Agent Runtime Infrastructure
AI Agent Profile: Make Settings Deliverable
An AI agent profile describes the model, tools, files, and policies a run should receive. Delivery contracts make unsupported settings fail before a worker starts.
AI Agent Runtime Architecture: Removing a Second Execution Path
AI agent runtime architecture gets safer when one shared path can deliver the promised settings, progress, errors, and records. We removed a second coding-agent path only after checking those boundaries.
AI Agent Observability: Make the Work Visible
AI agent observability should expose live progress and requested tool calls while leaving completion and duration unknown when the remote service never sends them.
AI Dev Container For Production Agents
An AI dev container needs isolation, command execution, durable sessions, trace export, and explicit failure handling before an agent touches a real repository.
LLM Sandbox Environment For Agent Runs
An LLM sandbox environment isolates tools, records side effects, survives reconnects, and gives reviewers enough evidence to approve or reject an agent run.