AI Systems Lab
Terminal Byte treats AI as a systems problem: state, permissions, workflow recovery, observability, cost, reliability, and human control matter as much as model output.
Artifact
This artifact makes the AI direction more credible by documenting an implementable workflow runner architecture.
A concrete AI systems design artifact for running LLM-assisted workflows through explicit task state, approval gates, tool permissions, retries, audit events, recovery paths, and human-operable controls.
View artifactCurrent Direction
The AI direction is intentionally connected to production software concerns rather than generic prompt demos.
Workflow architecture for LLM-assisted systems where actions pass through permissions, human approval, retries, audit logs, rollback paths, and state-machine boundaries.
Developer tooling direction focused on using LLMs to support code review, documentation, task decomposition, recovery notes, and delivery workflow automation without weakening engineering ownership.
Observability model for AI systems using traces, queue depth, latency, failure modes, cost signals, prompt/version metadata, and human handoff points before automation is trusted.
A path toward intelligent scheduling and autoscaling using baseline load tests, Prometheus metrics, Grafana dashboards, service-pressure signals, and eventually learning-based policy experiments.
Architecture
These principles define what has to be true before an AI workflow is treated as production-grade software.
Research Fit
The lab connects LLM workflows to the same engineering concerns found in distributed systems: state, coordination, scheduling, observability, feedback loops, and failure handling.
Exploring how services expose useful pressure signals through metrics, logs, traces, queues, load tests, and runtime dashboards before scaling decisions are trusted.
Studying how AI-enabled workflows can support infrastructure operations while preserving approval gates, audit trails, rollback behavior, and human control.
Investigating how workload profiles, latency, saturation, queue depth, and cost signals can inform future scheduling and autoscaling policies.
Building small, inspectable systems that turn complex ideas into measurable experiments across browser execution, monitoring, automation, and distributed behavior.
Backlog
These experiments are framed as future implementation work so the portfolio stays honest about what is planned versus what is already built.
A small agentic workflow runner with approval states, retry policy, audit events, and task recovery.
A developer-productivity assistant that turns architecture notes, commit history, and issue context into review-ready implementation plans.
An observability notebook for AI workloads that tracks latency, queue depth, cost, failures, prompt versions, and human handoff points.
A scheduling experiment that compares rule-based scaling decisions against workload and service-pressure signals.
Writing
Current technical writing already explains the architecture concerns behind reliable AI-assisted systems.
Agentic AI workflow architecture with permissions, approval gates, audit trails, retries, and recovery paths
LLM-assisted developer productivity and workflow automation for engineering teams
Observability foundations for AI workloads, queues, traces, metrics, logs, cost visibility, and scaling signals
Intelligent software systems that combine backend architecture, cloud infrastructure, and AI-enabled decision support