Autonomous agents are moving from experiments to infrastructure. We’re excited to partner with @Kassai_ai to explore how cryptographic verification can secure agent behavior. This is about making agent actions provable, not just executable: 🧵

2/ Kassai is building a distributed inference network that operates at the edge. Instead of concentrating compute in centralized data centers, Kassai activates dormant consumer GPUs to form a globally distributed, low-latency execution layer for agentic systems. This design prioritizes resilience, cost efficiency, and sovereignty.

3/ Agentic AI is fundamentally different from single-shot inference. Agents plan, call tools, invoke models, exchange messages, and trigger downstream effects across systems. When these actions are opaque, failures are invisible and accountability disappears.

4/ Lagrange addresses this gap by adding verification at execution time. Using DeepProve, agent actions can emit zero-knowledge proofs that confirm the action followed authorized logic and valid inputs, without revealing internal reasoning or sensitive data. The result is evidence, not assumption.

5/ This creates a verifiable execution pipeline for agents running across distributed infrastructure: • Decision making • Tool invocation • Model inference • State updates • External actions Each step can be independently validated by other agents, applications, or enforcement logic.

6/ In this model, trust is no longer placed in the agent or the hardware it runs on. Trust is placed in cryptographic proofs that attest to correct behavior. Kassai provides the execution fabric. Lagrange provides the proof layer that makes that execution accountable.

7/ As agentic systems begin to coordinate economic activity, governance, and real-world operations, verification becomes mandatory. Autonomy at scale requires proof. This is the direction Lagrange and Kassai are building toward.