Fresh drop from r/PromptEngineering: u/glaydsonboa published the runtime proof for Worion, a private system built with Claude Code, Codex and DeepSeek. Step 2 is the twist, because the proof is mostly about what the AI was not allowed to decide.
What’s new
Worion runs a single research turn and keeps a hash-checked record of everything that happened. The package has 129 files, every one verified by SHA-256. It holds turns, runtime traces, reasoning records, search events and timestamps. It is the public evidence page for Traceweave, an open source project that verifies AI agent work using evidence from outside the agent.
Think of it as a flight recorder for one question. You can open the package, pick any file, recompute its hash and compare it to the manifest. If the numbers match, the file is what it claims to be. If they don’t, you know something changed after the fact. You don’t have to trust the author’s word, and you don’t have to trust the model’s word either.
The twist
Most people use AI to automate the process. Glaydson went the other way. Deterministic scripts limit, record and prove what the AIs did. The models interpret, reason and answer. They never get to decide what counts as a fact about the execution. That job belongs to logs, hashes and traces.
A good way to picture it: the model is the witness, and the logs are the camera. A witness can be honest and still misremember. A camera doesn’t have opinions. When the two disagree, the camera wins.
The page also publishes its own failures:
- Perplexity was triggered and returned HTTP 401. The turn still finished with Gemini and Brave.
- A second planned research turn never ran.
- The full text of the first reasoning cycle was not recovered. Only its size and hash survive.
- The source commit alone cannot reproduce the run, because the worktree was dirty.
The author argues this matters as much as the wins. An evidence chain is not there to prove the system is always right. It is there to stop a story from replacing what actually happened.
Look at how the 401 played out. A tidy write-up would have said “we used three search providers” and moved on. The trace shows that one provider failed at the door and the other two carried the turn. That is a much more useful sentence for anyone who wants to run something similar, because it tells you the fallback path really worked under pressure.
The turn, step by step
- 🔍 A plain code router, no model involved, classifies the question and picks the route. In this test the author switched research mode on by hand, so the routing decision itself is visible and repeatable.
- 🧠 DeepSeek takes the question, produces its reasoning and requests searches. Those requests are logged as events with timestamps, so you can see what it asked for before you see what it got back.
- 🌐 Gemini (with Google Search) and Brave run the searches. This is where the Perplexity call fired and failed with a 401, and the trace records it instead of hiding it.
- ✍️ Results go back to the same DeepSeek, which writes the answer. The final text is tied to the search events that fed it.
That is two AIs and one search engine, not dozens of agents chatting with each other. The headline claim of 240 thousand versioned lines of code sits behind a very small runtime path. Most of that code is the scaffolding that watches, limits and records. The part that actually thinks is tiny by comparison.
Pro tips
- Log the size and hash of anything you cannot store in full. Even a partial record beats a gap you have to explain away. If the text is lost later, you can still prove a specific piece of content existed and how big it was.
- Publish the failed calls next to the successful ones. A visible 401 tells readers the log is real. Perfect logs look edited.
- Pin your commit on a clean worktree before a run you plan to cite. A dirty tree is the reason this one cannot be replayed from the commit alone. A quick check of your git status before you hit run takes ten seconds.
- Let the model write the answer, but never let it write the audit trail. If the same system produces both, the record is only as trustworthy as the model on its worst day.
- Keep your router boring. A plain code router that picks the route is easier to test than a prompt that “usually” picks right, and you can write a unit test for it in an afternoon.
- Store timestamps from your own runtime, not from the model’s output. Models are confident about time and often wrong.
The question driving all of it is simple: when an AI says it searched, ran, tested or finished something, how do you prove it happened?
Call to Action
👉 Read the runtime proof in the Traceweave repo under research/worion-runtime-e2e-2026-10-14, then look at glaydsonboa/traceweave. Next time your agent claims it did the work, ask what outside evidence backs it up. Start small: pick one agent task this week and add a hash and a timestamp to its output.
Mais de 240 mil linhas de código versionadas, 1 agente central, 2 IAs e 1 buscador no turno inteiro — publiquei a prova de runtime do Worion
by u/glaydsonboa in PromptEngineering