SCHEMA.md

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	Ordered escalation path (e.g., agent_runtime → domain_expert → governance_board).

causal_telemetry_stream — list

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	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	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.