FinanceOS: Agentic AI for Finance

Command CenterMarkets Open Β· NYSE
AUM Monitored
$2.8B
Across 12 portfolios
Alpha Generated YTD
+4.2%
vs benchmark
Risk Flags Active
4
2 concentration Β· 1 VaR Β· 1 liquidity
SAR Queue
7
AML transactions flagged
πŸ€– AI Agent Status
16 financial AI agents across markets, risk, and compliance
Fraud Detection Engine2 alerts Β· reviewing
AML Transaction Monitor7 SARs queued
Market Research AgentRunning Β· 14 reports
Credit Underwriting AI8 applications
Portfolio Risk Engine4 flags Β· monitoring
Regulatory ReportingAll filings current
πŸ“‘ Live Market Intelligence Feed
Real-time AI analysis across all desks
Priority Risk Flags
πŸ” Fraud Alert β€” Card #4471
CRITICAL
3 transactions in 4 mins across 3 countries. Pattern matches card-present skimming attack. Velocity: 847% above baseline. Cardholder in London β€” transactions from Lagos, SΓ£o Paulo, Singapore.
🏦 Concentration Risk
HIGH
Tech sector exposure reached 38% of Portfolio A β€” above 35% policy limit. Top 3 holdings: NVDA, MSFT, AAPL = 24% of total. Recommend rebalance before month-end.
πŸ›‘ AML β€” Structuring Pattern
HIGH
7 cash deposits over 5 days: $8,400 Β· $8,700 Β· $9,100 Β· $9,300 Β· $8,800 Β· $9,000 Β· $8,600. Classic structuring to evade $10,000 CTR threshold. BSA officer review required within 24h.
Total Agents
16
Decisions Today
12,847
Risk Flags
4
False Positive Rate
2.1%
Risk & Compliance Agents
πŸ”
Fraud Detection Engine
Real-time transaction monitoring across all channels. Pattern matching against 200+ fraud typologies. Velocity checks, geolocation anomalies, behavioural biometrics.
Running Β· 847K txns/hr
ReAct + ML
πŸ›‘
AML Transaction Monitor
Monitors for structuring, layering, smurfing, and shell company patterns. Generates SAR narratives for BSA officer review. FinCEN 314(a) compliance automated.
Running Β· 7 SARs queued
Reflection + Rules
⚠️
Portfolio Risk Engine
Calculates VaR, CVaR, concentration risk, liquidity risk, and factor exposures across all portfolios. Monitors against policy limits in real time. Stress testing on demand.
Running Β· 4 flags
ReAct + Quant
Markets & Research Agents
πŸ”¬
Market Research Agent
Synthesises earnings calls, analyst reports, macro data, and news into actionable investment insights. Sources cited, sentiment scored, recommendations graded.
Running Β· 14 reports
ReAct + RAG
πŸ”„
Trading Signal Generator
Generates systematic trading signals from technical, fundamental, and alternative data. All signals require portfolio manager approval before execution.
Running Β· 8 signals
Reflection + Quant
πŸ’Ή
Earnings Analyser
Processes earnings calls in real time. Extracts guidance revisions, management tone shifts, and material non-public information flags. Quarter-on-quarter variance analysis.
Processing Β· Q1 season
Multi-Agent + NLP
Operations Agents
πŸ“Š
Credit Underwriting AI
Automated credit scoring for commercial and consumer lending. Analyses financials, payment history, sector risk, and covenant compliance. Recommendation with full explainability.
Running Β· 8 applications
Reflection + Explainability
πŸ“‹
Regulatory Reporting
Automates Basel III capital reporting, FINRA filing, SEC Form ADV, and CCAR stress test preparation. Deadline monitoring with automatic escalation.
Running Β· all current
Sequential + Compliance
πŸ“§
Client Reporting AI
Generates personalised portfolio performance reports, tax documents, and fund commentary. Natural language explanations of returns and risk metrics for clients.
Running Β· 284 reports
Reflection + Templates
Active Alerts
2
Immediate review
Txns Screened
847K
Last hour
Detection Rate
99.3%
False Positive Rate
2.1%
Industry avg: 8.4%
Active Fraud Alerts
πŸ” Card-Present Skimming Attack β€” Card #4471-XXXX-XXXX-3892
CRITICAL
Cardholder primary location: London, UK. Transactions detected:

19:41:02 Β· Lagos, Nigeria Β· $847.00 Β· Electronics retailer
19:43:15 Β· SΓ£o Paulo, Brazil Β· $1,200.00 Β· ATM withdrawal
19:44:47 Β· Singapore Β· $2,340.00 Β· Luxury goods
Pattern: Impossible velocity (3 countries in 3 mins). Matches card-present skimming typology. AI confidence: 0.97. Cardholder contacted β€” did not authorise. Estimated fraud loss prevented: $4,387.
🏧 Account Takeover Attempt β€” Account #8821047
HIGH
3 failed login attempts from unfamiliar IP (Russia Β· VPN detected) followed by successful login from new device. Immediate password reset + beneficiary change attempted. Beneficiary name: unfamiliar. Account frozen pending customer confirmation.
SAR Queue
7
BSA officer review
Txns Screened/Day
2.4M
High Risk Accounts
12
SAR Narratives AI
94%
Accepted without edit
Top SAR Alert β€” Structuring Pattern
πŸ›‘ Structuring / Smurfing β€” Account #CC-2847-0918
HIGH
7 cash deposits over 5 business days, all below $10,000 CTR threshold. Total: $62,100. Pattern: consistent amounts $8,400–$9,300. No legitimate business purpose identified. Shell company account opened 30 days ago.
May 12: $8,400 Β· May 13: $8,700 Β· May 14: $9,100
May 15: $9,300 Β· May 16: $8,800 Β· May 17: $9,000 Β· May 19: $8,600
Total: $62,100 Β· All: Cash, Branch counter deposits
AI-Generated SAR Narrative (draft)
πŸ“ SAR Narrative β€” Account #CC-2847-0918
AI draft Β· BSA Officer review and approval required before filing
Suspicious Activity Report β€” Draft for BSA Officer Review The reporting institution has identified suspicious activity in Account #CC-2847-0918, a business checking account opened on April 21, 2026 by Meridian Trading LLC. The account shows a pattern of structured cash deposits consistent with 31 U.S.C. Β§ 5324 (structuring to evade reporting requirements).

Between May 12 and May 19, 2026, seven cash deposits totalling $62,100 were made at various branch locations, each below the $10,000 Currency Transaction Report (CTR) threshold. The amounts ($8,400, $8,700, $9,100, $9,300, $8,800, $9,000, $8,600) suggest deliberate structuring. No corresponding business receipts or invoices were provided to justify the cash volume. The company's stated business (consulting services) is inconsistent with the cash-intensive deposit pattern...
Applications Today
8
AI Recommendation Rate
84%
Match final decision
Avg Decision Time
4m
vs 3 days manual
Explainability
100%
ECOA/FCRA compliant
πŸ“Š Credit Underwriting β€” Application #CRE-2026-0847
Meridian Manufacturing LLC Β· Commercial line of credit $2M
Credit Score
742 (Good)
DSCR
1.84x (Strong)
Debt/Equity
2.1x (Moderate)
Sector Risk
MEDIUM (Manufacturing)
βœ… AI Recommendation: APPROVE β€” with conditions
Approve $1.5M (75% of requested). Conditions: personal guarantee, monthly borrowing base certificate, Debt/Equity covenant ≀2.5x. Recommended rate: SOFR + 185bps.
AI explainability: Strengths β€” DSCR, payment history, diversified customer base. Risks β€” sector cyclicality, leverage above median peers. Full ECOA-compliant adverse action text available if declined.
πŸ”¬ Credit Analysis Process
Reflection + Explainability pattern β€” ECOA compliant by design
credit-agent Β· CRE-2026-0847
INGEST β†’ 3yr financials Β· tax returns Β· bank stmts
SCORE β†’ Credit: 742 Β· DSCR: 1.84x Β· D/E: 2.1x
SECTOR β†’ Manufacturing: medium risk Β· cyclical
REFLECT β†’ Critique: leverage above peer median
EXPLAIN β†’ ECOA reasons generated Β· adverse action
RECMD β†’ Approve $1.5M with conditions Β· 4m 12s
Reports Today
14
Sources Monitored
2,847
News Β· SEC Β· earnings
Avg Report Time
84s
vs 4 hours manual
Signal Accuracy
68%
vs 52% human baseline
πŸ”¬ Market Research β€” NVDA Earnings Analysis (Q1 FY2027)
Generated in 84 seconds from earnings call, 10-Q, analyst estimates, and news corpus
Revenue Beat
Revenue $26.0B vs $25.1B est (+3.6%). Data Centre +73% YoY. Automotive +21%. CEO tone: highly confident on H2 demand cadence.
Gross Margin
GM 76.7% vs 77.2% est (miss). Product mix shift toward lower-margin networking. New Blackwell architecture ramp impacting near-term margins.
AI Guidance Signal
Q2 revenue guide $28.0B Β±2% β€” above consensus $27.2B. Management confidence language up +12% vs Q4. Hyperscaler capex signals reinforcing.
AI Signal: POSITIVE β€” Beat on revenue, guidance above consensus, strong forward indicators. Margin miss is transient (product mix). Recommended action for PM review: maintain overweight, consider adding on any near-term weakness. All signals require portfolio manager approval before trade execution.
Total AUM
$2.8B
YTD Return
+12.4%
vs S&P +10.2%
VaR (95%, 1-day)
$8.4M
Limit Breaches
2
Concentration Β· sector
πŸ“ˆ Portfolio A β€” Performance Summary
$1.2B AUM Β· Equity Long/Short Β· YTD +14.7%
Tech (LIMIT 35%)38% β€” BREACH
Healthcare14%
Financials11%
Consumer Disc.9%
Other28%
⚠ Concentration Breach: Tech at 38% exceeds 35% policy limit. Recommend trimming NVDA/MSFT by $36M to return to compliance before month-end reporting.
⚠️ Risk Metrics Dashboard
Real-time risk calculation across all portfolios
1-Day VaR (95%)$8.4M
1-Day CVaR (Expected Shortfall)$12.1M
Beta (vs S&P 500)1.12
Sharpe Ratio (YTD)1.84
Max Drawdown (YTD)-4.2%
Liquidity Coverage (30-day)142%
Filings YTD
47
All on time
Upcoming Deadlines
3
Next 30 days
Capital Ratio (CET1)
14.2%
Min requirement: 10.5%
Exam Prep Score
94%
πŸ“‹ Regulatory Filing Calendar
May 31
FR Y-9C Consolidated Financial Statements β€” Fed Reserve
AI DRAFTING
Jun 15
Basel III Capital Adequacy Report β€” OCC
ON TRACK
Jun 30
CCAR Stress Test Submission β€” Federal Reserve
ON TRACK
Audit Entries Today
12,847
AI Decisions Logged
100%
Explainability Rate
100%
Exam-Ready
Yes
πŸ“ Complete Audit Trail
Every AI decision in FinanceOS is logged with: timestamp Β· agent identity Β· input data Β· reasoning steps Β· output Β· confidence score Β· human review status. Regulatory examination-ready. All fraud detection decisions include model explainability for Fair Lending compliance. AML decisions include full typology documentation for FinCEN examination. Immutable audit log with cryptographic signing β€” tamper-evident.
Active Signals
8
Win Rate (90-day)
61%
Avg Signal Alpha
+1.8%
Pending PM Approval
3
πŸ”„ Trading Signal Framework
FinanceOS generates systematic signals from technical, fundamental, alternative, and sentiment data. All signals are advisory only. Portfolio managers review and approve every trade before execution β€” FinanceOS never trades autonomously. Signal generation uses a Reflection pattern: generate β†’ critique β†’ calibrate β†’ present. Confidence threshold must exceed 0.65 before signal reaches PM queue. All signals include explainability narrative and historical backtested performance context.
VaR Breaches
1
Stress Loss (severe)
$84M
Liquidity Ratio
142%
Counterparty Risk
LOW
⚠️ Enterprise Risk Intelligence
The Portfolio Risk Engine runs continuous Monte Carlo simulations, factor model decomposition, and historical stress tests across all books. VaR, CVaR, DV01, and Greeks calculated in real time. Concentration limits, sector limits, single-name limits, and country limits monitored against policy. Limit breach β†’ automatic alert to CRO and risk committee. Liquidity stress testing daily. CCAR-compatible stress scenario library with 20+ macro scenarios.
Agents Active
16
Decisions/hr
847K
Compliance Events
7
Explainability
100%
πŸ“‘ Live Agent Trace
All AI decisions logged Β· SEC Β· FINRA Β· FinCEN compliant
πŸ›‘ Financial AI Governance
Why every decision must be explainable and auditable
Fair Lending (ECOA/HMDA): Every credit decision must have documented, non-discriminatory reasons. FinanceOS generates adverse action notices and fair lending justification automatically.
SR 11-7 Model Risk: Federal Reserve requires all financial models to be documented, validated, and approved before use. FinanceOS maintains model cards for every AI agent.
Market Manipulation Prevention: Trading signals never touch order management systems autonomously. Human approval layer prevents spoofing, layering, or wash trading by AI.
AgentOps β€” Live Agent Observability

πŸ“‘ Live Trace Feed

πŸ“Š Session Metrics (24h)

Total Sessions2,847
Avg Latency1.4s
P95 Latency3.1s
Error Rate0.3%
Tool Calls12,284
HITL Escalations47
RAGAS GatePASS βœ“

πŸ’° Cost & Tokens

Cost (24h)Β£847
Input Tokens48.2M
Output Tokens12.4M
Cache Hit Rate67%
Cost/SessionΒ£0.30

🎯 RAGAS Quality Scores

Faithfulness0.94 βœ“
Answer Relevance0.91 βœ“
Context Precision0.89 βœ“
Context Recall0.93 βœ“
Hallucination Rate0.8%

πŸ€– Agent Health

All agentsHealthy
OrchestratorActive
Tool registryOnline
MCP serversConnected
Memory storeHealthy
MLOps / LLMOps β€” Model Lifecycle

🧠 Model Registry

claude-sonnet-4-5 PRODUCTIONPrimary
claude-haiku-4-5 ROUTINGFast path
claude-opus-4-5 SHADOWComplex
text-embedding-3-large RAGVectors

Automatic fallback routing. Versioned in MLflow. Prompt changes require RAGAS eval gate pass.

πŸ“ˆ Drift Detection

Faithfulness drift (7d)+0.02 stable
Latency drift (7d)+120ms watch
Output length driftWithin Β±5%
Sentiment driftNo anomaly
Alert thresholdΞ”>0.05 β†’ PagerDuty

πŸ”€ A/B Experiment Controller

Prompt v2.3 vs v2.4Running
CoT vs DirectStaging

Statistical significance (p<0.05) required before promotion.

πŸͺ Feature Store

Vector IndexPinecone
Dimensions3,072
Indexed Docs284K
Retrieval P9542ms

πŸ“¦ Prompt Version Control

System promptsGit-tracked
Few-shot examplesVersioned
Eval datasetsDVC tracked
DevSecOps β€” Security-First CI/CD Pipeline

πŸš€ CI/CD Pipeline

πŸ”SAST β€” Semgrep + BanditPASS
πŸ“¦SCA β€” SBOM + TrivyPASS
πŸ§ͺUnit + Integration tests847/847
🎯RAGAS eval gate (β‰₯0.92)0.94 βœ“
πŸ”Secrets scan β€” GitleaksCLEAN
🐳Container scan β€” Grype0 CRITICAL
🚒Deploy β†’ KubernetesDEPLOYED

πŸ” Security Posture

RBAC β€” Role-based accessEnforced
API keys β€” HashiCorp VaultRotated 30d
mTLS β€” Istio service meshActive
PII scrubbing β€” NeMoActive
Audit log β€” ImmutableCloudWatch
Pen testQuarterly
SOC 2 Type IIIn progress
ISO 27001Compliant

πŸ— Infrastructure as Code

TerraformCloud infra
HelmK8s workloads
ArgoCD GitOpsSynced
Kustomize overlaysdev/stg/prd

♻️ Rollback & DR

RTO Target<15 min
RPO Target<5 min
Blue/Green DeployActive
Auto-rollbackError rate >1%

πŸ“‹ Regulatory Compliance

GDPR Art. 22 HITLEnforced
EU AI Act Art. 9Documented
NIST AI RMFMapped
ISO/IEC 42001Compliant
AI Observability β€” OpenTelemetry + Langfuse

πŸ”­ Observability Stack

L1TracesOpenTelemetry β†’ Jaeger
L2MetricsPrometheus β†’ Grafana
L3LLM TracesLangfuse (self-hosted)
L4LogsFluentd β†’ OpenSearch
L5AlertsAlertManager β†’ PagerDuty

πŸ“Š SLO Dashboard

Availability SLO99.9% target
Current (30d)99.96%
Error Budget73% remain
P50 Response0.8s
P95 Response3.1s
P99 Response7.4s

🚨 Active Alerts

Latency P95Normal
Error rate0.3% βœ“
Token budget84% remain
RAG recall0.93 βœ“
Latency drift+120ms watch

πŸ”¬ Langfuse Trace Explorer

πŸ“ˆ Avg Span Breakdown

API Gateway12ms
Auth + RBAC8ms
RAG retrieval42ms
Guardrail check18ms
LLM inference1,240ms
Tool execution84ms
Total E2E1,452ms
Guardrails β€” Responsible AI Framework

πŸ›‘ NeMo Guardrails β€” Active Rails

βœ… Human-in-the-Loop (HITL) Gate
All consequential actions require human approval before execution. Confidence <0.85 always escalates. GDPR Article 22 compliant β€” no fully automated consequential decisions.
πŸ” PII Detection & Scrubbing
Microsoft Presidio + custom patterns. Names, emails, NI/SSN, card numbers scrubbed from all LLM I/O before logging. 47 entity types across 12 jurisdictions.
🚫 Toxicity & Hallucination Filter
NeMo topic rails block off-topic responses. Factual grounding check cross-references every claim against retrieved context. Hallucination >5% triggers human review queue.
⏱ Rate Limiting & Abuse Prevention
Per-user token budgets at API gateway. 10Γ— anomalous usage triggers suspension + security alert. Cloudflare WAF DDoS protection.

πŸ“‹ Audit Trail & Explainability

πŸ“ Immutable Decision Log
Every AI recommendation logged: input context, retrieved docs, reasoning chain, confidence, model version, user ID, timestamp. 7-year retention for regulated decisions.
πŸ”Ž Explainability (XAI)
Every recommendation includes source citations, confidence intervals, alternatives considered, and limitation disclosures. SHAP attribution for structured ML models.
βš–οΈ Bias Monitoring
Fairness metrics tracked across protected characteristics. Disparate impact analysis monthly. EU AI Act Article 10 data governance requirements met.
πŸ› Regulatory Mapping
GDPR Art. 5/22 Β· EU AI Act Art. 9/10/13/14 Β· NIST AI RMF Β· ISO/IEC 42001 Β· IEEE 7001 Transparency. Compliance evidence pack generated quarterly.
0.3%
Hallucination Rate
Target <2%
100%
HITL Coverage
Consequential acts
0
PII Leaks (30d)
Target: 0
A+
Security Grade
Mozilla Observatory
Multi-Agent Architecture β€” Mesh & Orchestration

πŸ•Έ Agent Mesh Topology

Orchestrator
Agent 1
Agent 2
Agent 3
Agent 4
Agent 5
Agent 6

Orchestrator decomposes tasks, routes to specialists, aggregates results, handles conflicts. All inter-agent communication via typed schemas. No agent takes external action without Orchestrator validation.

βš™οΈ Agent Patterns

ReAct β€” Reason + Act loopsAnalytical
Reflection β€” Self-critique cyclesHigh-stakes
Planning β€” Hierarchical decompositionMulti-step
RAG β€” Retrieval-augmented genKnowledge
HITL β€” Human-in-the-loopAll consequential
Tool Use β€” Function callingAll agents

πŸ”„ Temporal.io Orchestration

Active Workflows2,847
HITL Signals Pending47
Retry PolicyExp backoff Γ—3
Saga PatternCompensating txns
Durable ExecutionCrash-safe βœ“

πŸ“¨ Kafka Message Bus

Topics47 agent topics
Throughput12K msgs/s
Consumer Lag<100ms
Schema RegistryConfluent
Dead Letter QueueMonitored

πŸ”Œ MCP Integration Layer

MCP β€” Data sourcesActive
MCP β€” CRM/ERPActive
MCP β€” Document storeActive
OAuth 2.0 authAll connectors
JSON Schema validationAll tools
Evaluation Framework β€” Continuous Quality Gates
0.94
Faithfulness
Gate β‰₯0.92 βœ“
0.91
Answer Relevance
Gate β‰₯0.88 βœ“
0.89
Context Precision
Gate β‰₯0.85 βœ“
0.93
Context Recall
Gate β‰₯0.90 βœ“

πŸ§ͺ Eval Suite Composition

Golden dataset2,847 Q&A pairs
Unit evals (per agent)120–400 cases
Integration evals84 end-to-end flows
Adversarial probes47 jailbreak tests
LLM-as-judgeclaude-opus-4-5
Human eval cadenceWeekly 5% sample

πŸ” Eval-Driven Dev Flow

1
Change proposed β†’ PR opened
Automated eval suite runs against golden dataset in CI. Results posted to PR.
2
RAGAS gate enforced
All metrics must meet thresholds. Failure blocks merge.
3
Canary deploy (5%)
Langfuse online evals on live traffic. Drift alerts trigger auto-rollback.
4
Full rollout + monitor
Weekly human eval sample. Monthly RAGAS full re-run.
Infrastructure β€” Kubernetes Β· Scale Β· Resilience

☸️ Kubernetes Cluster

ClusterEKS / GKE / AKS
Node pools3 (system Β· app Β· GPU)
HPA targetCPU 70% β†’ scale
KEDA triggersKafka consumer lag
Spot instances80% non-critical
Multi-AZ3 zones

πŸ’Ύ Data Architecture

PostgreSQL (RDS)Operational
Redis (ElastiCache)Session + cache
Pinecone / pgvectorVector search
S3 Intelligent TierDocuments
Kafka (MSK)Event streaming
Snowflake / BigQueryAnalytics DWH

πŸ’° Cost Architecture

LLM API (Anthropic)~45% of AI cost
Vector DB~12% of AI cost
Compute (K8s)~28% of AI cost
Prompt cache savingsβˆ’67% input tokens
Haiku fast-path savingβˆ’40% LLM spend
Est. monthly totalΒ£8–28K

πŸ” Disaster Recovery

1
Primary failure detected (<2 min)
Route53 health check fails β†’ DNS failover. Temporal promotes standby. Kafka MirrorMaker live.
2
DR validates (<5 min)
Smoke tests auto-run. PagerDuty alert to on-call. RTO target: 15 minutes.
3
Data reconciled (<15 min)
PostgreSQL read replica promoted. S3 cross-region lag <5min. RPO: 5 minutes.

πŸ“Š Capacity Planning

  • Baseline: 3 app nodes Β· 2 vCPU Β· 8GB RAM each
  • Scale trigger: Kafka consumer lag >10K msgs
  • Max scale: 20 nodes via KEDA + HPA
  • LLM concurrency: 50 parallel sessions managed
  • Vector search: Pinecone p1 β†’ p2 at 500K docs
  • DB connections: PgBouncer pool (max 500)
Documentation β€” Deployment Guide & Runbook

πŸš€ 10-Week Deployment Guide

1
Week 1–2: Data Foundation & Infrastructure
Deploy K8s cluster. Provision Temporal.io, Kafka, PostgreSQL, Pinecone. Connect source systems via MCP. Establish data governance and RBAC. Run baseline eval on golden dataset.
2
Week 3–4: Core Agents Live
Deploy first 3 highest-value agents. Wire HITL approval workflows in Temporal. Configure NeMo guardrails and PII scrubbing. Set up Langfuse tracing and RAGAS eval gate.
3
Week 5–7: Full Agent Mesh
Deploy all agents. Configure Orchestrator routing. A/B test prompt variants. Enable drift detection. Train end-users on HITL workflow.
4
Week 8–10: Production Hardening
Pen test + SAST/DAST scan. Load test 10Γ— baseline. Configure PagerDuty. Compliance review (GDPR, EU AI Act). Produce runbook. Go-live.

πŸ— 7-Layer Platform Stack

L7PresentationReact Β· Next.js Β· SSO
L6API GatewayFastAPI Β· OAuth2 Β· WAF
L5OrchestrationTemporal.io Β· LangGraph
L4Agent RuntimeNeMo Β· RAGAS Β· Tools
L3Model + ToolsClaude API Β· MCP servers
L2Data + IntegrationKafka Β· PostgreSQL Β· Redis
L1ObservabilityOTel Β· Langfuse Β· Grafana

πŸ”Œ Integration How-To

  • MCP server per data source (REST/GraphQL/gRPC)
  • OAuth 2.0 service account per enterprise system
  • Kafka topics per agent capability namespace
  • Schema registry for typed message contracts
  • Data lineage via OpenLineage β†’ Marquez
  • Webhooks for real-time event ingestion
  • dbt + Airflow for batch data refresh

πŸ‘€ RBAC User Roles

ViewerRead dashboards
AnalystRun queries + export
ApproverHITL decisions
ManagerConfig + agents
AdminFull platform
AI EngineerModels + prompts

IdP via Okta/Azure AD. MFA enforced for Approver+.

πŸ“ž Incident Runbook

  • High latency (>5s): Check Langfuse trace β†’ vector store β†’ LLM API status
  • RAGAS gate fail: Roll back last prompt change β†’ notify AI engineer
  • Error spike: Circuit breaker β†’ fallback to previous version
  • PII leak: Suspend session β†’ DPO notification within 24h
  • HITL queue backup: Escalate to senior approver
  • Cost overrun: Auto-throttle β†’ route to Haiku