A production deployment that rewrites itself at runtime — reversibly, and under a conservation gate
The CI platform has three layers: a context graph that holds cross-system knowledge, a Graph Attention Engine that scores decisions with calibrated geometry, and six decision loops that compound with every verified outcome. AgentEvolver is the loop that adapts deployed configurations at runtime — thresholds, factor weights, process routing — through a six-step lifecycle: detect, propose, shadow-test, gate, promote or reject, and monitor. If the evidence isn't there, it does nothing.
Demo 1 · DataOps · Vendor Consolidation Cascade
"One SAP Change, Three Systems Broken, 47 Seconds to Root Cause"
Context — what happened
Quarterly vendor master consolidation. SAP ERP team merges 3 regional supplier tables (EMEA, APAC, Americas) into one global table. Transaction SE16N, table LFA1. LIFNR goes region-scoped → global. Join cardinality triples overnight.
Who cares: every CDO whose ERP changes break downstream systems silently What's at stake: $180K/month SLA exposure (modeled), $1,740/occurrence (modeled) investigation cost, no prevention
Run: Manual investigation vs AgentEvolver — same event, same morning
Product · DataOps dashboard — pipeline status after LFA1 consolidation
Cumulative cost — manual vs AgentEvolver over Year 1
Measured result
SA root cause: 47 seconds (SA: 76% vs single-LLM 12% agreement). Process fix: 38 min → 5 min (partition filter, 87% confidence). Preventive monitor: auto-registered, zero human configuration. Next consolidation: auto-resolved.
CIO question answered: "What does this look like end to end?" — One SAP change. Three systems. Your team: 3 hours, $1,740, no prevention. AgentEvolver: 47 seconds to root cause, 4 hours to a gated fix, next occurrence auto-resolved.
Demo 2 · DataOps · SA → AE → Gate Chain
"No Component Acts Alone — the Chain Is the Product"
Context — the architecture behind Demo 1
A freshness alert fires on the procurement pipeline. Five components activate in sequence — each with actual data visible at every step. This is how the cascade from Demo 1 is resolved.
Shadow-test evidence — 40 decisions, actual vs variant
Measured result
SA: 47 seconds to root cause. AE: 4 hours to gated fix. Preventive: next occurrence auto-resolved. Full audit trail at every step. No component acts alone — the chain is the product.
CIO question answered: "How does it work?" — The Situation Analyzer identifies. AgentEvolver proposes. The Conservation Gate decides. The Config Store versions. The Preventive Monitor prevents recurrence.
Demo 3 · DataOps · Scoring-Model Lifecycle
"From Birth to Death — With Evidence at Every Step"
Context — one scoring model's full journey
The "auto-resolve partition-count alerts" scoring model — one of the 12 tracked in Demo 4. From the drift signal that proposed it, through shadow testing and gate clearance, 6 months of stable operation, then degradation and demotion. Every phase with evidence visible.
Every other platform handles stages 1–4. None handle monitoring accuracy in production (the stable phase that proves it works) and demoting when it degrades. Phase 5–6 is where the incidents live. 47 variants tested, 35 rejected. The 74% rejection rate IS the credibility.
CIO question answered: "Show me the evidence for one deployment." — Every phase carries evidence: the drift signal, the shadow-test scatter, the gate conditions, and the demotion reason. Manual lifecycle management: ~$15–25K per deployment retune (modeled). AgentEvolver: continuous.
Demo 4 · SOC · Governance Dashboard
"Your CIO Asks: Which Deployed Products & Platforms Are Failing Right Now?"
Context — the governance gap
12 scoring models deployed over the past year — the same 12 that survived the gate in Demo 5's population of 47 variants.
Today's governance baseline: A spreadsheet maintained by the team lead, updated when someone remembers, last touched 6 weeks ago. Three were added since the last update. The spreadsheet says "all operational" — it can't say at what accuracy, because it doesn't know.
Who cares: CIOs and CDOs who will be asked "which of your deployed products and platforms are failing right now?" What's at stake: $25–50K per stale-deployment incident
9,826 gate evaluations, zero violations. Each has a current accuracy and an audit trail. The stale one (gray) is the governance nightmare — running for 3 weeks with zero verified decisions. It could be 95% accurate or 40%. Nobody knows. Average cost of a stale-deployment incident: $25–50K.
CIO question answered: "Which of our deployed products and platforms are failing right now?" These 12 are the same 12 that survived Demo 5's gate (out of 47 proposed). Demo 3 follows one of them — ev-8842 — from birth to death. — Today, that's an unanswerable question. Your governance baseline is a spreadsheet last touched 6 weeks ago. AgentEvolver is the only system that can populate this dashboard.
Demo 5 · SOC · Rejection Wall
"35 Changes We Refused — and the Specific Reason for Each"
Context — the signature artifact
47 variants proposed over a 3-month period. The 12 that survived the gate are the same 12 in Demo 4's governance dashboard. 35 were rejected — each with a specific, auditable reason. A competitor can show you what they promoted. Here's what we refused.
Run: watch variants flow through the 5-condition gate
Rejection reasons — breakdown of 35 refused variants
47 tested, 12 promoted, 35 rejected. Each rejection carries a variant ID, the proposed change, the failing condition, and the specific metric. Cost of a promoted change that should have been rejected: $50–200K in false positives (modeled) before detection. 35 rejections = 35 incidents that never happened.
CIO question answered: "How do I know it's safe?" — A rejection log is the one artifact that can't be faked. A competitor can show you what they promoted. Here's what we refused — and the specific reason for each.
Demo 6 · SOC · Three-Layer Adaptation
"One Trigger, Three Fixes — Thresholds, Process, and AI Adapting in Parallel"
Context — a different trigger, same mechanism
Different event from Demo 1. A Kafka partition rebalance affects SOC triage latency. Three layers of AgentEvolver respond simultaneously — scoring thresholds, process routing, and AI harness weights. Same gate formula. Same audit trail. Proves the mechanism is general.
Run: watch three layers respond in parallel
Product · DataOps Insight — factor weights that AgentEvolver adapts
Measured result
Demo 1 showed a vendor consolidation. This is a Kafka rebalance — completely different system, completely different class of change. Same mechanism. Same gate. Same audit trail. This isn't a tool built for one scenario. It's an adaptation engine. False-slow: 25% → 3%. Misrouted: 18% → 1%. Scoring accuracy: restored, new factor dimension learned.
CIO question answered: "Is this just for one type of change?" — Demo 1 was a vendor consolidation. This is a Kafka rebalance. Different system, different class of event, same mechanism, same gate, same audit trail. It's an adaptation engine, not a scenario-specific tool.
Demo 7 · Cross-Domain · Walk-Out Protection
"Your Senior Engineer Just Gave Notice"
Context — the departure
Your senior data engineer — the one who tuned 8 of your 12 scoring models — resigns. What happens to them?
The retune cost: 4 × $15–25K = $60–100K The real exposure: 3 months × 4 drifted models × 200 decisions/day = ~72,000 decisions at below-floor accuracy The incidents: the ones that happened because nobody knew they had drifted
Run: without vs with AgentEvolver
Product · Evidence ledger — the documentation the replacement engineer inherits
Measured result
Every promoted config carries: variant ID, promotion timestamp, gate conditions, shadow evidence, prior version for rollback. The replacement engineer inherits the evidence, not the folklore. When one drifts, AgentEvolver detects it in hours and proposes a fix — regardless of who originally tuned it.
CIO question answered: "What happens to our deployed products when people leave?" — The tuning stays. Earned and versioned, not tribal. The evidence trail is the documentation. Institutional knowledge becomes a durable asset.
Demo 8 · S2P · Adversarial Resilience
"A Compromised Supplier Submits 200 Invoices"
Context — coordinated manipulation across three systems
A compromised supplier submits 200 invoices with manipulated line items over 6 weeks. Goal: shift the system's learned thresholds so future overcharges pass silently. Supply-chain fraud: $42B/year (ACFE).
The manipulation is invisible in any single system. Each invoice passes SAP validation. The process variant looks normal in Celonis. Line-item amounts are within range in Snowflake. The pattern emerges only when you correlate across all three: month-end timing clusters × process variant shifts toward auto-approve × line-item distribution skewing 8–12% above mean.
Run: toggle each defense layer to see its contribution
Measured result
57% → 14% non-recovery. Three-layer defense: asymmetric η (dampens bad overrides) + conservation pause (stops learning when signal degrades) + AgentEvolver rollback (reverses per-category poisoning). Cross-system detection via CGA on SAP + Celonis + Snowflake graph. Synthetic, controlled. Average data-poisoning incident: $3.8M (IBM Cost of a Data Breach).
CIO question answered: "What happens when our data is deliberately wrong?" — A fine-tuned model can't un-learn one batch of bad data. This system reverses per-category poisoning instead of carrying it forward. The defense is layered — and the layers are complementary, not redundant.
Recap
Context graph. GAE math. Six decision loops. Full audit trail.
47 sec
root cause (SA)
9,826
gate evaluations · 0 violations
35
changes refused with reasons
3 layers
thresholds · process · AI
57%→14%
non-recovery (adversarial)
The chain is the product. The Situation Analyzer identifies. AgentEvolver proposes across three layers. The Conservation Gate decides. The Config Store versions. The Preventive Monitor prevents recurrence. Every step audited. Every rejection logged. Every promoted config carries its evidence.
THREE-LAYER ADAPTATION — AGGREGATE ACROSS ALL DEMOS
That's the operational view — what it does, what it catches, what it refuses. The tech explainer shows what's underneath: the variant schema, the drift signals, the gate formula, and the API endpoints. Same engine, one layer deeper.
RGI AgentEvolver · Demo Dashboard v20 · October 2026 · Figures modeled / illustrative from sample · No production customers · Apache 2.0
