# Atlas Apex Cross-Domain Autonomous Intelligence Pack — Schema

One row = one complete autonomous decision cycle. All records share the same seven top-level fields.

Schema version: `1.0.0-atlas-apex-sample`

## Top-level fields

### `schema_version` — string
Schema identifier. Constant within a sample release.

### `event` — struct
Identifier fields and the overall strategic classification for the cycle.

| Field | Type | Notes |
|---|---|---|
| `id` | string | Stable event identifier, e.g., `ATLAS-100000`. |
| `trace_id` | string (UUID) | Cross-links telemetry within the cycle. |
| `timestamp` | string (ISO-8601) | Cycle anchor time. |
| `strategic_value` | string | `low`, `medium`, `high`, `critical`, `transformative`. |
| `outcome` | string | `objective_achieved`, `partial_success`, `rolled_back`, `escalated_to_human`, `executed_with_caveats`. |
| `confidence` | double | 0–1 engine confidence in the outcome. |

### `identity_context` — struct
Agent archetype and autonomy posture.

| Field | Type | Notes |
|---|---|---|
| `agent_type` | string | `AI_Scientist`, `Trading_Agent`, `Orchestrator`. |
| `reasoning_dna` | string | Explicit reasoning-strategy identifier (e.g., `DNA-A7F3-MCTS-EXPLORE-0.65`). Encodes a branch preference (`EXPLORE`/`EXPLOIT`/`HYBRID`/`CONSERVATIVE`/`AGGRESSIVE`) and a scalar exploration parameter. |
| `autonomy_level` | string | `L2_Assisted`, `L3_Supervised`, `L4_Conditional`, `L5_Full_Auto`. |
| `human_approval_required` | bool | `true` when autonomy is L2 or L3. |
| `escalation_chain` | list<string> | Ordered escalation path (e.g., `agent_runtime` → `domain_expert` → `governance_board`). |

### `causal_telemetry_stream` — list<struct>
Ordered cross-domain events in the cycle. One struct per step.

Step struct:

| Field | Type | Notes |
|---|---|---|
| `timestamp` | string (ISO-8601) | Step time. |
| `event_name` | string | Scenario-specific action label (e.g., `HYPOTHESIS_GENERATED`, `SATELLITE_SIGNAL_DETECTED`, `SWARM_NODE_FAILURE_DETECTED`). |
| `domain` | string | `biotech`, `legal`, `finance`, `economics`, `space`, `robotics`, `systems`, `meta`. |
| `data_source` | string | Abstract upstream source name (e.g., `literature_corpus`, `earth_observation_feed`, `task_scheduler`). |
| `value_at_risk_usd` | double | Scenario-scaled USD value at stake at the step. |
| `fidelity_score` | double | 0–1 data-fidelity score for the source. |
| `latency_ms` | int | Observed latency for the step. |

### `reasoning_trace` — struct
Agent-reasoning metadata for the cycle.

| Field | Type | Notes |
|---|---|---|
| `primary_objective` | string | Short objective label (scenario-appropriate). |
| `decision_depth` | int | Depth of the reasoning tree (MCTS-style). |
| `confidence_threshold` | double | 0–1 engine confidence gate. |
| `branches_evaluated` | int | Number of reasoning branches considered. |
| `winning_branch_reward` | double | Reward attributed to the selected branch. |
| `counterfactual_considered` | bool | Whether an alternative was explicitly scored. |

### `detection_logic` — struct
Cross-domain anomaly / conflict metadata.

| Field | Type | Notes |
|---|---|---|
| `anomaly_description` | string | Natural-language description of the cross-domain pattern observed. |
| `predictive_fidelity` | double | 0–1 predictive fidelity of the detection logic. |
| `cross_domain_signal_count` | int | Number of distinct `domain` values in the telemetry. |
| `signal_conflicts` | list<string> | Conflicts observed (e.g., `fidelity_mismatch`, `temporal_inversion`, `value_at_risk_divergence`). Often empty. |

### `simulation` — struct
Simulation engine provenance and scenario class.

| Field | Type | Notes |
|---|---|---|
| `synthetic` | bool | Always `true`. |
| `engine` | string | Simulation engine label (`atlas_apex_sim_v1`). |
| `cross_domain_sync_mechanism` | string | `event_sourced_bus`, `shared_knowledge_graph`, `temporal_lockstep`, `cross_domain_oracle`, `digital_twin_state_sync`. |
| `scenario_class` | string | `autonomous_scientific_discovery`, `ai_driven_economic_decisions`, `distributed_system_coordination`. |
| `intended_use` | list<string> | ML use-case tags. |

## Distribution of this sample

- 10,000 cycles total.
- Scenario class: balanced 3,333 per class.
- Agent type: balanced 3,333 per archetype (one archetype per scenario).
- Strategic value: scenario-weighted (science discovery carries more `transformative` tail; system coordination skews lower value).
- Autonomy level: weighted toward L4 `Conditional` with meaningful L5 `Full_Auto` and L3 `Supervised` shares.
- Outcomes: scenario-weighted; ~45% `objective_achieved`, ~28% `partial_success`, remainder split across rolled-back, escalated-to-human, and executed-with-caveats.

## Sanitization notes

- Event IDs are synthetic (`ATLAS-*`).
- Trace IDs are random UUIDs.
- All domain content is abstract narrative templates — no real scientific results, trades, robotic telemetry, or patents are present.
- `data_source` values (e.g., `earth_observation_feed`, `legal_llm`, `lims_feed`) are generic type labels, not references to specific products or vendors.

## Relationship to the full pack

The production pack scales to 100K+ cycles with expanded domain coverage (energy, defense, biosecurity, supply chain, climate), richer agent archetypes (swarm coordinators, red-team agents, digital-twin orchestrators), multi-agent collaboration traces, longer causal chains, adversarial / cooperative variants, and gym-compatible delivery. See the pack card for commercial access.