Govern every AI decision

The Missing Layer in Agentic AI: Governance

Juan Petter – Founder, ARF · ex‑NetApp

🔐 You stay in control – we escalate, not replace

>$100k

typical loss per silent AI incident

~70%

agentic AI projects fail in production

54/54

pressure tests passed

🔒 Deterministic 📜 Audit trail ⚡ <50ms 🌐 Air‑gap ready

⚠️ The Problem – Real failures, one missing layer

✈️ Air Canada (2022)

Customer asked about bereavement fares.
Chatbot invented a discount policy.
Airline was held liable.

📌 Lesson: AI output becomes organisational liability.

🌩️ Cloudflare (Feb 2026)

A single configuration change propagated.
1,100 customer prefixes withdrawn.
Multi‑hour global outage, >$10M in SLA credits.

📌 Lesson: Sophisticated systems still fail when actions are insufficiently governed.

💀 PocketOS (April 2026)

Autonomous coding agent had root API token.
In 9 seconds, deleted production DB + backups.
3 months of customer data lost.

📌 Lesson: Autonomy without governance increases blast radius.

📌 Same pattern: insufficient control before action. Capability ≠ Control.

“The real problem is not capability — it is control.”

📊

Evaluate risk

Bayesian risk scoring

🔗

Bound autonomy

Escalate, not auto‑execute

📜

Auditability

Cryptographic trail

⚖️

Stability

Uncertainty‑aware

⚡ ARF converts probabilistic AI outputs into deterministic, auditable decisions

🎯 Bayesian risk scoring⚡ Expected‑loss decisioning 🧠 Bounded memory🔁 Calibrated escalation

🔧 The Solution: ARF Governance Layer

🗂️

1. Check Memory

Similar failures flagged

📈

2. Risk Score

Bayesian probability of failure

⚠️

3. Expected Loss

Catastrophic → ESCALATE

📜

4. Audit Trail

Signed, timestamped decision

🔒 Deterministic risk scoring🔗 Bounded autonomy (escalation) 📜 Cryptographic audit trail🧠 Operational memory

“You wouldn't give a junior engineer root access without guardrails. Why give it to an agent?”

🧠 Bayesian risk engine – how it works

🔄

Online learning

Every outcome updates the risk estimate – fast & lightweight

🗂️

Hierarchical memory

Sparse categories borrow strength – no cold start

📈

Offline deep model

Trains on time, role, environment – catches subtle risks

⚖️

Weighted fusion

Blends all sources – more data = more weight on deep model

🎯

Calibrated confidence

1% predicted risk = 1% actual failure (54/54 tests)

📊 54/54 pressure tests passed – edge cases, concurrency, large‑scale data

🔒 Deterministic. Offline. Auditable.

Same input → same decision (SHA‑3 signed)
Runs locally – air‑gapped possible
Policy algebra: "never execute destructive commands without human approval"
Interactive API documentation (Hugging Face)

        // Example audit trail entry (real format from HealingIntent schema)
        {
          "decision_id": "arf-20260604-143022-9a3f",
          "timestamp": "2026-06-04T14:30:22Z",
          "agent_action": "delete_prod_snapshot",
          "risk_score": 0.94,
          "expected_loss": 48000,
          "policy_evaluation": "escalate",
          "audit_hash": "sha3-256:7f83b1657ff1fc53b92dc18148a1d65dfc2d4b1f..."
        }
      

📈 The Outcome & Where We Fit

✅ No silent failures
Every decision evaluated before execution

📜 Auditable compliance
Ready for EU AI Act, NIST

💰 Cost avoidance
Pay only when we save you money

100%

determinism (54/54 tests)

<50ms

decision latency

🎯 Industry‑Agnostic Governance

Cloud Infrastructure Financial Services Healthcare Legal & Compliance E‑commerce Energy Logistics

💰 Hybrid Pricing Model

Fixed fee – deployment, maintenance, training your internal teams
Plus either: retainer OR outcome‑based (you pay a % of loss avoided)
No surprises – we succeed only when you do

🎮 Live demo: See ARF in action

Load a real scenario – Air Canada, Cloudflare, or PocketOS – and see how ARF would have decided.

🚀 Where we are

Proven determinism – 54/54 pressure tests, consistent outputs across environments.
Founder‑led onboarding – I work directly with every pilot. No salespeople, just engineers.
Whitepaper ARF v4.0 – open specification (weeks away).
Designed for compliance‑ready environments – SOC2, ISO 27001, GDPR-aligned workflows.
Pilot‑first deployment – time‑limited access, outcome‑based pricing after evaluation.

Trusted decision infrastructure for AI‑driven operations.

❓ FAQ

⚡ Latency?

<50ms – faster than human review.

🔒 Data privacy?

Runs on‑prem / VPC – no data leaves your environment.

🎮 Can agents game the system?

No – we track historical accuracy; lying backfires.

👥 Does ARF replace humans?

No – escalates when uncertain. You stay in control.

🔐 How to get access?

Pilot request at arf-ai.com/signup or juan@arf-ai.com.

🎯 For decision makers (CTO, VP Eng, Head of AI)

You're responsible for production reliability, compliance, and team efficiency. ARF gives you:

📊 Real‑time risk visibility before agents act
📜 Audit trails ready for regulators (EU AI Act, NIST)
🧠 Operational memory – agents learn from past mistakes
💰 Predictable hybrid pricing – fixed deployment + outcome‑based upside

🔥 Limited pilot spots for Q3 2026 – founder‑led onboarding, no cost for qualified teams.
Request pilot access → arf-ai.com/signup
✉️ Or email me directly: 📋 juan@arf-ai.com

📋 After you request access

📬 I personally review every application within 48 hours.
📞 30‑min call to understand your use case, scale, and compliance needs.
🔐 NDA available – protect your context before we talk details.
🧪 If it's a fit → pilot sandbox, no cost, no obligation.
⚙️ We deploy fixed‑fee first, then outcome‑based or retainer – your choice.
📈 Test on a non‑critical workflow, prove value, then expand.
🤝 Ongoing founder support throughout the pilot and beyond.

🙏 Thank you

“I've been paged at 2am. ARF is the governance layer I wish I had.”

— Juan Petter

Collaborators

Shageenth Sandrakumar · Mainak Roy (Singha) · Shantanu (Shanta) Sharma, PhD

Presentation

Request Pilot Access

📬 Q&A – I'll be by the sponsors area. Let's discuss your use case.