Flue brings a TypeScript harness to autonomous AI agents, letting you define models, tools, skills, and sandbox environments in code. In the example you import two MARKDOWN-based skills—triage and verify—from your project, pull in GitHub tooling, then wire everything together with defineAgent. You specify model (“anthropic/claude-sonnet-4-6”), a local sandbox, skill modules, and free-form instructions like “Triage a bug report end-to-end.” That single file turns into an HTTP-exposed agent you can run locally or deploy to any hosted runtime. Under the hood, Flue handles sessions, durable recovery, and contextual continuity. It tracks agent state across restarts so a long-running workflow survives failures. You get secure sandboxes—virtual, local or remote—where agents can modify files, call APIs or run code without risking your host. If you need multiple specialized roles, you define subagents and let the main agent delegate tasks: one subagent might triage issues, another verify fixes. The ecosystem includes @flue/cli for build and dev commands, @flue/runtime for the core harness, @flue/sdk for client code, plus adapters for Postgres persistence and OpenTelemetry tracing. Flue also plugs into the Model Context Protocol to authenticate tools. You wire in observability with Braintrust, Sentry or your own system, and you can hook agents to Slack, Teams, Discord or GitHub via event channels. All told, Flue packages the scaffolding you need to move beyond raw LLM calls toward real-world, end-to-end AI workflows.