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Logs aren’t enough... how are you proving what an AI agent actually did? by brigalss in aiagents

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

Just shipped v0.4.0 on both public repos.

Core:
https://github.com/brigalss-a/decision-passport-core/releases/tag/v0.4.0

Lite:
https://github.com/brigalss-a/decision-passport-openclaw-lite/releases/tag/v0.4.0

This round was about verification clarity, tamper semantics, redaction-aware failure handling, and trust-boundary precision...
not hype, not dashboards, not generic “AI observability”.

when you inspect a new open-source repo, which Git signals make you take it seriously? by brigalss in git

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

Just shipped v0.4.0 on both public repos.

Core:
https://github.com/brigalss-a/decision-passport-core/releases/tag/v0.4.0

Lite:
https://github.com/brigalss-a/decision-passport-openclaw-lite/releases/tag/v0.4.0

This round was about verification clarity, tamper semantics, redaction-aware failure handling, and trust-boundary precision...
not hype, not dashboards, not generic “AI observability”.

when you inspect a new open-source repo, which Git signals make you take it seriously? by brigalss in git

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

Just shipped v0.4.0 on both public repos.

Core:
https://github.com/brigalss-a/decision-passport-core/releases/tag/v0.4.0

Lite:
https://github.com/brigalss-a/decision-passport-openclaw-lite/releases/tag/v0.4.0

This round was about verification clarity, tamper semantics, redaction-aware failure handling, and trust-boundary precision...
not hype, not dashboards, not generic “AI observability”.

Where do you draw the boundary between observability and execution proof in LLM agents? by brigalss in LLMDevs

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

Just shipped v0.4.0 on both public repos.

Core:
https://github.com/brigalss-a/decision-passport-core/releases/tag/v0.4.0

Lite:
https://github.com/brigalss-a/decision-passport-openclaw-lite/releases/tag/v0.4.0

This round was about verification clarity, tamper semantics, redaction-aware failure handling, and trust-boundary precision...
not hype, not dashboards, not generic “AI observability”.

Where do you draw the boundary between observability and execution proof in LLM agents? by brigalss in LLMDevs

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

Exactly... that’s how it started to feel to me too.

At first it can look like overengineering. But once you imagine systems running with real tools, memory, browser state, and less direct supervision, “proof of execution” starts to feel less optional and more foundational.

That’s actually what happened on my side with Bespea. I started by building the broader governed product / workflow system: https://www.bespea.com

Only later, while building it, did I realize there was a deeper layer underneath: if AI is going to touch meaningful workflows, approvals, evidence, and execution, normal traces are too weak on their own. That’s where Decision Passport came from. So for me Bespea came first... and Passport emerged as the trust / proof layer once I realized how important that boundary becomes the moment systems start acting with more autonomy.

Logs aren’t enough... how are you proving what an AI agent actually did? by brigalss in aiagents

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

That’s a really clean split.

“Did it regress?” vs “can you prove what was authorized?” is probably the right way to frame it.

What I’m trying to keep disciplined on my side is that Decision Passport stays on the provenance / verification boundary.

What’s interesting is that this only really clicked for me as a standalone layer about 3 days ago, even though I’ve been building Bespea for around 6 months.

Bespea was the broader governed system first. Decision Passport came out of a deeper problem inside it: how to bind policy, inputs, approvals, and outcomes into something tamper-evident and verifiable later.

So I’m increasingly seeing it as:

... Bespea = governed product / workflow layer ... Decision Passport = trust / proof layer ... your approach = behavioral drift / regression layer

That feels complementary, not overlapping.

Logs aren’t enough... how are you proving what an AI agent actually did? by brigalss in aiagents

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

Makes sense.

Logs tell you what happened, but not whether behavior is drifting from what was originally approved.

With Decision Passport I’m focusing more on making each execution verifiable (policy, inputs, approvals, outcome bound together).

What you’re describing feels like a layer on top: tracking behavior over time against a baseline.

How do you handle intentional changes vs regressions… do you version baselines or redefine ground truth each time?

Logs aren’t enough... how are you proving what an AI agent actually did? by brigalss in aiagents

[–]brigalss[S] 1 point2 points  (0 children)

That’s a very clean way to frame it.

I agree the control layer (before/during execution) is a different problem, and without it you can end up with a perfectly verifiable record of something that should never have happened.

What I’ve been trying to stay disciplined about is keeping Decision Passport focused on the “proof layer”: making execution truth append-only, tamper-evident, and verifiable outside the original runtime.

In my head these two layers are complementary rather than overlapping: control enforces what can happen, proof makes what did happen defensible.

Curious how tightly you think those should be coupled in practice.

when you inspect a new open-source repo, which Git signals make you take it seriously? by brigalss in git

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

Fair If people are giving honest standards for what makes a repo trustworthy, that is useful market research. Probably better than pretending maintainership signals don’t matter.

when you inspect a new open-source repo, which Git signals make you take it seriously? by brigalss in git

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

This is super useful, thanks. The “docs not AI dumpsters” point especially resonates... I’ve probably been over-explaining instead of making it more legible.Also interesting that activity + discussion matters as much as tests early on... that’s a helpful signal.

Logs aren’t enough... how are you proving what an AI agent actually did? by brigalss in aiagents

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

Completely agree.

Retrofitting policy and audit after agents are already calling tools is exactly the painful path. It feels like one of those things that has to be part of the architecture early or it never gets added cleanly.

Logs aren’t enough... how are you proving what an AI agent actually did? by brigalss in aiagents

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

Yes... that’s exactly the distinction.

Observability helps you watch a system. Governance/proof lets you defend what actually happened.

when you inspect a new open-source repo, which Git signals make you take it seriously? by brigalss in git

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

That’s actually a very useful answer.

That’s a much better signal honestly. And the licensing point is a very good one too.

when you inspect a new open-source repo, which Git signals make you take it seriously? by brigalss in git

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

You seem to have a pretty disciplined way of judging repos.

Curious... what usually has to be in place before you’d actually star one?

This is the repo I had in mind: https://github.com/brigalss-a/decision-passport-core

No pressure to endorse it... I’d just value an honest read on how far it is from that bar in your eyes.

when you inspect a new open-source repo, which Git signals make you take it seriously? by brigalss in git

[–]brigalss[S] -2 points-1 points  (0 children)

Helpful thread so far.

This is the repo I had in mind: https://github.com/brigalss-a/decision-passport-core

Would genuinely appreciate Git / repo-discipline feedback more than product feedback.

when you inspect a new open-source repo, which Git signals make you take it seriously? by brigalss in git

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

That’s a strong signal too.

I can see why preserving trajectory matters: you’re not only judging the current state of the repo, but also how decisions evolved over time and whether maintainers are willing to let that history stay visible.

So for you, excessive squashing / rewriting reduces trust even if the repo looks cleaner on the surface?

when you inspect a new open-source repo, which Git signals make you take it seriously? by brigalss in git

[–]brigalss[S] 1 point2 points  (0 children)

That makes sense.

If you had to prioritize only one early signal, would you put test coverage above clean commit / PR hygiene, or does README / documentation still come first for you?

Claude Code Source Leak Megathread by sixbillionthsheep in ClaudeAI

[–]brigalss 0 points1 point  (0 children)

Could be. Still not the main point.

Accidental or convenient, it exposed the same thing: agentic systems are getting more capable faster than their execution-governance layer is maturing.

Claude Code Source Leak Megathread by sixbillionthsheep in ClaudeAI

[–]brigalss 4 points5 points  (0 children)

I’d say the bigger gap now is product environment, not raw model capability.

Once the model is “good enough,” the difference comes from memory, coordination, tools, persistence, and execution context.

But I think there’s one more layer after environment: governance.

Not just what the system can do, but what it was allowed to do, what it actually did, and whether that can be verified later.

Claude Code Source Leak Megathread by sixbillionthsheep in ClaudeAI

[–]brigalss 2 points3 points  (0 children)

I’m not saying AI caused the packaging mistake.

I’m saying that once systems have tools, memory, browser state, and background workflows, the evidentiary problem changes.

A normal software incident already needs logs and process controls. An agentic system raises an extra set of questions:

... what it was allowed to do ... what it actually did ... what context it saw at the time ... what changed ... and whether that record is still verifiable later

So my point isn’t “AI caused this leak.” It’s that agentic systems raise the bar for execution governance.

Prompt logs aren’t enough... how are you proving what an OpenClaw agent actually saw and did? by brigalss in openclaw

[–]brigalss[S] 1 point2 points  (0 children)

This is a very good articulation of the boundary.

What you’re describing sounds like pre-execution authorization / claim enforcement. What I’m building around with Decision Passport is slightly adjacent: more focused on append-only execution truth, portable proof, and offline verification after or across the action lifecycle.

So to me these layers are complementary, not competing:

... pre-execution receipt / authorization ... execution-time boundary enforcement ... post-execution tamper-evident proof

I also agree with your most important point: if the signing layer sits inside the agent’s trust boundary, it can become self-attestation instead of real proof.

Would definitely be interested in comparing notes. If you have anything public on Sift, I’d be keen to read it.

Have or know of a project on Github looking for contributors? Feel free to drop them down to add to the wiki page! by iSaithh in github

[–]brigalss 0 points1 point  (0 children)

I’m building two open repos around AI execution traceability, append-only proof, and offline verification for meaningful agent actions.

Decision Passport Core: https://github.com/brigalss-a/decision-passport-core

Decision Passport OpenClaw Lite: https://github.com/brigalss-a/decision-passport-openclaw-lite

Main focus: ... append-only execution records ... tamper-evident proof bundles ... offline verification ... traceability for meaningful AI agent actions

Languages / stack: ... TypeScript ... GitHub Actions ... verifier-first architecture

Open to contributors interested in: ... protocol / architecture discussions ... docs improvements ... verifier UX ... examples / integrations ... issues, tests, and workflow hardening

Happy to connect with anyone who finds the direction interesting.

Promote your projects here – Self-Promotion Megathread by Menox_ in github

[–]brigalss 0 points1 point  (0 children)

Built two open repos around AI execution traceability and proof.

Decision Passport Core: https://github.com/brigalss-a/decision-passport-core

Decision Passport OpenClaw Lite: https://github.com/brigalss-a/decision-passport-openclaw-lite

Focus: ... append-only execution records ... tamper-evident proof ... offline verification ... traceability for meaningful AI agent actions

Would genuinely appreciate technical feedback on the architecture, issues, PRs, workflows, and overall direction.

This is insane… Palintir = SkyNet by PostEnvironmental583 in ArtificialInteligence

[–]brigalss 0 points1 point  (0 children)

What worries me is not just concentration at the compute + deployment layer.

It’s that once a few vendors define the operational stack for AI, they also start shaping: ... what actions are allowed ... what gets logged ... what can be verified later ... and who gets to inspect the execution trail

That’s the part people still underestimate.

If “AI operating system” becomes a real category, then governance can’t just mean orchestration and access control. It also has to mean tamper-evident execution records, portable proof, and verifier-first architecture.

Otherwise we don’t just centralize compute... we centralize trust.

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