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Documentation Index

Fetch the complete documentation index at: https://docs.arc.computer/llms.txt

Use this file to discover all available pages before exploring further.

The Atlas SDK orchestrator starts every run with triage, gathers adaptive signals, and then chooses how tightly to supervise the Student. Understanding that flow makes it easier to tune configs and interpret telemetry.

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Adaptive Flow Overview

  1. Triage dossier – Every run invokes a triage adapter (default: atlas.utils.triage.default_build_dossier) that normalises session metadata into risks, signals, and persona hints.
  2. Capability probe – The probe LLM inspects the dossier plus recent history, returning {mode, confidence, evidence}. If certify_first_run is enabled and the fingerprint is unseen, the runtime forces a one-time certification (paired) before probing.
  3. Adaptive mode – The orchestrator records the decision, stores probe evidence, and chooses between three lanes:
    • auto – single-shot execution without validation to keep latency low.
    • paired – single-shot execution with a single validation pass (ideal for certifications).
    • coach – converts the reviewed plan into a single step but always validates and allows a retry.
  4. Execution loop – Depending on the lane, the Student either executes a single combined step or walks through the reviewed plan. Teacher interventions (validation, guidance, retries) are lane-aware.
  5. Reward & learning – The Reward System aggregates judges, emits session_reward, and captures learning notes. Certification verdicts are reused as the reward signal when possible.
  6. Memory & telemetry – Persona memories are refreshed, adaptive summaries are stored, and exporters/streamers consume the structured metadata.

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Lane Cheatsheet

LaneWhen it triggersSupervision profilePersistence highlights
autoHigh confidence historyStudent executes once, no validationTelemetry records lane + confidence, reward may be skipped
pairedCertification required or medium confidenceStudent executes once, Teacher validates final answerCertification flag stored, reward reused from validation
coachLow confidence or probe confidence below paired thresholdPlan collapses to single step with validation + optional retryGuidance compact, adaptive summary logs probe evidence
Tune thresholds and fallback behaviour under adaptive_teaching.probe in your config (see SDK Configuration).

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Retries, Guidance, and Certification

  • Step retries are only attempted in the coach lane and are capped by orchestration.max_retries.
  • The Teacher’s guidance is appended to the execution context and streamed to the console so you can see why a retry happened.
  • Certification runs (paired on a new fingerprint) mark the session as certification_run inside adaptive_summary and store the verdict for future routing.

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Event Stream & Telemetry

Every significant action is published to the ExecutionContext event stream. Subscribe to it to power CLI streams, dashboards, or custom logging: 
from atlas.runtime.orchestration.execution_context import ExecutionContext

context = ExecutionContext.get()
subscription = context.event_stream.subscribe(print)
Key metadata to expect:
  • adaptive_summary – active mode, probe payload, confidence, and recent history (surfaces in the console streamer and JSONL exports).
  • steps – per-step attempts, timings, retry status, and guidance messages.
  • session_reward / reward_summary – aggregated reward score plus individual judge breakdowns.
  • triage_dossier, personas_used, persona_updates – the context and outcomes that feed persona learning.
Attach a custom TelemetryPublisher if you need to forward events elsewhere; otherwise the default console streamer handles everything automatically.

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Anatomy of atlas.core.run

At a high level the public API (atlas.core.run / atlas.core.arun) performs the following:
  1. Load and validate your config (atlas/config/loader.py) and reset the ExecutionContext.
  2. Build the agent adapter (create_from_atlas_config) plus Student and Teacher prompts (atlas.prompts.build_student_prompts / build_teacher_prompts).
  3. Instantiate Student, Teacher, and the session-level Reward Evaluator.
  4. Load the triage adapter and capability probe client defined in adaptive_teaching, collecting fingerprint hints for persona memory.
  5. Connect to optional storage (Postgres) and set up telemetry publishers or console streaming.
  6. Run the Orchestrator, which performs triage, probes for a lane, executes the plan (single-shot or stepwise), and records results.
  7. Persist session metadata—including adaptive_summary, reward payloads, and persona updates—before returning an atlas.types.Result.

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When to Customize

GoalConsider tweaking
Force a specific laneadaptive_teaching.mode_override
Bias routing thresholdsadaptive_teaching.probe.thresholds and fallback_mode
Tighten or relax retriesorchestration.max_retries and Teacher guidance prompts
Adjust reward escalationrim.variance_threshold, rim.uncertainty_threshold, or custom reward objectives
Stream custom telemetryAttach a TelemetryPublisher or subscribe directly to the event stream

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Next Steps