Senior Technical Leader focused on Kubernetes-based distributed systems, workload isolation patterns, and AI-operationalized infrastructure in regulated enterprise environments.

I design systems that are:

• Scalable under burst load
• Governed under compliance constraints
• Deterministic under failure
• AI-augmented without sacrificing control
• Observable, diagnosable, and self-healing

• Kubernetes workload isolation & intelligent routing (light vs heavy patterns)
• Autoscaling strategy (HPA + resource governance)
• Kafka-based decoupled architectures
• Distributed system resilience & failure isolation
• Observability-driven scaling decisions
• Production-grade AI orchestration patterns (governed agent workflows)
• Enterprise-safe AI operationalization (approval gates, policy guardrails, audit trails)
• Multi-agent reasoning systems with deterministic aggregation logic
• Provider-resilient data ingestion architectures
• Conviction × Risk Gate decision governance frameworks

Advanced multi-agent AI system that formalizes high-conviction, high-beta investing into a structured, deterministic governance framework.

Stock-Agent is not a trading bot.

It is an accountability engine that separates:

What we want to do (Conviction)
from
What we are allowed to do (Risk Gate)

Each position is evaluated by specialized agents:

• Thesis Integrity Agent
• Risk Exposure Agent
• Sentiment Agent
• Skeptic (Adversarial) Agent
• Bias Detection Agent
• Consistency Agent
• Judge Aggregation Agent

Agent outputs are aggregated deterministically to ensure repeatable, explainable decisions.

Produces:

• Conviction stance (ADD / HOLD / TRIM / SELL REVIEW)
• Conviction strength (0–100)

Conviction score incorporates:

• Thesis integrity
• Catalyst momentum
• Sentiment alignment
• Predictive support
• Risk exposure penalty
• Skeptic pressure penalty
• Disagreement index
• Instability / jitter penalty

Conviction reflects directional belief strength only.

Applies deterministic portfolio governance:

• Allocation vs target max
• SellRisk overrides
• Regime tightening (risk_on / risk_off)
• Flow headwinds (momentum + volume)
• Predictive headwinds
• Portfolio concentration constraints

Default gating thresholds:

• ADD ≥ 85
• ADD SMALL ≥ 70
• HOLD ≥ 55
• TRIM ≥ 40

In risk_off:

• ADD is capped to ADD SMALL

Over target max:

• ADD is blocked

SellRisk ≥ 70:

• Forced SELL REVIEW

Quant is used for structural awareness — not blind prediction.

Includes:

• Beta vs SPY
• Annualized volatility
• 12–1 momentum
• Correlation clustering
• HHI concentration
• Historical VaR & Expected Shortfall
• Monte Carlo drawdown simulation
• Liquidity classification
• Volume regime detection

Short-horizon positioning state derived from:

• 5-day momentum
• 20-day momentum
• 20-day annualized volatility
• Volume ratio

Flow can block or cap adds.

Short-horizon ridge regression model using:

• Multi-horizon momentum
• Volatility features
• Volume ratio

Outputs:

• Direction (BULLISH / NEUTRAL / BEARISH)
• Expected return
• Probability positive
• Confidence score

Predictive signals influence Conviction and Risk Gate penalties but never override governance alone.

Each run generates:

• Conv column (stance + strength)
• Gated Effective Score
• Explicit Gate Blocks (e.g., OVER_TARGET, SELLRISK_ELEVATED)
• Short-horizon predictive metrics
• Data coverage indicators

The report includes an updated Efficacy Over Time chart tracking:

• Hit rate
• Average forward return
• Action-conditioned returns
• Cumulative performance

This makes the system measurable, auditable, and continuously evaluated.

Primary: Yahoo Finance (yfinance)
Fallback: Stooq daily CSV endpoint

Runtime behavior:

1. Check cache
2. Attempt Yahoo fetch
3. If empty/blocked → fallback to Stooq
4. If both fail → mark data unavailable explicitly

Each ticker includes a Data Coverage Indicator to prevent silent gaps.

Repo: https://github.com/Bellbotics/stock-agent

Enterprise-grade AI orchestration framework demonstrating safe operationalization of LLM-driven workflows in regulated environments.

Capabilities:

• Stateful graph-based orchestration
• Deterministic node transitions
• Human-in-the-loop approval gates
• Policy-as-code enforcement
• Artifact-driven execution
• Persistent audit trails
• Resumable workflow execution

Repo: https://github.com/Bellbotics/enterprise-langgraph-ops-demo

Reference architecture demonstrating:

• Kafka-based workload decoupling
• Intelligent routing (light vs heavy classification)
• Independent worker pools with separate HPAs
• CPU-driven scaling under burst conditions
• Isolation preventing cross-workload interference

Repo: https://github.com/Bellbotics/k8s-intelligent-workload-orchestrator

Kubernetes-deployed ML sidecar forecasting memory usage for compute-intensive document workloads.

Prevents OOMKills and improves routing efficiency under burst conditions.

Repo: https://github.com/Bellbotics/memory-spike-predictor

• Operate Kubernetes ecosystems supporting millions of API calls per day
• Process ~18K+ daily compute-intensive document transactions
• Tune pod CPU/memory constraints under production load
• Implement service mesh resilience policies (Istio)
• Instrument distributed systems with Datadog & OpenTelemetry
• Modernize legacy integrations to event-driven architectures
• Build governance-first AI orchestration systems
• Design provider-resilient ingestion layers
• Engineer deterministic aggregation across multi-agent systems

• Lead architecture sessions with Deloitte GPS CTO leadership
• Translate compliance requirements into scalable platform designs
• Bridge executive strategy and distributed system implementation
• Mentor engineers in reliability, scaling, AI safety, and cloud-native best practices
• Advocate deterministic AI orchestration over black-box automation

Resume: resume/greg-bell-resume.md
LinkedIn: https://www.linkedin.com/in/gregwbell/

All projects listed are architectural demonstrations.

They are not financial advice or endorsements of any specific technology.