Pillar · autopilot

Autopilot — describe the goal, we design the agents.

Mission, blueprint, hosted service. Declare the outcome; Autopilot decomposes into agents, picks tools, captures the trace. Curator harvests every successful run as JSONL — the seed corpus for next quarter's fine-tune. Goals in, agents out, training data accumulating.

from sagewai.autopilot import Mission

mission = Mission("triage and respond to support emails")
result = mission.run()  # Curator captures the trace automatically

→ Example 36 — Autopilot training loop Run the 5-minute quickstart

Other pillars

SDK — the harness that works with any LLM.
$0.004 to triage 6 support emails on Haiku 4.5
Fleet — workers, dispatch, scoped routing.
24 workers / 100 tasks / 0 cross-tenant leaks (CI-gated)
Observatory — show your CFO where the AI money goes.
Real OTel + Grafana, populated by mixed-tenant load
Training Loop — from juggernauts to your own model.
$0.35 RunPod fine-tune, deployed via Ollama