IntentFlow: Governed LLM Agents With Auditable, Hash-Chained Traces

Last Updated on August 19, 2026 by Editorial Team Author(s): Diogo Santos Originally published on Towards AI. A small declarative language that compiles agent intent into a governed plan — and proves, after the fact, that the run stayed inside its rules. You wired up an LLM agent. It can read a GitHub issue, search the repo, draft a reply, and — because you were in a hurry — it can also post that reply and close the issue. You put the guardrails in the prompt: “Never close an issue. Always cite evidence. Ask a human if you’re unsure.” The article explains why “agent governance” implemented only through prompt wording, application code, or framework callback logic can leak under real-world pressure, and argues for an enforced, reviewable governance artifact that yields independently verifiable proof after the run. It introduces IntentFlow: a small declarative “.iflow” language that compiles an agent’s objectives, evidence requirements, action policy, verification rules, uncertainty handling, and output contract into an execution plan enforced outside the model via an ActionGate that never reads model output. Every run produces an append-only, hash-chained (optionally signed) trace,…