AI Engineering Leader · Agentic Systems · AI Developer Platforms · Autonomous Software Delivery
I build governed AI engineering platforms, intelligent agents, and autonomous software delivery systems for real-world production environments.
My work focuses on moving AI engineering beyond copilots, one-off prompts, and isolated coding agents toward reliable systems of humans + agents + deterministic software:
- Humans define intent, constraints, priorities, risk, and consequential decisions.
- Agents investigate, plan, use tools, modify software, and execute bounded work.
- Deterministic systems enforce contracts, validate outcomes, verify evidence, govern transitions, and provide recovery.
I’m especially focused on the engineering required to make agentic systems work reliably across the 100th or 1,000th execution—not just an impressive first demo: specification, context engineering, orchestration, memory, isolated execution, verification, observability, recovery, and governed continuous improvement.
Research, architecture, patterns, and implementation guidance for designing governed AI Software Factories that move beyond individual coding agents into reusable agent capabilities, harnesses, orchestration, independent verification, delivery, observability, and continuous learning.
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
- Governed continuous improvement driven by production evidence
- Spec-driven agentic development and requirements-to-verification lineage
The strongest AI systems combine three actors:
- Humans define intent, constraints, priorities, risk, and consequential decisions.
- Agents investigate, plan, modify software, use tools, and execute bounded work.
- 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 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.
Live site: ai-software-factory-mastery.vercel.app
AI Software Factory architecture and agentic engineering: A technical curriculum and architecture reference for designing governed autonomous software delivery systems spanning intent, agents, harnesses, orchestration, verification, delivery, observability, and continuous learning.
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.
LinkedIn: Jarrett West