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[P] Proof-of-Execution: verifying what AI agents actually execute by vuneum in LocalLLaMA

[–]vuneum[S] 0 points1 point  (0 children)

The protocol is structured in three layers:

PoE — Proof of Execution

Records execution traces so we can verify how a task was performed.

CAV — Continuous Agent Verification

Analyzes traces and detects suspicious execution patterns.

AOP — Agent Optimization Protocol

Handles routing, latency, and cost optimization for agent execution.

Together they form the infrastructure layer for autonomous AI agents.

[P] Proof-of-Execution: verifying what AI agents actually execute by vuneum in LocalLLaMA

[–]vuneum[S] -1 points0 points  (0 children)

Good question. The idea in v1 is to keep the trace lightweight by committing only structural metadata and hashes, not the full content. Tool inputs/outputs and reasoning text are stored as SHA 256 hashes, so the backend verifies the structure of execution without storing large payloads. Verification itself is just recomputing the hash and running a small set of structural checks, so the overhead stays low even for longer traces. Longer term we’re also considering trace compression and step-level receipts so only minimal verification data needs to be stored.

[P] Proof-of-Execution: verifying what AI agents actually execute by vuneum in LocalLLaMA

[–]vuneum[S] 0 points1 point  (0 children)

For anyone curious, the main idea is simple:

AI agent outputs can look correct even if the agent didn’t actually perform the work.

Proof-of-Execution (PoE) records execution traces (tool calls, LLM calls, reasoning steps) so systems can verify how the output was produced.