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Thinking about agent trust, portable verification, and cryptographic accountability in AI workflows.
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Chain of Custody for AI Agents: What Software Can Learn from Physical Evidence Handling
Physical evidence handling has solved chain of custody over centuries. There's a documented handoff at every step, every handler signs for what they received, and the integrity of the chain is what makes evidence admissible. AI agent workflows need exactly this.
Feb 15, 2026
From Subprocess to WASM: Eliminating the Subprocess Attack Surface
When your TypeScript SDK spawns a Rust binary, you've introduced a $PATH dependency, a binary substitution attack surface, and an IPC channel. All three go away when you compile to WASM.
Jan 8, 2026
Why We Chose Rust for the Trust Layer
The ZK proof ecosystem is Rust-first and Rust-only in any production-ready form. But that's not the only reason. Here's the complete case for Rust as the foundation of cryptographic agent infrastructure.
Dec 2, 2025
Privacy in Agent Workflows: Attestation Without Exposure
Attestation and privacy aren't opposites. You can prove an agent acted correctly without revealing what it acted on. Digest-based attestation, selective disclosure, and what this means for enterprise deployments.
Nov 18, 2025
Approval Nonces and Why a Single Field Prevents an Entire Attack Class
Approval reuse is subtle. An agent gets permission to perform one action, and nothing stops that approval from being used again. The nonce is the fix, and it's six characters of schema design.
Oct 7, 2025
DSSE: Dead Simple Signing Explained
DSSE is what happens when you strip JWT down to the signing part and stop trying to build auth on top of it. It's 40 lines of spec. Here's why it's the right primitive for agent attestation.
Sep 3, 2025
The Case for Portable Trust
Trust that only works inside one platform isn't trust. It's permission. What agent workflows actually need is cryptographic proof that travels with the artifact, not with the infrastructure.
Aug 5, 2025
Why Agent Actions Need Receipts
When a human takes an action, there's context: intent, memory, accountability. When an agent takes an action, there's just a log line. That asymmetry is the problem Treeship solves.
Jul 14, 2025