# Agentic AI Architecture: Visual Deep Dive --- ## Current Architecture (Today) ``` ┌─────────────────────────────────────────────────────────────┐ │ MERCURY SWITCH (Elixir) │ │ /monitoring/metrics endpoint │ │ (emits iso8583, network, beam_vm, reversal metrics) │ └──────────────────┬──────────────────────────────────────────┘ │ GET /metrics │ (every 15 seconds) ↓ ┌─────────────────────────────────────────────────────────────┐ │ PROMETHEUS :9090 │ │ (time-series database, 30-day retention, 10GB storage) │ └──────────────────┬──────────────────────────────────────────┘ │ ┌────────────┼────────────┐ ↓ ↓ ↓ ┌────────┐ ┌────────┐ ┌──────────────┐ │Grafana │ │Alert │ │ Prometheus │ │:3000 │ │Manager │ │ HTTP API │ │Dashbds │ │:9093 │ └──────────────┘ └────────┘ └────────┘ ↓ ↓ Operator Email/Slack Reviews Notification Manually ↓ ↓ Operator Logs in Investigates to check manually ↓ Manual fix ↓ ✗ 5-10 minutes MTTR ``` **Problem:** Everything is sequential + manual --- ## Proposed Architecture (With Agentic AI) ``` ┌──────────────────────────────────────────────────────────────────┐ │ MERCURY SWITCH (Elixir) │ │ /monitoring/metrics endpoint │ └─────────────────┬────────────────────────────────────────────────┘ │ GET /metrics (every 15s) ↓ ┌──────────────────────────────────────────────────────────────────┐ │ PROMETHEUS :9090 │ │ (time-series database, 30-day retention) │ └─────────┬────────────────────────────┬──────────────────────────┘ │ │ ┌───┴─────────────────────────────┴───┐ │ │ ↓ ↓ ┌──────────────────┐ ┌────────────────────────────────────┐ │ Grafana :3000 │ │ AGENTIC AI LAYER (NEW!) │ │ Dashboards │ │ │ │ (visualization) │ │ ┌──────────────────────────────┐ │ └──────────────────┘ │ │ LLM Integration (Claude) │ │ │ │ • Tool orchestration │ │ ↓ │ │ • Decision reasoning │ │ ┌─────────┐ │ │ • Feedback learning │ │ │Operator │ │ └──────────────────────────────┘ │ │Reviews │ │ │ │Alerting │ │ ┌──────────────────────────────┐ │ │Dashboard│ │ │ Agent Framework │ │ └─────────┘ │ │ • State machine │ │ ↓ │ │ • Confidence thresholds │ │ Approves/ │ │ • Action logging │ │ Rejects │ │ • Rollback capability │ │ Actions │ └──────────────────────────────┘ │ │ │ │ ┌──────────────────────────────┐ │ │ │ 5 Specialized Agents │ │ │ │ │ │ │ │ 1. Transaction Performance │ │ │ │ 2. Network Resilience │ │ │ │ 3. VM Health │ │ │ │ 4. Reversal System │ │ │ │ 5. Incident Commander │ │ │ │ │ │ │ │ (observe → reason → act) │ │ │ └──────────────────────────────┘ │ │ │ │ ┌──────────────────────────────┐ │ │ │ Tool Capabilities │ │ │ │ │ │ │ │ • Prometheus queries │ │ │ │ • Mercury APIs │ │ │ │ • Docker/Service control │ │ │ │ • Config management │ │ │ └──────────────────────────────┘ │ │ │ │ ┌──────────────────────────────┐ │ │ │ Learning System │ │ │ │ │ │ │ │ • Outcome tracking │ │ │ │ • Pattern recognition │ │ │ │ • Threshold optimization │ │ │ │ • Feedback integration │ │ │ └──────────────────────────────┘ │ └────────────────────────────────────┘ │ ┌─────────────┼─────────────┐ ↓ ↓ ↓ ┌────────┐ ┌────────┐ ┌──────────┐ │Auto- │ │Request │ │Escalate │ │Execute │ │Approval│ │to Human │ │(safe) │ │(medium)│ │(complex) │ └────────┘ └────────┘ └──────────┘ ↓ ↓ ↓ IMMEDIATE 1-5 min wait PRIORITY + LOG HUMAN REVIEW ALERT ✓ 30-60 seconds MTTR ``` **Improvement:** Parallel processing + intelligent automation + learning feedback --- ## Agent Interaction Pattern ``` Real-time Metrics Stream │ ↓ ┌─────────────────────────────────────────┐ │ 1. OBSERVE PHASE │ │ All agents query latest metrics │ │ • TX Agent: iso8583_* metrics │ │ • NE Agent: network_* metrics │ │ • VM Agent: beam_* metrics │ │ • RV Agent: reversal_* metrics │ │ • Commander: All metrics │ └─────────────────────────────────────────┘ │ ↓ ┌─────────────────────────────────────────┐ │ 2. REASON PHASE (Parallel) │ │ Each agent analyzes independently │ │ │ │ TX Agent reasoning: │ │ "Latency spike detected (p99=650ms) │ │ Normal=400ms, deviation=+62.5% │ │ Historical precedent: yes (47 times) │ │ Likely cause: connection exhaustion │ │ Confidence: 89%" │ │ │ │ NE Agent reasoning: │ │ "RTT stable, no upstream issues" │ │ │ │ VM Agent reasoning: │ │ "Process count high (+25%), GC active" │ │ │ │ Result: All agents publish findings │ └─────────────────────────────────────────┘ │ ↓ ┌─────────────────────────────────────────┐ │ 3. COMMANDER ORCHESTRATION │ │ Incident Commander correlates │ │ │ │ "Multi-factor issue detected: │ │ • TX: Latency spike (89% confidence) │ │ • VM: High process count + GC │ │ • NE: Network stable │ │ │ │ Root cause hypothesis: │ │ Connection pool exhaustion + GC pause │ │ (94% match to historical pattern) │ │ │ │ Recommended action: │ │ 1. Increase connection pool (+20%) │ │ 2. Trigger GC (if not in progress) │ │ 3. Monitor for recovery │ │ │ │ Risk assessment: LOW │ │ Confidence: 92%" │ └─────────────────────────────────────────┘ │ ↓ ┌─────────────────────────────────────────┐ │ 4. ACTION DECISION GATE │ │ (Based on confidence & risk) │ │ │ │ Confidence < 60%? YES/NO │ │ │ │ │ ├─ YES → Escalate to human │ │ │ (no action taken yet) │ │ │ Operator sees: Full analysis │ │ │ + recommended action │ │ │ │ │ └─ NO → Check action risk level │ │ │ │ │ ├─ LOW-RISK (pre-approved) │ │ │ → AUTO-EXECUTE │ │ │ │ │ ├─ MEDIUM-RISK │ │ │ → REQUEST HUMAN APPROVAL │ │ │ Operator reviews in │ │ │ dashboard (takes 1-5 min) │ │ │ │ │ └─ HIGH-RISK │ │ → ALWAYS ESCALATE │ │ (Restart, DB changes) │ └─────────────────────────────────────────┘ │ ↓ ┌─────────────────────────────────────────┐ │ 5. EXECUTE PHASE │ │ (Auto or human-approved) │ │ │ │ Action 1: Call Mercury API │ │ → Increase connection pool 20% │ │ → Log: Timestamp, result │ │ │ │ Action 2: Trigger GC │ │ → Call monitoring API │ │ → Log: Result, GC duration │ │ │ │ Status: All actions logged │ │ Visibility: Operator sees in real-time│ └─────────────────────────────────────────┘ │ ↓ ┌─────────────────────────────────────────┐ │ 6. OBSERVE OUTCOME (30 seconds) │ │ Agents monitor post-action metrics │ │ │ │ TX Agent: │ │ "Latency: p99 → 410ms (was 650ms)" │ │ "Result: RECOVERED" │ │ │ │ VM Agent: │ │ "Process count: Stable" │ │ "Result: STABLE" │ │ │ │ Incident outcome: RESOLVED │ └─────────────────────────────────────────┘ │ ↓ ┌─────────────────────────────────────────┐ │ 7. LEARN PHASE │ │ Agents update internal models │ │ │ │ TX Agent learns: │ │ "Pattern [process↑ + latency↑] → │ │ increase pool by 20% works 94% of time"│ │ │ │ Commander learns: │ │ "This incident pattern → recovery │ │ in 45 seconds with +20% pool increase" │ │ │ │ Learning captured for future │ │ Feedback stored in vector DB │ └─────────────────────────────────────────┘ │ ↓ Incident resolved in 45 seconds Operator notified: "Auto-resolved: connection pool increase" Human reviews decision + outcome (satisfied) All logged for audit/compliance ``` **Cycle time:** 45-60 seconds (vs. 5-10 minutes manual) --- ## Parallel Agent Reasoning (Why It's Fast) ``` Scenario: Multiple issues at same time Timeline (with agents working in parallel): T+0s Alert detects │ (automatically triggers all agents) │ ├─ TX Agent starts: "Check latency, TPS, errors" ├─ NE Agent starts: "Check RTT, timeouts, pool" ├─ VM Agent starts: "Check memory, GC, processes" ├─ RV Agent starts: "Check queue, success rate" │ │ (all running simultaneously) │ T+3s TX Agent: "Latency↑ 650ms, likely connection issue" NE Agent: "Pool exhausted, RTT normal" VM Agent: "Memory 85%, GC 2sec pause" RV Agent: "Queue stable, no downstream issue" │ T+4s Commander receives all reports "Multi-factor: connection + memory pressure" Decides: "Increase pool + trigger GC" │ T+5s Actions executed │ T+35s Recovery observed │ T+40s Operator notified Total: 40 seconds (vs. 8 minutes if sequential) ``` --- ## Learning Feedback Loop Over Time ``` Week 1: Agent deployed (shadow mode) ├─ Decision: "Increase connection pool by 20%" ├─ Outcome: "Latency recovered ✓" ├─ Confidence: "I'll try 20% first next time" │ Week 2: Similar issue occurs ├─ Agent remembers: "20% worked last time" ├─ Decision: "Increase pool 20%" ├─ Outcome: "Recovered in 45 seconds ✓" ├─ Confidence: 92% → 93% │ Week 3: Similar issue, different time-of-day ├─ Observation: "High workload period (3pm Monday)" ├─ Decision: "Increase pool 25% (more aggressive)" ├─ Outcome: "Recovered faster ✓✓" ├─ Learning: "Monday afternoons need +25%, not +20%" ├─ Confidence: 93% → 94% │ Week 4: Agent suggests increase before latency spike ├─ Observation: "3pm Monday, workload ramping up" ├─ Observation: "Pool currently at 80% capacity" ├─ Proactive decision: "Increase pool to 120% preemptively" ├─ Outcome: "Prevented latency spike entirely ✓✓✓" ├─ Learning: "Can predict and prevent; new confidence level 96%" │ Month 2: Full autonomous mode ├─ Agent acts on pre-approved actions without human review ├─ Confidence > 95% for connection pool adjustments ├─ Human monitors dashboard for exceptions only │ Month 3: System learns seasonal patterns ├─ "Fridays = 15% higher TPS than Tuesday" ├─ "Month-end reversals spike 3x" ├─ "GC tuning needs seasonal adjustment" ├─ Agent becomes domain expert ``` **Result:** Over time, agent becomes increasingly accurate and autonomous --- ## Data Flow: Decision Transparency ``` ┌────────────────────────────────────┐ │ Agent Internal State │ │ │ │ Current observations: │ │ • Latency p99: 650ms │ │ • Process count: 450 │ │ • Memory: 85% │ │ • GC: 2.1s ago │ │ │ │ Historical context: │ │ • Normal latency: 400ms │ │ • Baseline process: 350 │ │ • Baseline memory: 70% │ │ │ │ Pattern matching: │ │ • This state matches "pool │ │ exhaustion" pattern in history │ │ • Success rate: 89% │ │ • Confidence: 92% │ │ │ │ Action selected: │ │ • increase_connection_pool(20%) │ │ • Pre-approved by policy │ │ • Risk level: LOW │ └────────────────────────────────────┘ ↓ ┌────────────────────────────────────┐ │ User-Facing Decision Card │ │ (What operator sees) │ │ │ │ ⚠️ AUTO-MITIGATED INCIDENT │ │ │ │ Time: 2024-01-15 14:32:15 │ │ Agent: Transaction Performance │ │ Action: Increased connection pool │ │ │ │ Root Cause: │ │ Connection pool exhaustion + GC │ │ │ │ Agent Confidence: 92% │ │ │ │ Actions Taken: │ │ ✓ Increased pool 20% (14:32:20) │ │ ✓ Monitored recovery (14:32:50) │ │ ✓ Verified latency recovered │ │ │ │ Metrics Before/After: │ │ • Latency: 650ms → 410ms ✓ │ │ • Error rate: 2.3% → 0.1% ✓ │ │ │ │ Resolution Time: 45 seconds │ │ │ │ [View Full Reasoning] [Approve] │ │ [Reject] [Provide Feedback] │ └────────────────────────────────────┘ ``` --- ## Escalation Funnel ``` Issues Detected: 100 │ ├─ Simple/known patterns: 60 │ └─ LOW-RISK AUTO-ACTIONS │ • Trigger GC │ • Increase connection pool │ • Adjust concurrency │ → AUTO-EXECUTE (0-5 seconds) │ → Operator notified (post-action) │ ├─ Complex/medium-confidence: 25 │ └─ MEDIUM-RISK APPROVAL-NEEDED │ • Resource adjustments │ • Service restarts │ • Config changes │ → REQUEST HUMAN APPROVAL │ → Operator reviews (1-5 min) │ → Execute if approved │ └─ Novel/high-risk: 15 └─ HIGH-RISK HUMAN-ONLY • Routing changes • Database modifications • Infrastructure changes → ESCALATE WITH FULL CONTEXT → Human decides → Execute or escalate further Result: 60% fast (no human delay) 25% medium (short human review) 15% slow (requires human expertise) ``` --- ## Summary: Why This Architecture Works 1. **Parallel Processing** — All agents observe simultaneously 2. **Specialized Expertise** — Each agent expert in one domain 3. **Coordinated Response** — Commander correlates findings 4. **Safety Gates** — Low-risk auto, medium needs approval, high escalates 5. **Fast Feedback** — Outcome monitoring captures learning 6. **Continuous Improvement** — Agents get smarter over time 7. **Human Oversight** — Humans never fully out of loop