Onboarding complete. Atlas signs you off as a systems engineer.

You started without knowing how to talk to an LLM. You closed the onboarding by designing an end-to-end multi-step system with evals.

What you carry aboard after Track 4:

• Recognize when one prompt isn't enough and you need to split into a chain, and when NOT to split.
• Design declarative chains: focal steps, explicit dependencies, unique output keys, parallel when possible.
• Manage state between steps: what to pass, what to drop, what to cache, what never to cache.
• Recognize the five patterns of broken chains before they reach production.
• Build routers with a safe default, choosing between rules, LLM, or cascade depending on context.
• Design model cascades (small filters, big responds) to scale while lowering cost.
• Implement agent loops with real stop conditions: max_steps, loop_detection, errors that go back to the model.
• Communicate budgets (steps, tokens, money) to the agent so it warns before crashing.
• Define retry and backoff policies per error type, with actionable dead-letter.
• Design graceful degradation: the system stays useful when an internal piece fails.
• Design honest mini-RAG that acknowledges its gaps instead of hallucinating.
• Instrument traces and spans with cost, status, and redaction, to debug without re-running.
• Write intentional eval sets: control + edge + adversarial, all with justification.
• Write debuggable LLM-judges with anchors and rationale.
• Compare versions of your chain with real A/B testing, not contaminated "before and after".

What you walked through in the full onboarding:

• Track 1. The anatomy of a prompt. Vega and Echo.
• Track 2. Conversations that get somewhere. Vega, Forge, Echo.
• Track 3. Tools the agent actually uses. Forge.
• Track 4. Multi-step systems with evals. Orbit, , .