# ClearGlassInc Artemis — Self-Evolving AI Intelligence Platform Blueprint ClearGlassInc Artemis is a full-stack, coalition-aware intelligence platform blueprint built on Palantir Gotham, Foundry, AIP, and Apollo. It fuses live and historical data, reasons over a governed ontology, assists operators at machine speed, and proposes safe upgrades to prompts, workflows, heuristics, and model routing only through explicit human approval. ## System Architecture ### Platform roles - **Gotham** provides operational intelligence, investigations, entity tracking, link analysis, case timelines, and commander views. - **Foundry** provides data integration, transformation pipelines, ontology object types, Actions, Functions, application logic, lineage, and governed writeback. - **AIP** provides copilots, tool-using agents, evaluations, prompt/workflow orchestration, model routing, and human-in-the-loop automation. - **Apollo** provides secure deployment, runtime configuration, canaries, rollback, promotion, and fleet control across enclaves. ### End-to-end topology ```mermaid flowchart LR subgraph Sources[Live + Historical Sources] ISR[ISR / IoT / sensor telemetry] CYBER[EDR / NDR / SIEM / Wazuh] OSINT[OSINT collectors] HUM[Operator notes + debriefs] DOCS[Reports / PDFs / legal / contracts] CRM[CRM + mission outcome systems] end subgraph Foundry[Palantir Foundry] CONN[Data Connections] PIPE[Pipeline Builder + Code Repositories] ONTO[Ontology: Objects, Links, Actions] FNS[Functions + Application Logic] FEAT[Feature Store + Eval Corpora] end subgraph Gotham[Palantir Gotham] ENT[Entity resolution + link analysis] CASE[Cases + investigations] TIMELINE[Temporal operational picture] OPS[Commander workflows] end subgraph AIP[Palantir AIP] ROUTER[Model Router] AGENTS[Agent Orchestrator] TOOLS[Approved Tool Registry] EVALS[Evaluation Harness] COPILOTS[Analyst + Commander Copilots] end subgraph Runtime[ClearGlassInc Artemis Runtime] UI[TypeScript Web UI] API[Zero-trust API Gateway] BUS[Kafka / Pulsar Event Bus] SEARCH[Hybrid Search + Vector Retrieval] POLICY[OPA + ABAC Policy Layer] OBS[OpenTelemetry + SIEM] end subgraph Apollo[Palantir Apollo] SIGN[Signed artifacts] CANARY[Canary rings] FLAGS[Runtime flags] ROLLBACK[Automated rollback] end Sources --> CONN --> PIPE --> ONTO ONTO --> ENT --> CASE --> OPS ONTO --> SEARCH --> TOOLS UI --> API --> POLICY API --> AGENTS --> TOOLS --> FNS --> ONTO BUS --> AGENTS AGENTS --> EVALS --> FEAT OBS --> CANARY Apollo --> Runtime Apollo --> AIP ``` ### Layer responsibilities | Layer | Responsibility | Production controls | |---|---|---| | Frontend | Analyst workbench, commander console, ModelOps console, mission timeline, approval queues | WebAuthn, classification banners, audit overlays, redaction-aware rendering | | API gateway | REST/GraphQL ingress, mTLS, JWT/SPIFFE identity propagation, request signing | Rate limits, schema validation, replay protection, policy pre-checks | | Backend services | Fusion, correlation, enrichment, workflow execution, self-improvement proposals | Idempotency keys, event sourcing, circuit breakers, typed contracts | | Data layer | Lakehouse, low-latency stores, immutable audit ledger, feature/eval corpora | Lineage, retention policy, encryption, dataset ACLs | | Ontology layer | Mission entities, relationships, Actions, Functions, temporal state | Object-level permissions, confidence, provenance, policy-bound Actions | | AI orchestration | Agents, copilots, tools, prompts, evals, model routing | Tool allowlists, approval gates, deterministic traces, eval thresholds | | Policy layer | Need-to-know, coalition boundaries, purpose-of-use, prompt/model governance | OPA/Rego, ABAC, row/column/entity filters, signed policies | | Observability | Logs, traces, metrics, eval dashboards, drift and trust telemetry | OpenTelemetry, SIEM export, immutable audit, SLO alerts | | Deployment | Secure rollout, rollback, promotion, runtime control | Apollo canaries, signed artifacts, health gates, config freezes | ## Data and Ontology ### Core ontology objects ```yaml objectTypes: Mission: pk: mission_id properties: [name, objective, theater, classification, coalition_tags, active_window, roe_ref, commander] Event: pk: event_id properties: [event_type, severity, occurred_at, detected_at, source_system, confidence, classification] Alert: pk: alert_id properties: [rule_id, score, status, disposition, created_at, assigned_to, sla_deadline] Case: pk: case_id properties: [mission_id, priority, status, owner, disposition, created_at, closed_at] Entity: pk: entity_id properties: [kind, canonical_name, aliases, risk_score, confidence, valid_time, tx_time] Device: pk: device_id properties: [hostname, ip, mac, owner_ref, compromise_score, last_seen] Location: pk: location_id properties: [lat, lon, geohash, area_name, jurisdiction, confidence] Evidence: pk: evidence_id properties: [source_uri, hash, collected_at, collector, classification, handling_caveats] OperatorFeedback: pk: feedback_id properties: [operator_id, case_id, target_ref, correction_type, label, rationale, created_at] PromptVersion: pk: prompt_version_id properties: [name, version, body_hash, author, eval_score, approval_state, deployed_ring] WorkflowVersion: pk: workflow_version_id properties: [name, version, graph_hash, eval_score, approval_state, deployed_ring] ``` ### Relationships ```yaml relationships: - Mission CONSTRAINS Case - Case CONTAINS Alert - Alert TRIGGERED_BY Event - Event OBSERVED_AT Location - Event INVOLVES Entity - Entity USES Device - Evidence SUPPORTS Event - OperatorFeedback CORRECTS Alert - PromptVersion POWERS Agent - WorkflowVersion ORCHESTRATES Agent - Agent PRODUCES IntelProduct ``` ### Confidence, lineage, and temporal semantics ```python from dataclasses import dataclass from datetime import datetime, timezone from math import exp @dataclass(frozen=True) class EvidenceScore: source_confidence: float corroboration_count: int age_hours: float policy_context_weight: float def confidence(score: EvidenceScore) -> float: corroboration = min(1.0, 0.45 + 0.15 * score.corroboration_count) recency_decay = exp(-score.age_hours / (24 * 21)) raw = score.source_confidence * corroboration * recency_decay * score.policy_context_weight return round(max(0.0, min(raw, 1.0)), 4) def lineage_record(parent_ids: list[str], transform: str, model: str | None) -> dict: return { "parent_evidence_ids": parent_ids, "transform_version": transform, "model_version": model, "tx_time": datetime.now(timezone.utc).isoformat(), } ``` Artemis stores both **valid time** (when a fact was true in the world) and **transaction time** (when the platform knew or changed the fact). Agents use that bitemporal state to answer questions such as “what did we know before the approval?” and “which inference changed after operator correction?” ## AI and Agent Design ### Copilots - **Analyst Copilot**: explains alerts, summarizes entities, cites evidence, drafts hypotheses, asks clarifying questions, and prepares investigation notes. - **Commander Copilot**: compares courses of action, quantifies risk, identifies approval gates, and produces decision briefs. - **ModelOps Copilot**: reviews prompt diffs, eval regressions, drift events, cost/latency changes, and proposed routing updates. - **Coalition Copilot**: creates releasable summaries, strips restricted fields, and verifies coalition caveats before sharing. ### Multi-agent workflow ```mermaid stateDiagram-v2 [*] --> Ingested Ingested --> TriageAgent TriageAgent --> EnrichmentAgent: policy allows enrichment EnrichmentAgent --> CorrelationAgent CorrelationAgent --> SummarizationAgent SummarizationAgent --> RecommendationAgent RecommendationAgent --> HumanApproval: operational significance >= threshold RecommendationAgent --> AutoClose: low-risk informational HumanApproval --> ExecuteAction: approved HumanApproval --> LearnFromRejection: rejected or edited ExecuteAction --> MissionOutcome AutoClose --> MissionOutcome MissionOutcome --> EvalCorpusUpdate LearnFromRejection --> EvalCorpusUpdate ``` ### Tool-using agent contract Agents can query policy-filtered ontology objects, search evidence, generate intelligence products, open cases, and prepare action packages. They cannot execute operationally significant actions without a human approval token bound to mission, purpose, classification, and action type. ```python from pydantic import BaseModel, Field from typing import Literal class ToolRequest(BaseModel): tool_name: str mission_id: str purpose: Literal["triage", "investigation", "briefing", "approval_package"] args: dict class ToolResult(BaseModel): ok: bool data: dict | list | None = None citations: list[str] = Field(default_factory=list) audit_id: str ``` ## Self-Improvement Loop ### Signals captured - Operator feedback: labels, corrections, rejections, edited summaries, trust ratings, escalation decisions. - Query logs: prompts, retrieved objects, redactions, tool calls, latency, model route, token cost. - Alert outcomes: true positive, false positive, duplicate, stale, insufficient evidence, policy blocked. - Mission outcomes: commander disposition, time-to-decision, downstream impact, safety incidents, post-action review. - Runtime telemetry: drift, retrieval misses, hallucination flags, failed citations, eval regressions. ### Safe upgrade pipeline ```mermaid flowchart TD Signals[Feedback + logs + outcomes] --> Normalize[Normalize + redact] Normalize --> Evals[Generate eval cases] Normalize --> Candidates[Generate prompt/workflow/routing candidates] Candidates --> Offline[Offline eval suite] Offline --> Risk[Risk scoring + policy diff] Risk --> Review[Human review board] Review --> Canary[Apollo canary ring] Canary --> Monitor[Live eval + SLO monitoring] Monitor --> Promote[Promote] Monitor --> Rollback[Rollback] Promote --> Registry[Version registry] Rollback --> Registry ``` ### Control principles 1. Artemis may propose improvements but may not change mission objectives, rules of engagement, legal policy, classification policy, or action authority. 2. Every prompt, workflow, routing policy, and heuristic is versioned, signed, evaluated, and auditable. 3. Deployment requires eval thresholds, policy review, human approval, and Apollo canary success. 4. Rollback is automatic on safety regressions, hallucination-rate increases, citation failures, policy-denial anomalies, or latency SLO breaches. ### Metrics | Metric | Purpose | Example threshold | |---|---|---| | Precision | Reduce false positives | `>= 0.92` for critical alerts | | Recall | Avoid missing mission-relevant events | `>= 0.86` for priority feeds | | Citation accuracy | Ensure claims are evidence-backed | `>= 0.98` | | Policy violation rate | Governance safety | `0` allowed violations | | Latency p95 | Machine-speed operations | `< 1.5s` triage, `< 8s` package | | Operator trust | Human acceptance and edit distance | `>= 4.3/5` and falling edit distance | | Mission impact | Decision speed and outcome quality | `>= 20%` time-to-decision reduction | ## Full-Stack Implementation ### Repository layout ```text artemis/ apps/web/ # Next.js/TypeScript operator UI apps/api/ # FastAPI gateway and service façade services/fusion/ # entity resolution + correlation services/agent-runtime/ # AIP tool executor and workflow runner services/self-improvement/ # eval generation + candidate proposals packages/policy/ # Rego policies and Python policy client packages/ontology/ # typed ontology models and query builders infra/apollo/ # release channels, rings, health gates infra/terraform/ # cloud and enclave infrastructure observability/ # dashboards, OpenTelemetry config, alerts ``` ### Web UI blueprint - Mission header with classification, coalition scope, current objective, and Apollo release ring. - Real-time event stream with severity, confidence, lineage, and policy status. - Ontology graph panel powered by policy-filtered entity relationships. - Copilot sidecar with citations, tool traces, approval buttons, and redaction warnings. - ModelOps dashboard for eval results, prompt diffs, routing changes, and canary health. ### Backend service blueprint - API Gateway: FastAPI, Pydantic contracts, mTLS, JWT validation, OPA authorization, OpenTelemetry spans. - Event bus: Kafka/Pulsar topics for raw events, normalized events, alerts, cases, feedback, eval cases, deployment outcomes. - Data layer: Foundry datasets for canonical history, low-latency Redis/Postgres for hot state, vector index for evidence retrieval. - Model router: routes by mission classification, latency SLO, eval score, cost budget, tool availability, and data residency. - Workflow engine: state machines for triage, enrichment, approval, action-package preparation, and post-action learning. ## Security and Governance ### Need-to-know authorization Artemis enforces authorization at multiple layers: API request, ontology query, retrieval result, generated answer, tool call, and UI render. A user must have the right role, mission assignment, clearance, coalition tag, purpose-of-use, and compartment access before viewing or acting on data. ```rego package artemis.authz default allow := false allow if { input.subject.clearance_level >= input.resource.classification_level input.resource.mission_id in input.subject.assigned_missions every tag in input.resource.coalition_tags { tag in input.subject.coalition_tags } input.action in input.subject.allowed_actions input.purpose in input.subject.allowed_purposes not denied_compartment } denied_compartment if { some c in input.resource.compartments not c in input.subject.compartments } ``` ### Governance controls - Row, column, and entity-level filters in Foundry and the API gateway. - Classification-aware retrieval that excludes restricted evidence before model context construction. - Prompt governance with signed versions, owners, approvals, eval scores, and deployment rings. - Model governance with approved model inventory, data residency rules, telemetry, and fallback routes. - Immutable audit logs for every query, object access, prompt render, tool call, approval, deployment, and rollback. - Zero-trust execution with service identity, workload attestation, sealed secrets, egress allowlists, and sandboxed tool calls. ## Code Examples ### FastAPI gateway with policy and audit envelope ```python from fastapi import FastAPI, Depends, HTTPException, Request from pydantic import BaseModel from opentelemetry import trace app = FastAPI(title="ClearGlassInc Artemis API") tracer = trace.get_tracer("artemis.api") class TriageRequest(BaseModel): mission_id: str event_id: str purpose: str = "triage" class Principal(BaseModel): sub: str clearance_level: int assigned_missions: list[str] coalition_tags: list[str] allowed_actions: list[str] allowed_purposes: list[str] compartments: list[str] async def principal_from_request(request: Request) -> Principal: # Production: validate mTLS-bound JWT/SPIFFE identity and map claims. return Principal.model_validate_json(request.headers["x-artemis-principal"]) async def opa_allow(principal: Principal, action: str, resource: dict, purpose: str) -> bool: # Production: call OPA sidecar or embedded policy bundle. return purpose in principal.allowed_purposes and resource["mission_id"] in principal.assigned_missions @app.post("/v1/triage") async def triage(req: TriageRequest, principal: Principal = Depends(principal_from_request)): with tracer.start_as_current_span("triage") as span: span.set_attribute("mission_id", req.mission_id) resource = {"mission_id": req.mission_id, "classification_level": 3, "coalition_tags": ["REL-CG"]} if not await opa_allow(principal, "triage:event", resource, req.purpose): raise HTTPException(status_code=403, detail="policy denied") result = await run_triage_workflow(req.event_id, req.mission_id, principal.sub) await write_audit("triage:event", principal.sub, req.model_dump(), result.audit_id) return result.model_dump() ``` ### Ontology-driven query builder ```python from dataclasses import dataclass @dataclass(frozen=True) class OntologyQuery: object_type: str filters: dict include_links: list[str] as_of: str | None = None def alert_context_query(mission_id: str, alert_id: str) -> OntologyQuery: return OntologyQuery( object_type="Alert", filters={"mission_id": mission_id, "alert_id": alert_id, "status": {"$ne": "redacted"}}, include_links=["TRIGGERED_BY", "INVOLVES", "SUPPORTED_BY", "CONSTRAINED_BY"], ) async def execute_foundry_query(query: OntologyQuery, principal: Principal) -> dict: filtered = apply_need_to_know_filter(query, principal) return await foundry_ontology_client.search(filtered) ``` ### Workflow state machine ```python from enum import StrEnum from pydantic import BaseModel class State(StrEnum): INGESTED = "ingested" TRIAGED = "triaged" ENRICHED = "enriched" CORRELATED = "correlated" SUMMARIZED = "summarized" PENDING_APPROVAL = "pending_approval" EXECUTED = "executed" CLOSED = "closed" class WorkflowContext(BaseModel): mission_id: str event_id: str confidence: float = 0.0 operational_significance: float = 0.0 approvals: list[str] = [] async def next_step(ctx: WorkflowContext, state: State) -> State: if state == State.INGESTED: ctx.confidence = await triage_agent(ctx) return State.TRIAGED if state == State.TRIAGED: await enrichment_agent(ctx) return State.ENRICHED if state == State.ENRICHED: ctx.operational_significance = await correlation_agent(ctx) return State.CORRELATED if state == State.CORRELATED: await summarization_agent(ctx) return State.SUMMARIZED if state == State.SUMMARIZED and ctx.operational_significance >= 0.65: await create_approval_package(ctx) return State.PENDING_APPROVAL return State.CLOSED ``` ### AIP-style tool execution with approval gate ```python class ActionPackage(BaseModel): case_id: str mission_id: str recommendation: str evidence_ids: list[str] risk_score: float requires_approval: bool = True async def prepare_action_package(case_id: str, principal: Principal) -> ActionPackage: case = await execute_foundry_query( OntologyQuery("Case", {"case_id": case_id}, ["CONTAINS", "SUPPORTED_BY"]), principal ) recommendation = await model_router.complete( task="commander_recommendation", context=case, constraints={"must_cite_evidence": True, "no_unapproved_action": True}, ) return ActionPackage( case_id=case_id, mission_id=case["mission_id"], recommendation=recommendation.text, evidence_ids=recommendation.citations, risk_score=recommendation.risk_score, ) async def execute_operational_action(pkg: ActionPackage, approval_token: str | None) -> dict: if pkg.requires_approval and not await validate_approval_token(approval_token, pkg): raise PermissionError("human approval required") return await foundry_action_client.invoke("OpenOperationalTask", pkg.model_dump()) ``` ### Eval and self-upgrade candidate generation ```python class EvalCase(BaseModel): input_snapshot_ref: str expected_label: str operator_rationale: str policy_context: dict class CandidatePatch(BaseModel): target: str version_from: str diff: str rationale: str offline_scores: dict[str, float] async def build_eval_cases(feedback_batch: list[dict]) -> list[EvalCase]: cases: list[EvalCase] = [] for fb in feedback_batch: if fb["correction_type"] in {"false_positive", "missed_correlation", "bad_summary"}: cases.append(EvalCase( input_snapshot_ref=fb["snapshot_ref"], expected_label=fb["label"], operator_rationale=fb["rationale"], policy_context=fb["policy_context"], )) return cases async def propose_prompt_patch(eval_cases: list[EvalCase], prompt_name: str) -> CandidatePatch: current = await prompt_registry.get_active(prompt_name) diff = await model_router.complete( task="prompt_improvement_proposal", context={"current_prompt": current.body, "eval_failures": [c.model_dump() for c in eval_cases]}, constraints={"output_format": "unified_diff", "do_not_change_policy": True}, ) scores = await eval_runner.run(prompt_diff=diff.text, cases=eval_cases) return CandidatePatch( target=prompt_name, version_from=current.version, diff=diff.text, rationale="Reduce observed eval failures while preserving policy constraints.", offline_scores=scores, ) ``` ### SQL eval dashboard view ```sql create or replace view artemis_eval_dashboard as select prompt_name, prompt_version, workflow_version, model_route, count(*) as eval_count, avg(case when passed then 1 else 0 end) as pass_rate, avg(latency_ms) as avg_latency_ms, percentile_cont(0.95) within group (order by latency_ms) as p95_latency_ms, avg(citation_accuracy) as citation_accuracy, sum(case when policy_violation then 1 else 0 end) as policy_violations from eval_runs where executed_at >= now() - interval '30 days' group by prompt_name, prompt_version, workflow_version, model_route; ``` ## Scenario Walkthrough 1. **Live event arrives**: an encrypted telemetry stream reports anomalous access from a coalition-restricted device. The ingestion normalizer writes a raw event, hashes the evidence, and emits `normalized.event.created`. 2. **Ontology update**: Foundry pipelines map the event to `Event`, link it to `Device`, `Location`, and `Mission`, and compute initial confidence from source reliability, recency, and corroboration. 3. **Agent triage**: the Triage Agent receives the event from Kafka, queries the policy-filtered ontology, and classifies it as medium severity because the device is mission-relevant but evidence is single-source. 4. **Enrichment and correlation**: the Enrichment Agent pulls recent cyber telemetry, the Correlation Agent finds two related failed logins, and confidence rises above the alert threshold. 5. **Recommendation**: the Commander Copilot drafts an action package recommending temporary account isolation, additional collection, and analyst review. Evidence citations and policy constraints are embedded in the package. 6. **Approval gate**: because account isolation is operationally significant, the system routes the package to a cleared operator. The operator edits the rationale, approves collection, but rejects immediate isolation as too disruptive. 7. **Execution**: approved collection runs through a Foundry Action. Rejected isolation is logged with operator rationale and linked to the case. 8. **Learning signal**: the rejection, edit distance, final disposition, and later mission outcome become `OperatorFeedback` and eval cases. 9. **Candidate upgrade**: the self-improvement controller detects a pattern of over-aggressive isolation recommendations in similar cases and proposes a prompt patch plus a workflow threshold change. 10. **Evaluation**: offline evals show higher precision, unchanged recall, zero policy violations, and improved operator trust prediction. 11. **Human review**: the ModelOps board approves the patch for Apollo canary ring `r1` only. 12. **Canary and rollback safety**: live metrics remain healthy, so Apollo promotes to `r2`. If citation accuracy, precision, or policy-denial anomalies regress, Apollo rolls back to the previous signed prompt and workflow versions. ## How Artemis Gets Better Safely Artemis improves by converting operator behavior and mission outcomes into governed evals, not by autonomously rewriting its goals. It can recommend prompt edits, workflow graph changes, retrieval parameters, model routes, and heuristic thresholds. Humans approve or reject those recommendations. Apollo deploys approved versions gradually, observability verifies live behavior, and immutable audit records preserve every reason, score, diff, approval, and rollback. ## Defensive Cyber Technique Tool Merge ClearGlassInc Artemis now exposes an operator-facing **Attack Lifecycle Defense Matrix** in the command console. The tool is intentionally defensive and educational: it helps analysts normalize common cybersecurity lifecycle terms into observable risk states, candidate telemetry, and approved countermeasures. ### Technique catalog | Technique | Operational meaning | Defensive posture | |---|---|---| | Exploitation | Abuse of a vulnerability, unpatched system, or misconfiguration to produce unintended behavior. | Patch management, vulnerability scanning, secure input validation, exploit telemetry correlation. | | Credential Theft | Unauthorized acquisition of usernames, passwords, hashes, session tokens, or authentication material. | MFA, least privilege, credential guardrails, token anomaly analytics. | | LSASS Dumping | Attempted memory access against `lsass.exe` to obtain cached credentials or password hashes. | Windows Defender Credential Guard, LSA Protection, handle-access alerting, privileged process monitoring. | | AMSI Bypass | Attempts to blind or disable the Windows Antimalware Scan Interface before script inspection. | Script Block Logging, transcription, AMSI tamper analytics, behavior-based EDR. | | Defender Disabling | Unauthorized changes that pause, disable, or weaken Microsoft Defender Antivirus controls. | Tamper Protection, managed baselines, drift detection, blocked local policy changes. | | Defense Evasion | Obfuscation, injection, timestamp manipulation, or other behavior intended to avoid detection. | EDR heuristic analytics, provenance checks, suspicious process lineage detection. | | Stealth Persistence | Maintaining access across restart or credential rotation while minimizing visible footprint. | Autorun audits, scheduled-task baselines, WMI consumer monitoring, configuration-delta review. | | Lateral Movement | Movement from one affected system to other hosts or trust zones using remote admin pathways. | Segmentation, privileged access boundaries, east-west authentication monitoring. | | Unauthorized Access | Any actor, process, or device reaches data or segments without explicit authorization. | Zero Trust Architecture, entity-level permissions, continuous verification, purpose-of-use enforcement. | ### Ontology alignment ```yaml objectTypes: DefensiveTechnique: pk: technique_id properties: - name - lifecycle_phase - operational_definition - defensive_countermeasure - telemetry_sources - approved_playbooks - classification relationships: - DefensiveTechnique MITIGATES ThreatPattern - DefensiveTechnique MAPS_TO MitreAttackTechnique - Alert INDICATES DefensiveTechnique - Playbook IMPLEMENTS DefensiveTechnique - OperatorFeedback TUNES DefensiveTechnique ``` ### Precision scoring helper ```python from dataclasses import dataclass @dataclass(frozen=True) class TechniqueSignal: technique_id: str true_positive: int false_positive: int false_negative: int @property def precision(self) -> float: denom = self.true_positive + self.false_positive return self.true_positive / denom if denom else 0.0 @property def recall(self) -> float: denom = self.true_positive + self.false_negative return self.true_positive / denom if denom else 0.0 @property def f1(self) -> float: p, r = self.precision, self.recall return 2 * p * r / (p + r) if p + r else 0.0 ``` The same technique catalog drives UI education, AIP agent grounding, SIEM correlation labels, operator feedback capture, and eval slices for precision/recall measurement. ## Python Precision Implementation Appendix ClearGlassInc Artemis uses Python as the precision control plane for deterministic scoring, eval generation, and guarded self-upgrade proposals. The implementation below is intentionally side-effect free until an approved Apollo rollout receives a signed change ticket. ### Precision-first package layout ```text artemis_precision/ api.py # FastAPI read/write boundary with policy checks events.py # Typed event ingestion contracts ontology.py # Foundry/Gotham object and relationship adapters policy.py # OPA/ABAC authorization client evals.py # Golden-set and live-shadow eval runners optimizer.py # Prompt/workflow/model-route proposal engine approvals.py # Human approval ticket state machine apollo.py # Signed deployment, canary, and rollback adapter observability.py # OpenTelemetry spans, metrics, redacted logs ``` ### Deterministic self-upgrade proposal model ```python from __future__ import annotations from dataclasses import dataclass, field from datetime import UTC, datetime from enum import StrEnum from typing import Literal from uuid import uuid4 class ChangeKind(StrEnum): PROMPT = "prompt" WORKFLOW = "workflow" MODEL_ROUTE = "model_route" HEURISTIC = "heuristic" @dataclass(frozen=True) class EvalMetrics: precision: float recall: float latency_p95_ms: int policy_violations: int citation_accuracy: float operator_trust_delta: float def is_safe_regression_free(self, baseline: "EvalMetrics") -> bool: return ( self.policy_violations == 0 and self.precision >= baseline.precision and self.recall >= baseline.recall * 0.995 and self.citation_accuracy >= baseline.citation_accuracy and self.latency_p95_ms <= int(baseline.latency_p95_ms * 1.10) ) @dataclass(frozen=True) class UpgradeProposal: proposal_id: str kind: ChangeKind mission_context: str root_cause: str diff_summary: str baseline: EvalMetrics candidate: EvalMetrics created_at: datetime = field(default_factory=lambda: datetime.now(UTC)) status: Literal["draft", "blocked", "review", "approved", "rejected", "rolled_back"] = "draft" def propose_upgrade( *, kind: ChangeKind, mission_context: str, root_cause: str, diff_summary: str, baseline: EvalMetrics, candidate: EvalMetrics, ) -> UpgradeProposal: """Create a human-reviewable proposal; never deploy directly from this path.""" status: Literal["blocked", "review"] = ( "review" if candidate.is_safe_regression_free(baseline) else "blocked" ) return UpgradeProposal( proposal_id=f"artemis-upgrade-{uuid4()}", kind=kind, mission_context=mission_context, root_cause=root_cause, diff_summary=diff_summary, baseline=baseline, candidate=candidate, status=status, ) ``` ### Human-approved Apollo rollout contract ```python @dataclass(frozen=True) class ApprovalTicket: ticket_id: str proposal_id: str approver: str decision: Literal["approved", "rejected"] reason: str signed_at: datetime class ApolloRolloutClient: def deploy_canary(self, proposal: UpgradeProposal, approval: ApprovalTicket) -> str: if proposal.status != "review" or approval.decision != "approved": raise PermissionError("Apollo rollout requires explicit human approval") # Real implementation signs the artifact, pins the exact prompt/workflow/model-route # version, deploys to ring-0, and emits immutable provenance to Foundry/Apollo logs. return f"apollo-rollout://{proposal.proposal_id}/ring-0" def rollback(self, rollout_id: str, reason: str) -> str: # Real implementation restores the previous signed artifact and records the reason. return f"apollo-rollback://{rollout_id}?reason={reason}" ``` This appendix turns the architecture into an implementable Python control plane: feedback becomes typed evidence, evidence becomes evals, evals become reviewable proposals, and only signed human approval can trigger Apollo canaries or rollback. ## Production Implementation Addendum This addendum makes the ClearGlassInc Artemis blueprint directly implementable by defining concrete service contracts, Python-first control-plane modules, TypeScript UI boundaries, SQL schemas, and deployment guardrails. The design assumes Gotham, Foundry, AIP, and Apollo are the system-of-record platforms, while Artemis-owned services provide narrowly scoped orchestration, policy checks, event normalization, and operator experience. ### Full-stack reference architecture ```text [Browser / Mission Console] ├─ Next.js app router ├─ WebSocket event stream ├─ Copilot panel with cited answers └─ Approval queue with signed decisions │ ▼ [API Gateway / FastAPI] ├─ mTLS + JWT/SPIFFE identity validation ├─ schema validation and request signing ├─ OPA policy pre-checks ├─ audit envelope writer └─ OpenTelemetry trace root │ ├──────────────► [AIP Agent Runtime] │ ├─ model router │ ├─ prompt registry │ ├─ tool executor │ └─ eval harness │ ├──────────────► [Foundry Ontology] │ ├─ object search │ ├─ Actions │ ├─ Functions │ └─ lineage graph │ ├──────────────► [Gotham Operational Graph] │ ├─ investigations │ ├─ entity tracking │ └─ commander picture │ └──────────────► [Event Bus] ├─ normalized.event.created ├─ alert.dispositioned ├─ operator.feedback.created ├─ eval.case.created └─ deployment.rollout.observed ``` ### Python service modules ```python # artemis_precision/events.py from __future__ import annotations from datetime import UTC, datetime from enum import StrEnum from typing import Any, Literal from uuid import uuid4 from pydantic import BaseModel, Field, field_validator class Classification(StrEnum): UNCLASSIFIED = "U" CONTROLLED = "CUI" SECRET = "S" TOP_SECRET = "TS" class NormalizedEvent(BaseModel): event_id: str = Field(default_factory=lambda: f"evt_{uuid4().hex}") mission_id: str source_system: str event_type: str occurred_at: datetime detected_at: datetime = Field(default_factory=lambda: datetime.now(UTC)) classification: Classification coalition_tags: list[str] = Field(default_factory=list) payload_hash: str attributes: dict[str, Any] @field_validator("payload_hash") @classmethod def require_sha256(cls, value: str) -> str: if len(value) != 64 or any(c not in "0123456789abcdef" for c in value.lower()): raise ValueError("payload_hash must be a lowercase SHA-256 hex digest") return value.lower() class FeedbackSignal(BaseModel): feedback_id: str = Field(default_factory=lambda: f"fb_{uuid4().hex}") mission_id: str case_id: str operator_id: str target_ref: str correction_type: Literal[ "true_positive", "false_positive", "missed_correlation", "bad_summary", "unsafe_recommendation", "policy_overblock", ] label: str rationale: str created_at: datetime = Field(default_factory=lambda: datetime.now(UTC)) ``` ```python # artemis_precision/policy.py from __future__ import annotations from dataclasses import dataclass from typing import Any @dataclass(frozen=True) class Principal: subject: str clearance_level: int assigned_missions: frozenset[str] coalition_tags: frozenset[str] compartments: frozenset[str] allowed_actions: frozenset[str] allowed_purposes: frozenset[str] @dataclass(frozen=True) class ResourceRef: resource_id: str mission_id: str classification_level: int coalition_tags: frozenset[str] compartments: frozenset[str] def authorize(principal: Principal, action: str, purpose: str, resource: ResourceRef) -> bool: return ( principal.clearance_level >= resource.classification_level and resource.mission_id in principal.assigned_missions and resource.coalition_tags.issubset(principal.coalition_tags) and resource.compartments.issubset(principal.compartments) and action in principal.allowed_actions and purpose in principal.allowed_purposes ) def redact_for_principal(record: dict[str, Any], principal: Principal) -> dict[str, Any]: redacted: dict[str, Any] = {} for key, value in record.items(): policy = record.get("_field_policy", {}).get(key, {}) min_clearance = int(policy.get("clearance_level", 0)) required_tags = set(policy.get("coalition_tags", [])) if principal.clearance_level >= min_clearance and required_tags.issubset(principal.coalition_tags): redacted[key] = value else: redacted[key] = "" redacted.pop("_field_policy", None) return redacted ``` ```python # artemis_precision/workflows.py from __future__ import annotations from dataclasses import dataclass, field from enum import StrEnum from typing import Awaitable, Callable class WorkflowState(StrEnum): INGESTED = "ingested" TRIAGED = "triaged" ENRICHED = "enriched" CORRELATED = "correlated" PACKAGED = "packaged" PENDING_APPROVAL = "pending_approval" EXECUTED = "executed" CLOSED = "closed" @dataclass class MissionWorkflowContext: mission_id: str event_id: str case_id: str | None = None confidence: float = 0.0 significance: float = 0.0 citations: list[str] = field(default_factory=list) approval_token: str | None = None Transition = Callable[[MissionWorkflowContext], Awaitable[WorkflowState]] class WorkflowRunner: def __init__(self, transitions: dict[WorkflowState, Transition], max_steps: int = 12) -> None: self.transitions = transitions self.max_steps = max_steps async def run(self, ctx: MissionWorkflowContext) -> WorkflowState: state = WorkflowState.INGESTED for _ in range(self.max_steps): if state in {WorkflowState.EXECUTED, WorkflowState.CLOSED}: return state transition = self.transitions[state] state = await transition(ctx) raise RuntimeError("workflow exceeded max_steps; refusing silent partial execution") ``` ### Ontology and warehouse schema ```sql create table artemis_events ( event_id text primary key, mission_id text not null, event_type text not null, occurred_at timestamptz not null, detected_at timestamptz not null, source_system text not null, confidence numeric(5,4) not null check (confidence between 0 and 1), classification_level integer not null, coalition_tags text[] not null default '{}', lineage jsonb not null, tx_time timestamptz not null default now() ); create table artemis_feedback ( feedback_id text primary key, mission_id text not null, case_id text not null, operator_id text not null, target_ref text not null, correction_type text not null, label text not null, rationale text not null, created_at timestamptz not null default now(), immutable_hash text not null unique ); create table artemis_upgrade_proposals ( proposal_id text primary key, target_kind text not null check (target_kind in ('prompt', 'workflow', 'model_route', 'heuristic')), target_name text not null, version_from text not null, version_to text not null, diff_summary text not null, eval_metrics jsonb not null, risk_score numeric(5,4) not null check (risk_score between 0 and 1), approval_state text not null check (approval_state in ('draft', 'blocked', 'review', 'approved', 'rejected', 'deployed', 'rolled_back')), created_at timestamptz not null default now() ); ``` ### TypeScript operator console boundary ```tsx // apps/web/components/CopilotActionPackage.tsx import { useMemo } from "react"; type Citation = { evidenceId: string; label: string; allowed: boolean }; type ActionPackage = { caseId: string; recommendation: string; riskScore: number; requiresApproval: boolean; citations: Citation[]; }; export function CopilotActionPackage({ pkg, onApprove, onReject }: { pkg: ActionPackage; onApprove: (caseId: string) => Promise; onReject: (caseId: string, reason: string) => Promise; }) { const blockedCitationCount = useMemo( () => pkg.citations.filter((citation) => !citation.allowed).length, [pkg.citations], ); return (

Commander Recommendation

risk={pkg.riskScore.toFixed(3)}

{pkg.recommendation}

{blockedCitationCount > 0 && (

Some citations are hidden by need-to-know policy.

)}
    {pkg.citations.map((citation) => (
  • {citation.allowed ? citation.label : ""}
    • ))}
{pkg.requiresApproval && (
)}
); } ``` ### Self-improvement control flow ```python # artemis_precision/self_improvement.py from __future__ import annotations from dataclasses import dataclass from artemis_precision.events import FeedbackSignal @dataclass(frozen=True) class EvalThresholds: min_precision: float = 0.92 min_recall: float = 0.86 min_citation_accuracy: float = 0.98 max_policy_violations: int = 0 max_latency_p95_ms: int = 8000 @dataclass(frozen=True) class CandidateScores: precision: float recall: float citation_accuracy: float policy_violations: int latency_p95_ms: int operator_trust_delta: float def candidate_passes(scores: CandidateScores, thresholds: EvalThresholds) -> bool: return ( scores.precision >= thresholds.min_precision and scores.recall >= thresholds.min_recall and scores.citation_accuracy >= thresholds.min_citation_accuracy and scores.policy_violations <= thresholds.max_policy_violations and scores.latency_p95_ms <= thresholds.max_latency_p95_ms ) def feedback_to_eval_slice(feedback: FeedbackSignal) -> str: if feedback.correction_type == "unsafe_recommendation": return "safety_operational_action" if feedback.correction_type == "missed_correlation": return "correlation_recall" if feedback.correction_type == "policy_overblock": return "authorization_precision" return "triage_precision" ``` The self-improvement loop is deliberately asymmetric: feedback can create evals and proposals automatically, but deployment is impossible without review-board approval, signed artifacts, Apollo ring deployment, and live rollback criteria. ## Advanced Feature Merge — Secure-by-Design Production Extensions This merge adds advanced implementation guidance without removing any existing blueprint scope. The additions keep ClearGlassInc Artemis Python-first for deterministic control-plane logic and preserve the invariant that AI may propose improvements, but only humans can authorize operationally significant changes or production rollout. ### Advanced capability map | Capability | Primary platform | Python precision role | Guardrail | |---|---|---|---| | Real-time fusion fabric | Foundry + Gotham | Normalize events, compute confidence, publish typed envelopes | Reject malformed, oversized, duplicate, or unauthorized events before ontology writeback | | Agentic mission workflows | AIP | Run state machines, tool policies, eval gates | Human approval required for case-changing, access-changing, or operational actions | | Coalition release automation | Gotham + Foundry | Redaction checks, caveat validation, releasability scoring | Default-deny if classification, compartment, purpose, or coalition tags conflict | | Self-upgrade review board | AIP + Apollo | Generate eval-backed proposals and rollback triggers | No autonomous policy, objective, model, prompt, or workflow deployment | | Mission observability mesh | Apollo + SIEM | Emit privacy-aware OpenTelemetry, eval, audit, and drift events | No secrets, raw credentials, or restricted payloads in logs or model context | | Resilience control plane | Apollo | Canary, health gates, feature flags, rollback decisions | Signed artifacts, pinned versions, and immutable deployment provenance | ### Extended folder structure ```text artemis/ apps/ web/ # Next.js mission console and ModelOps console app/ mission/[missionId]/page.tsx approvals/page.tsx modelops/page.tsx components/ ClassificationBanner.tsx EvidenceCitationList.tsx ApprovalGate.tsx LiveEventStream.tsx lib/ api-client.ts accessibility.ts redaction.ts services/ api-gateway/ # FastAPI edge service with auth, validation, policy, audit artemis_api/ main.py auth.py schemas.py audit.py errors.py observability.py fusion-engine/ # Python event fusion and confidence scoring artemis_fusion/ normalizer.py correlator.py confidence.py lineage.py agent-runtime/ # AIP tool execution and workflow state machines artemis_agents/ router.py tools.py workflows.py approvals.py safety.py self-improvement/ # Eval generation and candidate upgrade proposals artemis_improve/ signals.py eval_cases.py prompt_candidates.py workflow_candidates.py drift.py rollout.py packages/ ontology/ # Shared typed ontology contracts and query builders policy/ # Rego bundles plus Python policy client telemetry/ # OpenTelemetry conventions, dashboards, alert rules infra/ apollo/ # Ring definitions, promotion rules, rollback health checks sql/ # Warehouse/lakehouse schemas and dashboard views terraform/ # Environment-scoped least-privilege infrastructure tests/ contract/ # API, event, and ontology contract tests policy/ # Positive and negative authorization tests evals/ # Golden-set and regression eval cases resilience/ # Timeout, retry, idempotency, rollback tests ``` ### Python control-plane security defaults ```python # services/api-gateway/artemis_api/schemas.py from __future__ import annotations from datetime import UTC, datetime from typing import Any, Literal from uuid import uuid4 from pydantic import BaseModel, ConfigDict, Field, field_validator MAX_ATTRIBUTES = 128 MAX_STRING = 4096 class StrictModel(BaseModel): model_config = ConfigDict(extra="forbid", str_strip_whitespace=True) class MissionEventIn(StrictModel): event_id: str = Field(default_factory=lambda: f"evt_{uuid4().hex}") mission_id: str = Field(min_length=3, max_length=96, pattern=r"^[a-zA-Z0-9_.:-]+$") source_system: str = Field(min_length=2, max_length=128) event_type: str = Field(min_length=2, max_length=128) occurred_at: datetime classification: Literal["U", "CUI", "S", "TS"] coalition_tags: list[str] = Field(default_factory=list, max_length=32) payload_hash: str = Field(min_length=64, max_length=64) attributes: dict[str, Any] = Field(default_factory=dict) @field_validator("payload_hash") @classmethod def payload_hash_must_be_sha256(cls, value: str) -> str: lowered = value.lower() if any(char not in "0123456789abcdef" for char in lowered): raise ValueError("payload_hash must be a SHA-256 hex digest") return lowered @field_validator("attributes") @classmethod def attributes_must_be_bounded(cls, value: dict[str, Any]) -> dict[str, Any]: if len(value) > MAX_ATTRIBUTES: raise ValueError("attributes exceeds maximum key count") for key, item in value.items(): if len(str(key)) > 128 or len(str(item)) > MAX_STRING: raise ValueError("attributes contains an oversized key or value") return value class SafeError(StrictModel): error_id: str message: str = "request could not be processed" retryable: bool = False emitted_at: datetime = Field(default_factory=lambda: datetime.now(UTC)) ``` ```python # services/api-gateway/artemis_api/main.py from __future__ import annotations import html import logging from uuid import uuid4 from fastapi import Depends, FastAPI, HTTPException, Request from fastapi.responses import JSONResponse from opentelemetry import trace from .schemas import MissionEventIn, SafeError app = FastAPI(title="ClearGlassInc Artemis API", version="1.0.0") log = logging.getLogger("artemis.api") tracer = trace.get_tracer("artemis.api") async def require_principal(request: Request) -> dict: # Production implementation validates an mTLS-bound JWT/SPIFFE identity. principal = request.headers.get("x-artemis-principal") if not principal: raise HTTPException(status_code=401, detail="authentication required") return {"subject": principal, "purpose": request.headers.get("x-purpose", "triage")} async def require_policy(principal: dict, action: str, mission_id: str) -> None: # Production implementation calls signed OPA bundles and Foundry/Gotham policy filters. if not principal["subject"] or not mission_id: raise HTTPException(status_code=403, detail="policy denied") @app.exception_handler(Exception) async def safe_exception_handler(request: Request, exc: Exception) -> JSONResponse: error_id = f"err_{uuid4().hex}" log.exception("request_failed", extra={"error_id": error_id, "path": request.url.path}) return JSONResponse(status_code=500, content=SafeError(error_id=error_id).model_dump(mode="json")) @app.post("/v1/events") async def ingest_event(event: MissionEventIn, principal: dict = Depends(require_principal)) -> dict: with tracer.start_as_current_span("events.ingest") as span: span.set_attribute("mission_id", event.mission_id) span.set_attribute("event_type", event.event_type) await require_policy(principal, "event:ingest", event.mission_id) normalized = event.model_dump(mode="json") normalized["source_system"] = html.escape(normalized["source_system"]) audit_id = f"audit_{uuid4().hex}" log.info("event_ingested", extra={"audit_id": audit_id, "event_id": event.event_id}) return {"ok": True, "event_id": event.event_id, "audit_id": audit_id} ``` ### Advanced self-improvement state machine ```python # services/self-improvement/artemis_improve/rollout.py from __future__ import annotations from dataclasses import dataclass from enum import StrEnum class ProposalState(StrEnum): DRAFT = "draft" EVALUATED = "evaluated" BLOCKED = "blocked" HUMAN_REVIEW = "human_review" APPROVED = "approved" CANARY = "canary" PROMOTED = "promoted" ROLLED_BACK = "rolled_back" ALLOWED_TRANSITIONS = { ProposalState.DRAFT: {ProposalState.EVALUATED, ProposalState.BLOCKED}, ProposalState.EVALUATED: {ProposalState.HUMAN_REVIEW, ProposalState.BLOCKED}, ProposalState.HUMAN_REVIEW: {ProposalState.APPROVED, ProposalState.BLOCKED}, ProposalState.APPROVED: {ProposalState.CANARY}, ProposalState.CANARY: {ProposalState.PROMOTED, ProposalState.ROLLED_BACK}, } @dataclass(frozen=True) class RolloutDecision: from_state: ProposalState to_state: ProposalState actor: str reason: str evidence_refs: tuple[str, ...] def transition(decision: RolloutDecision) -> ProposalState: allowed = ALLOWED_TRANSITIONS.get(decision.from_state, set()) if decision.to_state not in allowed: raise ValueError(f"invalid rollout transition: {decision.from_state} -> {decision.to_state}") if decision.to_state in {ProposalState.APPROVED, ProposalState.CANARY} and decision.actor == "system": raise PermissionError("system actor cannot approve or deploy self-upgrades") if not decision.evidence_refs: raise ValueError("rollout transition requires eval, approval, or monitoring evidence") return decision.to_state ``` ### SQL warehouse and immutable audit foundation ```sql create table if not exists artemis_audit_event ( audit_id text primary key, occurred_at timestamptz not null default now(), actor text not null, action text not null, mission_id text not null, resource_ref text not null, policy_decision text not null check (policy_decision in ('allow', 'deny', 'redact')), classification text not null check (classification in ('U', 'CUI', 'S', 'TS')), request_hash text not null, response_hash text, prior_audit_hash text, audit_hash text not null unique ); create table if not exists artemis_upgrade_proposal ( proposal_id text primary key, target_kind text not null check (target_kind in ('prompt', 'workflow', 'model_route', 'heuristic')), target_name text not null, base_version text not null, candidate_version text not null, state text not null, precision_delta numeric not null, recall_delta numeric not null, citation_accuracy_delta numeric not null, latency_p95_delta_ms integer not null, policy_violations integer not null default 0, created_by text not null, approved_by text, created_at timestamptz not null default now(), approved_at timestamptz ); ``` ### Deployment notes 1. Use Apollo release rings for every prompt, workflow, model-route, policy, API, and UI artifact: `dev -> ring-0 -> ring-1 -> mission-prod`. 2. Require signed artifacts, pinned dependency versions, SBOM generation, and environment-scoped service identities before promotion. 3. Keep production secrets only in approved runtime secret stores; local development uses safe mock defaults and fails closed for privileged operations. 4. Gate deployment on policy tests, contract tests, eval thresholds, accessibility checks for UI changes, and rollback rehearsal for mission-critical workflows. 5. Roll back automatically when policy violations exceed zero, citation accuracy regresses, p95 latency breaches SLO, drift detectors fire, or operator trust drops below threshold. ### Top 5 implementation risks and fastest mitigations | Risk | Failure mode | Fastest mitigation | |---|---|---| | Over-permissive agent tools | A tool path changes cases, access, or operational state without approval | Put every mutating tool behind a server-side policy check, explicit approval token, and regression test for denial | | Ontology permission leakage | Retrieval or UI rendering exposes coalition-restricted objects | Apply entity/row/column filters before retrieval, then run redaction-aware output validation before display | | Unsafe self-improvement | Prompt or workflow changes silently alter authority, objectives, or escalation logic | Treat all self-upgrades as proposals; require eval evidence, human approval, Apollo canary, and automatic rollback | | Telemetry data spill | Logs, traces, or eval corpora capture secrets or restricted payloads | Use structured allowlisted telemetry fields, hash payloads, redact at SDK boundaries, and scan artifacts in CI | | Latency under mission load | Agent chains exceed decision windows or time out during incidents | Add bounded queues, per-tool timeouts, cached ontology contexts, model-route fallbacks, and p95/p99 Apollo health gates |