● Human-approved self-improvement only
ClearGlassInc Artemis · System 2040
Self-evolving intelligence operations at audited machine speed.
A production-grade architecture for secure, coalition-aware operations using Palantir Gotham for investigations, Foundry for ontology and data pipelines, AIP for governed agents, and Apollo for deployment control, rollback, and runtime policy.
End-to-end control plane
IngestOSINT, cyber telemetry, HUMINT reports, sensors, and operator notes.
ResolveFoundry ontology normalizes entities, confidence, lineage, caveats, and access labels.
ReasonAIP agents call policy-filtered tools and produce evidence-linked hypotheses.
ApproveHuman gates are mandatory for case escalation and operational action packages.
ImproveFeedback becomes evals, proposed patches, canaries, and reversible releases.
Safe learning loop
- Capture corrections, query logs, alert outcomes, mission results, and latency traces.
- Generate prompt, workflow, model-routing, or heuristic proposals with immutable evidence hashes.
- Run regression evals for precision, recall, policy violations, p95 latency, and operator trust.
- Require approval tokens before Apollo promotes the signed manifest beyond staging-canary.
Current blueprint metrics
0 autonomous operational actions without approval
5 layers of policy enforcement
100% proposed upgrades versioned and reversible
Representative governed proposal manifest
{
"proposal_type": "prompt_patch",
"target_component": "aip.agent.triage_copilot",
"rollout_ring": "staging-canary",
"approval_required": true,
"eval_gates": ["precision >= .92", "recall >= .86", "policy_violations == 0"],
"apollo_controls": ["signed_artifact", "canary", "health_gate", "rollback"]
}System Architecture
- Role-aware dashboards, case boards, link analysis, streaming alert rail, and approval queue.
- React/TypeScript client consumes policy-scoped GraphQL and server-sent event channels.
- FastAPI services for case orchestration, feedback capture, eval jobs, model routing, and audit writes.
- Every request carries identity, coalition, compartment, mission, and purpose-of-use claims.
- Batch and streaming pipelines bind events to typed objects, relationships, lineage, and temporal validity.
- Search indexes and vector stores inherit object-level permissions instead of duplicating policy logic.
- Copilots perform triage, enrichment, correlation, summarization, recommendation, and eval generation.
- Tool use is policy-filtered, evidence-linked, and blocked at operational action boundaries.
- Need-to-know access at row, column, entity, relationship, and derived-output levels.
- Prompt governance, model governance, immutable audit trails, and approval-token enforcement.
- Signed artifacts, ring rollouts, canary metrics, health gates, rollback manifests, and runtime kill switches.
- Self-improvement proposals ship only as reversible, human-approved releases.
Data and Ontology
Core object types
- Person, Organization, Asset, Location, CyberIndicator, Event, Source, Case, Mission, IntelProduct, ApprovalDecision.
- Each object stores confidence, caveats, source lineage, temporal state, sensitivity labels, coalition releasability, and permitted purposes.
- Relationships include observed_at, associated_with, controls, communicates_with, derived_from, approved_by, and contradicts.
How ontology drives agents
- Agents reason over typed facts, not loose text blobs, and must cite object IDs plus lineage hashes in every recommendation.
- Temporal validity prevents stale intelligence from being treated as current operational truth.
- Permission tags propagate into summaries, embeddings, generated reports, and downstream action packages.
Self-Improvement Loop
Operator corrections, query reformulations, dismissed alerts, accepted recommendations, mission outcomes, latency traces, and policy denials are written as immutable feedback events.
AIP eval builders transform signals into labeled test cases, golden traces, counterexamples, adversarial prompts, routing benchmarks, and workflow regression suites.
Improvement agents generate prompt patches, workflow DAG changes, retrieval filters, threshold updates, or model-routing changes with risk scoring and rollback plans.
Foundry validation jobs and AIP evals require precision, recall, policy, latency, cost, bias, and explanation-quality thresholds before a human reviewer can approve.
Apollo promotes signed manifests through dev, staging-canary, mission-canary, and production rings with automatic rollback on drift, policy violations, or trust-score drops.
Code Examples
Python FastAPI policy-scoped feedback endpoint
from fastapi import FastAPI, Depends
from pydantic import BaseModel, Field
from uuid import uuid4
from datetime import datetime, timezone
app = FastAPI(title="ClearGlassInc Artemis Control API")
class FeedbackEvent(BaseModel):
mission_id: str
case_id: str
object_id: str | None = None
signal_type: str = Field(pattern="^(correction|accept|reject|outcome|latency|policy_denial)$")
payload: dict
@app.post("/v1/feedback")
async def capture_feedback(event: FeedbackEvent, principal=Depends(require_operator)):
enforce_need_to_know(principal, mission_id=event.mission_id, purpose="improve_ai")
record = {
"id": f"fb_{uuid4().hex}",
"ts": datetime.now(timezone.utc).isoformat(),
"actor": principal.subject,
"coalition": principal.coalition,
"labels": principal.labels,
"event": event.model_dump(),
"lineage_hash": hash_payload(event.payload),
}
await event_bus.publish("artemis.feedback.captured", record)
await immutable_audit.write(record)
return {"accepted": True, "feedback_id": record["id"]}Ontology-driven SQL pattern
SELECT e.event_id, e.event_type, e.valid_time, r.relationship_type,
o.object_id, o.object_type, o.confidence, o.sensitivity_label
FROM ontology_events e
JOIN ontology_relationships r ON r.source_id = e.event_id
JOIN ontology_objects o ON o.object_id = r.target_id
WHERE e.mission_id = :mission_id
AND e.valid_time > now() - interval '30 minutes'
AND policy_can_read(:principal, o.object_id, 'operational_triage')
ORDER BY e.valid_time DESC, o.confidence DESC;Governed workflow state machine
states:
ingest: [resolve_entities]
resolve_entities: [triage_with_aip]
triage_with_aip: [enrich, dismiss_low_confidence]
enrich: [correlate, request_more_data]
correlate: [draft_recommendation]
draft_recommendation: [human_review]
human_review:
approve: [prepare_action_package]
reject: [capture_counterexample]
prepare_action_package: [apollo_audit_checkpoint, gotham_case_update]
capture_counterexample: [eval_backlog]Scenario Walkthrough
A live cyber-intelligence event enters ClearGlassInc Artemis from a coalition sensor feed. Foundry normalizes it into a CyberIndicator linked to an Asset, Location, Mission, Source, and Case. AIP triage agents compare it against recent behavior, Gotham case context, and policy-scoped retrieval results. The commander copilot drafts a recommended response package, but Apollo and policy-as-code require an operator approval token before any operationally significant action is opened or exported.
The operator rejects one correlation as overconfident and adds a correction. That correction becomes a feedback event, then an eval case. The next nightly improvement run proposes a stricter confidence threshold and a prompt patch requiring stronger temporal evidence. The patch beats baseline precision without policy regressions, receives human approval, and Apollo rolls it out to staging-canary. If drift appears, Artemis rolls back to the previous signed manifest automatically.