVercelOracle CloudGitHub ActionsExternal APIs
Implementation across these projects is AI-assisted. Johnny’s primary responsibilities are product architecture, workflow orchestration, requirements, safeguards, acceptance criteria, and evidence-driven release decisions.
Product work
Selected Product Work
Case studies from independent product work — honest status labels, no fabricated metrics.
Operational Trading PlatformIN DEVELOPMENT
QuantGorilla — Agentic Market Intelligence and Trading Systems Research
Johnny's role: Product architect and agentic systems orchestrator—product requirements, trading-workflow design, risk-control architecture, agent coordination, operator experience, evidence standards, and readiness decisions.
Multi-agent trading operator for Hyperliquid with scanner pipelines, risk gates, mode separation (paper/shadow/soak/live), and an operator cockpit. Research platform — not a profit guarantee.
Key accomplishment: Paper/shadow/soak/live mode separation with operator-visible gate funnel
Python/FastAPINext.jsPostgreSQLHyperliquid API
Read case study
Consumer AI ProductACTIVE
Parlay Gorilla — Explainable Sports Analytics Application
Johnny's role: AI product architect—sports-intelligence workflows, explainability requirements, product experience, responsible-use boundaries, data requirements, and AI-assisted delivery coordination.
AI sports analytics and parlay research with transparent confidence scores, multi-selection workflows, and explainable analysis — a research application, not a sportsbook.
Key accomplishment: Explainable confidence UX across parlay builder and game analysis surfaces
Next.jsNode.jsSports data APIsTypeScript
Read case study
Wellness AI · Product IntentCOMING SOON
ZenThink — Safety-Aware Conversational Wellness UX
Johnny's role: AI experience and safety architect—product intent, conversational workflows, safety boundaries, escalation requirements, privacy expectations, and human-centered experience design.
Safety-aware conversational wellness product exploring mood and journal workflows, context-aware AI interactions, escalation design, and non-diagnostic language — not therapy or medical treatment.
Key accomplishment: Safety boundary and escalation architecture for conversational wellness UX
React Native directionNext.jsLLM integrationTypeScript
Read case studyArchitecture
Selected Architecture Patterns
Recurring patterns across my independent project work.
Full-stack product lane
Next.js frontend → FastAPI service → PostgreSQL
Typed API boundary between React surfaces and Python services, with auth, migrations, and operator dashboards on shared data models.
Agent workflow lane
Model router → agent workflow → tool layer → validation layer
Route requests through orchestrated agents with explicit tool contracts, structured output validation, and human override hooks.
Data intelligence lane
Data ingestion → normalization → intelligence pipeline → operator interface
Ingest heterogeneous sources, normalize to auditable records, run classification or scoring, and surface results in reviewable UIs.
High-stakes decision lane
Event processing → decision gate → human approval → action
Process streaming events through explicit gates with mode separation, kill-switch design, and operator-visible rejection reasons.
Reliability lane
Health monitoring → degraded mode → recovery workflow
Detect upstream failures, enter degraded operation with clear UI labels, and document recovery paths before returning to normal.
Evidence
Product and Systems Evidence
Documented patterns from real project work — not fabricated results.
Mode separation
Proves paper, shadow, soak, and live workflows are labeled and routed separately in trading products. Does not prove live profitability, production scale, or that any metric shown is from live capital.
View evidence →Evidence-driven gates
Proves promotion paths reference soak reports, health checks, and operator approval before risk increases. Does not prove every gate is enforced in production or that past soak runs guarantee future behavior.
View evidence →Explainable confidence design
Proves sports analytics surfaces expose reasoning and confidence context for decision support. Does not prove pick accuracy, win rates, or betting outcomes.
View evidence →Safety boundaries
Proves wellness, trading, and tax products carry non-diagnostic, non-advice, and mode-label disclaimers at boundaries. Does not prove clinical safety certification or regulatory approval.
View evidence →Testing of critical paths
Proves Vitest coverage on content models, SEO schema, contact routing, and component contracts in marketing repos. Does not prove full end-to-end production soak or security audit completion.
Product status truth
Proves every portfolio project displays current readiness without fabricated traction. Does not prove roadmap delivery dates or investor-grade maturity.
View evidence →Operator-visible state
Proves cockpits and dashboards surface gate results, mode labels, and health signals for human review. Does not prove unattended autonomy or hands-off live operation.
View evidence →Rollback and human-approval design
Proves workflows include kill-switch patterns, override paths, and degraded-mode behavior in product design. Does not prove every override has been exercised under live market stress.
View evidence →Interested in working together?
I'm open to AI product, automation implementation, technical product, and cloud/AI solutions roles — and focused conversations about agentic workflows and evidence-driven release practices.