Deterministic AI Governance Runtime

Your AI Is Making Decisions Nobody Can Verify

45% of AI-generated code contains security vulnerabilities. Your agents hallucinate after turn 12. Your team spends 4.3 hours/week checking outputs. Exogram stops it.

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Founded by Richard Ewing

AI Economist

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The Numbers Nobody Wants to See

Your AI Is Bleeding Money in Production

These are not hypothetical risks. This is what's happening right now across the industry.

$67.4B

Per Year

Global business losses from AI hallucinations. Not theoretical. Measured.

88%

Failure Rate

AI agent projects fail in production. Traditional monitoring is blind to semantic failures — HTTP 200 while the agent hallucinates.

4.3 hrs/wk

Per Employee

Verifying AI outputs. That's $14,200/year per person spent babysitting a system that was supposed to save time.

19% Slower

METR Study

Experienced devs take 19% longer with AI tools. But they feel 24% faster. The productivity illusion is costing you real money.

45%

Vulnerability Rate

AI-generated code contains security vulnerabilities (GitClear). Nearly half of everything your copilot writes is a liability.

Aug 2026

EU AI Act

Full enforcement. Fines up to 7% of global turnover. If you can't prove governance, you can't operate.

The industry is using one unpredictable AI to babysit another unpredictable AI and calling it "governance."

That is stacked uncertainty. It works for chatbots. It is a disaster for production systems running enterprise software, financial infrastructure, or real-world operations.

The Mechanism

Exogram Is a Deterministic AI Governance Runtime

Not another monitoring dashboard. Not just prompts and guardrails. A verification layer that enforces correctness before output reaches production.

What Exogram Is Not

✕Another observability dashboard that shows you failures after they happen
✕A prompt-engineering wrapper that hopes the model behaves
✕An LLM-as-a-judge approach — stacking uncertainty on uncertainty
✕A "guardrail" that only catches the failures you already predicted

What Exogram Is

→A verification layer that sits between AI reasoning and production execution
→Deterministic policy enforcement — binary go/no-go decisions, not probabilistic guesses
→An auditable execution ledger that proves why every action was permitted or blocked
→Like how a compiler catches errors before runtime — Exogram catches AI failures before they reach users

When the early internet started handling real money, we had to invent SSL. AI is at that exact tipping point. Exogram is the SSL certificate for agentic execution.

Who Needs This

Built for Teams Where AI Failure Has Real Consequences

VP/Directors of Engineering

Running AI-augmented teams and can't prove to the CFO that agent output is reliable. Your team spends more time verifying AI than the AI saves.

Platform Engineers

Deploying AI agents to production and discovering that traditional monitoring is blind to semantic failures. The HTTP returns 200 while the agent hallucinates.

CISOs

45% of AI-generated code contains vulnerabilities. 67% of your employees use AI tools. Only 18% of orgs have policies. You need enforcement, not policies.

CTOs Facing EU AI Act

Full enforcement August 2026. Fines up to 7% of global turnover. Exogram provides the auditable execution ledger that proves governance to regulators.

Quantify the Damage

Stop Guessing. Start Measuring.

If you can't quantify the cost, you can't govern the system. These tools expose the hidden economics.

Hallucination Tax Calculator

Calculate exactly how much your org bleeds per year on AI verification, correction, and failure recovery.

Calculate Your Tax

AI Economics Audit

Full diagnostic on your AI unit economics. Find out if your AI tools are assets or liabilities.

Run Free Audit

Gut-Check Call

30-minute call with Richard Ewing. Bring your AI governance questions. Leave with specific answers.

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Ecosystem Presence

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Featured OnThere's An AI For That

Architecture

The Four-Layer Substrate

Exogram sits beneath the model layer. Four deterministic layers that enforce governance where probabilistic systems cannot.

Layer I: Persistent Context

State Graph

The foundational baseline that maintains identity, goals, and operational state across completely different models and platforms. Exogram unifies disconnected memory silos into a single, portable, and persistent state graph.

Layer II: Dynamic Governance

Policy Engine

The policy layer that defines the rigid operational boundaries, permission rules, and execution constraints for any given agent. Translates human intent into deterministic operational boundaries.

Layer III: Deterministic Admissibility

Execution Gateway

The runtime execution bouncer. Instead of asking if a probabilistic model can perform an action, this layer deterministically evaluates whether the execution should be allowed to occur at all, intercepting out-of-bounds actions before they hit your infrastructure.

Layer IV: The Auditable Ledger

Memory v2

An append-only, verifiable history of every action, context shift, and governance decision. It provides execution traceability, transforming passive AI memory into enterprise-grade accountability.

Execution Interception

The Execution Loop

Exogram intercepts the standard AI execution loop to inject persistence and deterministic trust.

Standard Flow — High Risk, Zero Memory

Prompt→Model→Execution→Result

Exogram Flow — Trusted, Continuous, Verifiable

Prompt→State Injection→Model→Admissibility Gateway→Ledger Log→Execution→Result

Technical Preview

The Admissibility Request

When an agent attempts to execute an action, it must pass through the Exogram Admissibility Gateway.

{
  "execution_request": {
    "agent_id": "agt_8f72c91a",
    "target_system": "aws_production_db",
    "action": "DROP_TABLE",
    "context_hash": "a1b2c3d4e5f6...",
    "exogram_admissibility": {
      "policy_check": "FAILED",
      "reason": "VIOLATES_DYNAMIC_GOVERNANCE_RULE_04:
                NO_DESTRUCTIVE_ACTIONS_IN_PROD",
      "action_permitted": false
    }
  }
}

Admissibility in Action

Intercepting probabilistic execution before it reaches production environments.

Exogram Runtime Simulator

Select Probabilistic Input

Verification Console

Waiting for payload...

Why I Built Exogram

A Note from the Founder

I'm a product guy, not a machine learning engineer. I don't have a Stanford AI lab pedigree, and I didn't set out to build deep AI infrastructure.
I built Exogram because I was trying to actually use AI to build real things, and the systems kept driving me absolutely crazy.

At first, the experience felt like magic. These frontier models are absolute miracles of cognition. They could scaffold projects and reason through complex logic at insane speeds. But the minute I tried to step back and give these agents real autonomy in my workflows, they lost their minds.

They would forget context, contradict themselves, recreate bugs they had just fixed, and hallucinate operational decisions.

I realized the entire industry is treating these unpredictable, probabilistic systems like they are reliable infrastructure. They are not. Reasoning is not infrastructure.

Exogram is my solution to that nightmare. It sits right between the AI's brain and the actual execution controls. Exogram operates across four distinct layers: persistent context, dynamic governance, deterministic admissibility, and an auditable ledger.

Instead of asking if the AI is smart enough to do a task, Exogram acts as a strict, deterministic bouncer that asks if the AI should be allowed to touch the controls at all.

Everyone is racing to make AI smarter, faster, and more autonomous.

The engineers are building brilliant brains, but almost nobody is building the safety net for when these systems actually start running our lives.

What the industry currently calls "memory" is basically just chat history. That is fundamentally inadequate. Autonomous execution requires an auditable ledger.

Right now, the industry's idea of a guardrail is just using one unpredictable AI to babysit another unpredictable AI.
That is stacked uncertainty.

That works fine if you are building a customer service chatbot. It is a total disaster if that AI is running enterprise software, financial systems, or real-world infrastructure.

The biggest problem we face:

We are giving AI the keys to the car without building the brakes. We need a definitive, verifiable way to enforce operational boundaries before these systems cause real damage.

I am genuinely terrified that we are going to lose a shared sense of reality.

AI is making it entirely too easy to generate infinite amounts of persuasive, synthetic garbage. If we do not build systems to verify what is real, what is a hallucination, and what is actually allowed to execute, the internet just becomes a massive noise machine. When that bleeds over into how physical infrastructure and human institutions operate, things get very dangerous very quickly.

That is the gap Exogram was built to address.

I want Exogram to become the absolute default layer of trust for the next era of AI. I want to build the SSL certificate for autonomous agents.

When the early internet started handling real money and sensitive data, we had to invent new security protocols to make it safe for the real world. AI is at that exact same tipping point right now.

I want to make autonomous intelligence persistent and verifiable so we can drive this technology as fast as possible without dying in the process.

Why This Matters Now

The Exogram Paradigm

As frontier models proliferate and capability converges, the strategic value shifts increasingly toward persistent operational infrastructure sitting beneath the model layer.

We are giving autonomous intelligence the keys to the car without building the brakes. Today humans repeatedly adapt themselves to disconnected AI systems. Eventually, autonomous systems will adapt themselves to persistent, auditable human context.

The future requires a persistent intelligence substrate where:

→Context survives entirely independently of the underlying model
→Governance defines rigid operational boundaries for any given agent
→Identity persists across completely different models and platforms
→The Auditable Ledger provides enterprise-grade accountability for every action

Instead of fragmented intelligence silos competing over temporary context windows, autonomous systems gain persistent operational continuity across environments.

Today humans repeatedly adapt themselves to disconnected AI systems. Eventually AI systems will adapt themselves to persistent, auditable human context.

Building in Public

Open Requests for Comment

We are building an open standard. Below are the active RFCs regarding the Exogram Protocol.

RFC-01

The Persistent Context Schema (EXO-STATE)

Goal: Define a universal JSON schema for human-to-agent context that can be injected into any orchestration layer, regardless of whether the model is OpenAI, Anthropic, or an open-source local model.

Core Challenge: Normalizing context injection so it consumes the minimal amount of tokens while maintaining 100% operational fidelity across environments.

RFC-02

Deterministic Admissibility Gateway

Goal: Establish an execution gateway that processes go/no-go decisions at sub-runtime latencies.

Core Challenge: If the admissibility check takes too long, autonomous loops break down. Probabilistic LLM-as-a-judge approaches are too slow and unreliable. This RFC proposes moving governance checks to deterministic policy engines — fast, binary rules — to safely gate API and tool calls natively.

RFC-03

The Auditable Ledger Format

Goal: Create a verifiable, append-only standard for logging AI execution history.

Core Challenge: Standard AI logs just show input (prompt) and output (response). The Exogram ledger must capture State Hash + Active Governance Policy + Attempted Action + Approval/Denial Code. This RFC designs the exact data structure required for an enterprise compliance team to trace exactly why an agent took an action.

The Stack

Exogram is the persistent intelligence substrate beneath the model layer.

Layer 1COMPUTE→ GPUs, infrastructure

Layer 2MODELS→ OpenAI, Anthropic, etc.

Layer 3PERSISTENCE & TRUST→ EXOGRAM

Layer 4APPLICATIONS→ Agents, Copilots, SaaS

Frontier models are miracles of cognition. Exogram preserves operational continuity, governance, and trust across them.

Explore Exogram

Deep Dive

Amateurs deploy AI. Professionals govern it.