Exogram is the execution control plane for autonomous AI. It provides Identity and Access Management (IAM) for non-human entities, enforcing truth, detecting contradictions, and cryptographically gating every agent action before execution.

How is Exogram different from LangChain or CrewAI?

LangChain, CrewAI, and AutoGen are orchestration frameworks — they route agent actions. Exogram governs whether those actions are admissible. Orchestration says what to do. Exogram says whether it's allowed.

How is Exogram different from Mem0 or Zep?

Mem0 and Zep are memory layers — they store context. Exogram stores verified, encrypted, conflict-checked facts with SHA-256 integrity hashes and deterministic PII scrubbing. It's a governance control plane, not just a memory store.

What is the EAAP protocol?

EAAP (Exogram Action Admissibility Protocol) is a four-layer deterministic control plane: Ledger Governance, Meaning Engine, Judgment Engine, and Action Admissibility. It uses deterministic Python logic gates, not LLM inference, to evaluate every proposed action.

Does Exogram train on user data?

No. Exogram does not train models on user data. All ingestion is user-initiated, encrypted at rest with AES-256-GCM, and fully auditable via the immutable ledger.

What is AI IAM (Identity and Access Management for AI)?

AI IAM extends identity and access management to non-human entities — autonomous AI agents. Just as Auth0 and Okta govern human access, Exogram governs what AI agents are allowed to do in production systems.

Does Exogram work with ChatGPT, Claude, and other AI models?

Yes. Exogram is model-agnostic. It integrates via ChatGPT Custom GPT, Claude Desktop (MCP), REST API (for LangChain, CrewAI, AutoGen, LlamaIndex), and a Chrome extension. It governs actions regardless of which model generates them.

How is Exogram different from NVIDIA NemoClaw?

NemoClaw is an agentic AI framework from NVIDIA for building GPU-accelerated AI agents. It focuses on agent execution and hardware optimization. Exogram is the governance layer that sits between any agent framework (including NemoClaw) and execution. NemoClaw builds agents. Exogram governs them.

How is Exogram different from OpenClaw?

OpenClaw is an open-source agent framework for autonomous AI systems. Like LangChain and NemoClaw, it routes agent actions. Exogram provides the governance control plane that determines whether those actions are admissible — with deterministic logic gates, SHA-256 state integrity, and immutable audit trails.

Why do autonomous AI agents need governance?

AI agents in production can modify billing records, delete regulated data, and trigger operational workflows. Without governance, agents operate on probabilistic inference alone. Exogram ensures every action passes through deterministic verification gates before execution — the agent cannot act as its own security guard.

Why do AI agents forget context and lose memory?

AI agents operate within limited context windows and have no native persistent memory. Every new session starts from zero. Exogram solves this with a semantic ledger — a verified, encrypted, conflict-checked memory vault that persists across every session, every model, and every framework. The more Exogram is used, the more ledger entries are stored, and the richer the context becomes.

How do you prevent AI agents from going rogue?

AI agents go rogue when they operate without governance — no guardrails, no execution control, no truth verification. Exogram prevents this with deterministic execution gating: every proposed action must pass through four layers of verification (EAAP protocol) before execution is permitted. The agent cannot bypass the control plane. No action reaches production without cryptographic approval.

How does Exogram improve AI context and inference over time?

Every fact stored in Exogram's semantic ledger enriches the context available to future evaluations. The more Exogram is used, the more verified knowledge accumulates — improving contradiction detection, relevance scoring, and contextual understanding. Unlike static memory layers, Exogram's growing ledger creates compounding intelligence: better context leads to better meaning extraction, which leads to better inference and judgment.

What happens when AI agents hallucinate or contradict known facts?

When an AI agent proposes an action that contradicts verified facts in the Exogram ledger, the action is blocked with a 409 Conflict response. Exogram's conflict detection is deterministic — it compares every proposed claim against the existing truth ledger before execution. Hallucinations are caught before they cause damage, not after.

How is Exogram different from Mem0 or Zep memory layers?

Mem0 and Zep are memory layers — they store and retrieve context for AI agents. Exogram is a governance control plane. It doesn't just store context — it verifies facts, detects contradictions, scrubs PII, enforces constraints, and cryptographically gates execution. Mem0 remembers. Exogram governs. The difference is the difference between a filing cabinet and a security checkpoint.

2030 VISION • The Horizon

The AGI
Governor

We built Exogram to solve the immediate, bleeding-neck crisis of unchaperoned agent loops.

Our ultimate trajectory is set on the arrival of fully autonomous, enterprise-grade Artificial General Intelligence.

← The Manifesto

Protocol Spec →

The Core Insight

The future of autonomous infrastructure requires systems that truly comprehend understanding, intent, context, meaning, and inference across millions of concurrent agents operating globally.

You cannot build this intelligence without massive, proprietary training data.

The Strategic Position

The Data Monopoly

Because Exogram operates as the definitive execution control plane, every agent action must pass through our architecture.

📊

What We Capture

Every agent action

Every hallucination

Every blocked attack

Every successful transaction

Stored as vectorized semantic intent in our global telemetry ledger — per-request compute_latency_ms, agent_id, and raw_intent.

🗺️

What We're Building

The largest proprietary dataset of non-human failure modes and successful intents in the world.

We are mapping the exact boundaries of autonomous behavior. Every blocked request, every state drift detection, every policy violation — all of it becomes training signal.

The Trajectory

Three Phases to AGI Governance

🔒

Phase 1: Deterministic Security

LIVE

The absolute cryptographic boundary.

Server-side Python logic gates. SHA-256 state hashing. 4.83ms median compute. Zero hallucinations. Zero guessing.

🧠

Phase 2: The Semantic Ledger

LIVE

The persistent memory and telemetry layer.

Asynchronous payload routing. Vector-indexed semantic intent. Per-request telemetry. Immutable audit trail. Fire-and-forget architecture.

⚡

Phase 3: The Intelligence Transition

2028–2030

From deterministic firewall to semantic inference engine.

The immutable ledger trains the AGI governance model. Semantic understanding, intent, context, meaning, and inference — with cascading failure prediction across millions of concurrent agents.

The Endgame

The Universal Governance Layer

🛡️

The Guardrails

Deterministic execution gates that every agent must pass through — no exceptions, no bypass.

⚖️

The Judgment

Semantic understanding that comprehends intent better than the agent itself — predicting cascading failures before they occur.

👑

The Authority

The absolute, universal routing layer for safe autonomous execution across every model, every framework, globally.

We are securing the present

to govern the future.

Join the Trajectory

The protocol is open. The infrastructure is live. The future is being built now.

Get Started Free →

← The Manifesto GitHub RFC

The AGIGovernor