AI Engineering Leader · Agentic Systems · AI Developer Platforms · Autonomous Software Delivery

I build governed AI engineering platforms, intelligent agents, and autonomous software delivery systems designed for real-world production environments.

My focus is on moving AI engineering beyond copilots, one-off prompts, and isolated coding agents toward reliable systems of humans + agents + deterministic code — where humans define intent, constraints, risk, and acceptance criteria while agents execute bounded work and deterministic systems validate, verify, govern, and learn from the results.

I’m especially focused on the engineering needed to make agentic systems work repeatedly at scale: specification, context engineering, orchestration, memory, isolated execution, verification, observability, recovery, and continuous improvement.

Mission Control is my primary AI Software Factory project — a governed control plane for human-directed autonomous software delivery.

It coordinates the software development lifecycle from intent through learning:

Constitution → Mission → Specification → Plan → WorkOrder → Context → Execution → Independent Verification → PR → Human Acceptance → Factory Learning

Mission Control is built around a few core principles:

• Humans retain consequential authority while agents execute bounded engineering work.
• Agents propose and execute; deterministic systems validate and govern.
• Agent or harness completion is not the same as verified success.
• Verification is independent, attributable, and bound to exact candidates and evidence.
• Memory, observability, and learning remain advisory rather than becoming hidden authority.
• Every important transition has durable lineage, provenance, and recovery semantics.

Current capabilities include Spec-Driven Mission Intake, Quality Contracts, Factory Memory, Generic Harness execution, worker leases, isolated Remote Sandboxes, independent Verification Attempts, exact-current GitHub evidence, Observability/Evals, Progressive Factory workflows, and governed Factory Learning.

I’m currently focused on:

• Governed AI software factories and autonomous software delivery
• Agent harnesses and provider-neutral execution infrastructure
• Multi-agent orchestration and durable agent workflows
• Context engineering, RAG, memory, and knowledge systems
• Verification-first AI engineering and evidence-driven acceptance
• Agent observability, evaluations, recovery, and operational control
• Secure isolated agent execution and sandbox infrastructure
• Self-improving software factories driven by production evidence
• Spec-driven agentic development and requirements-to-verification lineage

The strongest AI systems combine three actors:

1. Humans define intent, constraints, priorities, risk, and consequential decisions.

2. Agents investigate, plan, modify software, use tools, and execute bounded work.

3. Deterministic Code enforces contracts, scope, identity, tests, verification, evidence, security boundaries, currentness, and acceptance gates.

The goal isn't simply to run more agents. The goal is to build systems that can execute reliably across the 100th or 1,000th run, not just produce an impressive first demo.

Languages: Python · TypeScript · JavaScript · C#

AI & Agent Systems: OpenAI · Claude · Codex · Agent Harnesses · Multi-Agent Systems · Agent SDKs · LangGraph · RAG · Context Engineering · Durable Memory

AI Software Factory: Governed Missions · Spec-Driven Development · Quality Contracts · WorkOrders · Verification-First Delivery · Independent Verification · Evidence Lineage · Factory Learning · Human-in-the-Loop Control

Execution Infrastructure: Generic Harness Contracts · Worker Runtimes · Capability Admission · Leases · Agent Sandboxes · Process Isolation · Git Worktrees · Recovery · Model Routing

AI Operations: Evals · Observability · Tracing · Provenance · Currentness · Deterministic Gates · Failure Recovery · Continuous Improvement

Backend & Platform: FastAPI · Node.js · Convex · REST APIs · Docker · Git · GitHub Apps · CI/CD

Engineering Tooling: Cursor · VS Code · Codex · Claude Code · Postman

Governed AI Software Factory: A human-directed autonomous software delivery platform spanning specification, planning, context, execution, verification, evidence, acceptance, observability, and continuous learning.

Software Factory architecture and agentic engineering patterns: Research, architecture, patterns, and implementation concepts for moving from coding agents toward reusable AI developer workflows and autonomous software factories.

Forward-Deployed AI Engineering: AI-driven workflows for discovering engineering friction, identifying automation opportunities, validating outcomes, and continuously improving developer systems.

Agent harness engineering: Experiments in model/tool execution, workflow composition, agent capabilities, controllability, and reusable harness infrastructure.

Operational visibility for agent systems: Tracing, runtime visibility, evaluation, and operational controls for understanding and improving multi-agent execution.

Persistent knowledge and context infrastructure: Knowledge, retrieval, memory, provenance, and context systems designed to provide agents with durable and attributable information.

I enjoy collaborating with people working on AI engineering platforms, agentic systems, developer infrastructure, autonomous software delivery, and practical production AI.

GitHub: @jaydubya818
LinkedIn: Jarrett West