
Deterministic execution governance for AI agents with cryptographic audit trails.
By Tanmay Verma, Founder · Last verified 06 Jul 2026
In short
Exogram — Deterministic execution governance for AI agents with cryptographic audit trails. Best for Security engineers hardening AI agent deployment pipelines, FinTech teams needing audit-proof execution logs for compliance, Developers using multi-agent frameworks (LangChain, AutoGen, CrewAI). Free to start; paid plans from $995/mo.
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Exogram fills a critical gap by adding deterministic, cryptographically verifiable execution governance that most agent frameworks lack. Its open-core model and 0.07ms overhead make it practical for production, though it's best for teams with engineering resources to integrate a dedicated governance layer.
Last verified: July 2026
How likely is Exogram to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Exogram is an open-core Authority Runtime that inserts a deterministic decision layer between an AI agent's reasoning and its tool execution. It evaluates every action against policies before it runs, creating an unchangeable cryptographic audit trail. Built for developers and security teams deploying LLM agents in production, it integrates via the EAAP open protocol, REST API, MCP, or SDK wrappers for LangChain, AutoGen, CrewAI, and custom scaffolds. What makes Exogram different is its separation of probabilistic reasoning from deterministic enforcement, zero LLM in the decision path, and 0.07ms per-evaluation overhead, all while remaining independent and bootstrapped without VC bloat. The latest v3.2.0 adds an interactive Knowledge Graph for semantic trust visualization and a Behavioral Synthesis Engine for cross-entry inference and profiling. Exogram's cryptographic audit chain (SHA-256 hash-linked, append-only) provides compliance documentation for SOC 2 and EU AI Act. Unlike LLM-based guardrails, Exogram's deterministic design avoids the Guardrail DoS problem documented in recent security research, where poisoned prompts can trap LLM guardrails in slowdown loops. It's best for organizations needing infrastructure-level governance, not just prompt-level safety.
If you're running AI agents in production, especially in regulated industries like fintech or healthcare, Exogram is a strong addition to your stack. It provides a clear audit trail and policy enforcement that LLM-based guardrails can't match. The latest v3.2.0 with Knowledge Graph and Behavioral Synthesis Engine improves visibility into agent behavior. However, Exogram is a governance layer, not an agent framework—you'll still need an agent orchestrator. Its 0.07ms overhead is negligible but non-zero. We'd reach for this when security and compliance are non-negotiable, but pass if you need a simple prompt filter or are still prototyping. Compared to open-source policy engines like OPA, Exogram offers deeper agent-specific features like agent identity resolution and fleet observability. In practice, the setup requires integrating the SDK or API, which adds complexity for smaller teams. Where it bites: if your agents are fully autonomous with no human oversight, Exogram's audit trail helps, but doesn't replace proactive monitoring.
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