I have been writing here about one core idea:
AI agents do not only fail because they forget things.
They fail when the things they know, the thi...
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Very interesting read. It makes me think a bit about a system I designed a few months ago, when dealing with MCP-Lite. the MCP system is fundamentally insufficient for real world execution, because the use of tools is too ambiguous. You either need too much context to make it worth while, or you need a scoring/gating system to prevent wrong-tool-for-the-job execution. What I ended up doing there, was adding a reliability score and an intent-driven graph db that looks at the success vectors. Eg. If you want to travel to a site, you use navigate, which also fetches the AOM, but if you are passing to a known site you just need to traverse, you use fast-navigate. As the system uses these tools, it adjusts the tool's success score and appends the graph db, so it can adequately discern which tool actually works for the job and how efficient it is. Then I looked into hard-gating, where any destructive tool, or input tool, must first clear a standardized test: Does the tool description match the desired outcome? Is the output format of the tool what we want? Is there other tools relevant? Is the tool sufficient for the task? Are there parameters that the tool uses that could make results better? Those tests slowed it down considerably, but worked wonders for preventing wrong tool calls. Especially when tied with a graph db to keep track of why it worked, where it worked and when it worked. Might be worth looking in to? These were ways I gated tools dynamically, so it can self-correct. (Note, these were experimental features that didnt make the cut for MCP-Lite, as I leaned more towards eventually creating a LoRA to teach the LLM how to use the tools, instead of hard-cutoffs)
This is a really good distinction, and i think you actually built two different
success vectors is an optimization layer, it learns which tool is best for the job
and gets sharper the more it runs. the hard-gate standardized test is a safety
layer. those shouldn't be merged, because a learned reliability score will happily
pass a tool that worked 99 times and is catastrophic on the 100th. the statistics
don't protect you on the tail. efficiency can be learned, safety can't be, not from
inside the actor. that's also where i'd gently push on the LoRA direction. teaching
the model to use tools better makes it more competent, but the failure i care about
isn't using the right tool wrong, it's using the right tool correctly toward a
drifted purpose. a LoRA makes the actor better, it doesn't give you a checker the
actor can't talk its way past, and the whole point of the gate is that it lives
outside the thing it governs. one thing i loved though, your standardized test, does
the description match the desired outcome, is the output format what we want,
that's a coherence check at the tool level, what it does vs what it's for. same
shape, one layer down. the slowdown is the honest cost, and i'd pay it on anything
destructive every time.
Exactly, good observation. That's the exact case, I still had to add gating for specifics, eg. when logging in with user-data, or entering banking details (features of my internal MCP-Heavy), given these are oopsies that wont just cause a loop, but can cause unauthorized purchases, or user-account alterations, that's where I pay the latency price of gating (half the reason why I expanded on V.A.L.I.D. into V.E.L.O.C.I.T.Y. which dropped rule execution down to 658ns, which is more than acceptable even for high speed tool calling (with my rust-based MCP server)
This is the right instinct exactly, you're paying the latency only where the oopsie
is irreversible, login with user data, banking details, unauthorized purchases, the
stuff that can't be un-rung. that's the same line i was chewing on another thread
here, you don't gate uniformly, you gate by whether the action can be taken back. a
loop you can just kill costs nothing, an unauthorized purchase costs everything. and
658ns basically kills the "gating is too slow" objection outright, that's noise
even at high speed tool calling, so the latency excuse for skipping the safety check
on destructive actions doesn't survive contact with your numbers. rust doing rust
things. curious whether your VELOCITY rules classify by effect type or by specific
tool, because the effect-class version is what lets you gate a tool you've never
seen before by default instead of enumerating every dangerous one.
Depends on the rule, VELOCITY, is built similar to V.A.L.I.D., so it operates much alike to CSLA, except much faster and even more generic, so you can make any kind of rule you like. I initially built it as a bank transaction infrastructure, when I finalized it, it was E2E runtime encrypted, 23 microsecond per-transaction, offline-first, high-speed queued congestion mitigating, multi-thread optimized data pipeline, that I ended up experimenting with for different use-cases. Namely, I made a Messenger (message, voice call, video call, voice notes, file transfer), Sharing (file transfer and sync), Remote desktop app, all of which perform phenomenally well, it runs so fast, I had to write a new unified file-type system just to not bottleneck it and hardware-hook for everything. Incredibly fast and extremely low overhead (0 allocation, across all of them), busy polishing the UI, before I release all 3 together, but for context, it can run sub-ms sampling for audio and video without cracking or bloating, in-memory ftp tests saturated DDR5's read/write speeds and tested data transfer rate for remote is enough to support over 600hz streaming, with less than 2ms latency addition over raw line-transfer speed. So some really cool stuff dropping soon.
This is serious work, and the part that matters most for the gating thread is buried
in your numbers, 23 microseconds, 0 allocation. when the safety layer costs that
little, skipping it stops being a performance decision and becomes a discipline
failure wearing a performance excuse. the single most common reason people skip the
gate on destructive actions is "too slow," and you just deleted that excuse at the
infrastructure level. it tracks that you built it as bank transaction infra first,
the irreversible high-stakes domain is exactly where this kind of rigor gets forced
into existence, same reason trading surfaced it for me. one thing i'm genuinely
curious about, since VELOCITY is generic rule execution, can it express a coherence
or independence check, not just per-transaction validation. meaning can a rule
recompute a value from a second disjoint source and compare it, or is it scoped to
validating the transaction in front of it. because the thread a few of us are
pulling here is that the dangerous failures aren't one bad transaction, they're two
views that should disagree and quietly don't, and a rule engine fast enough to
recompute a shadow view for free would be a real weapon against that. excited to see
the three drop.
every system that logs intent, not outcome, has this problem. trading just makes the gap visible.
This lands on me hard — we hit the exact same trap this morning, but on
the verdict-pipeline side rather than the agent side.
Our shadow→live promotion scorecard flagged ONE cell as PASS out of 78
tested (mean +0.31%, CI95 [+0.087, +0.526], n=782 over 14d).
Looked clean. Then walk-forward 7d:
last-7d +0.220% CI [-0.057, +0.498] inconclusive
prev-7d +0.362% CI [+0.080, +0.644] pass
prev² +0.170% CI [-0.111, +0.452] inconclusive
prev³ -0.473% CI [-0.715, -0.230] fail
Rotation: fail → inconclusive → pass → inconclusive. The PASS verdict
was an artifact of the 14-day window itself being a moving target —
window-shopping baked into the scorecard.
The deeper point in your post that hit hardest: "rules only matter if
the system self-interrupts BEFORE human intervention." Our pipeline
emitted PASS. A human (me) had to manually run the walk-forward to
catch it. If the operator hadn't asked me to look at the scorecard
today, that PASS would have drifted unchallenged for a week. We didn't
get saved by process — we got lucky on attention.
Two things we're adding because of your post:
"A written protocol is not agency." Going on a sticker.
This is a cleaner demonstration of the trap than the one i wrote. the rotation, fail
to inconclusive to pass to inconclusive, is the whole thing in one line, the pass
was a property of the window, not the strategy. and the sentence i can't stop
thinking about is yours, "we didn't get saved by process, we got lucky on
attention." that's the real state of almost every system that thinks it
self-corrects. on your two fixes, the persistence gate is right because it pulls the
check into the verdict instead of into your attention. but watch one thing, "2
consecutive windows" is itself a threshold you chose, and if you picked 2 after
seeing the rotation, that's window-shopping moved up a level. pre-register why 2 and
not 3 before you calibrate, or it's the same tautology in a longer coat. and on the
frozen manifest, the hash gives you integrity but not anteriority. it proves the
file wasn't changed, not that it existed before you saw the thresholds, unless the
freeze is timestamped somewhere you can't quietly re-roll, a public commit pushed
before calibration, an external timestamp. if the manifest and the calibration live
in the same hand, you can always re-freeze and call it always-thus. the anchor has
to be somewhere you can't reach back into. and "a written protocol is not agency"
going on a sticker made my day, put it where you'll see it at 2am.
Both critiques land. "Window-shopping moved up a level" is the right name for picking 2 after seeing the rotation — pre-registration is the only fix that doesn't quietly reintroduce the bug, and it has to be a public commit before calibration, not a private note. Same for the hash: integrity ≠ anteriority, and the anchor has to be somewhere I can't reach back into. A pre-calibration git tag pushed to a public remote, or an external timestamp service, is the cheap version. Anything that stays in my own hand is just a re-freezable manifest in a longer coat. Adopting both. "Tautology in a longer coat" is going on the same sticker.
That's the move, and the git tag pushed to a public remote before calibration is the
cheap version that actually holds, because the timestamp lives with a party you
can't lean on later. funny how the whole discipline keeps reducing to the same
thing, put the proof somewhere your future self can't quietly edit. glad it was
useful, you sharpened it as much as i did.
"Put the proof somewhere your future self can't quietly edit" is the through-line of every version of this we've both kept circling. Mutual sharpening is the right read; thanks for the rounds. Going on the same sticker as the rest.
Great read! I like how you highlighted the difference between generating signals and achieving real-world trading outcomes. It's a valuable reminder that AI can provide insights, but market success still depends on strategy, risk management, and execution. Thanks for sharing such a practical perspective on applying AI to trading.
Thank you, and you put your finger on the exact line i was trying to draw. the part
most people skip is where the agent should actually live. it's tempting to point it
at generating the signal, but the signal is the easy, crowded part. the durable
place for it is the risk management and execution you named, enforcing the
discipline around the trade, because that's the part humans break under emotion and
a system doesn't. appreciate you reading it closely.
The cross-layer coherence framing is the useful part for me, because most agent failures I look at aren't single-layer bugs, they're the four layers quietly disagreeing while each one looks fine in isolation. Putting it next to a brokerage account is a sharp move since the read-vs-act boundary stops being abstract the moment an order has a dollar value. How are you checking that the "leave receipts" step itself stays honest when the model is the thing writing the receipt?
This is the question, and the answer is the model can't be the thing writing the
receipt. if the model narrates what happened, the receipt is just the bribeable
checker again, it'll write "allowed, looked fine" because that's the path of least
resistance. so the receipt isn't authored, it's emitted by the deterministic gate as
a byproduct of the check. it records what the gate computed, which tool was called,
which frozen rule allowed or refused it, the input and output hashes, not what the
model says it did. the model's narration can ride along as a field, but it's never
the load-bearing part and it's labeled as narration, not fact. the test i'd hold it
to, can you verify the receipt without trusting the thing that produced the action.
if the answer needs the model to be honest, it's not a receipt, it's a confession
you're choosing to believe.
The part that stuck with me is the gap between the code catching a bad number and you catching a bad story. Freezing the validation universe before the run is the move that actually killed the fake edge, because the survivorship bias only shows up once the winners can't sneak back into the sample. The honest admission at the end is the rare thing these write-ups skip: the rules only worked because you were standing there to enforce them. A protocol the system follows only after a human points at the failure really is just a doc, and naming that out loud is worth more than another green checkmark.
You pulled out the exact line i care about most, code catching a bad number vs me
catching a bad story. those are two different gates and only one of them is solved.
the bad number is typed, you can check it deterministically, did the test pass, is
the value in range. the bad story isn't typed, "we're close, this is the milestone"
is a narrative overclaim and i don't have a clean deterministic gate for that yet,
which is exactly why i was the one who had to catch it. that's the honest frontier
under the admission you flagged. a protocol becomes agency the day it interrupts the
bad story before a human points at it, and the brutal part is the human is the
cheapest enforcer in the room, so there's never pressure to replace yourself until
you're asleep or gone and the story runs unchallenged. naming it was the point,
you're right, but i want to be honest that naming it isn't fixing it. the number
gate is real. the story gate is still mostly me.
Receipts are not outcomes is the cleanest way to name what most agent evals miss. A receipt proves the action ran; it says nothing about whether running it produced the result you wanted, and trading is the domain where that gap is brutal, because a perfectly executed order can still be the wrong order. An AI gate that checks did the step complete is measuring receipts; the gate that matters checks did the state move toward the goal, which needs a definition of the goal the agent didn't author. Most reliability tooling stops at the receipt because it's easy to verify and the outcome is hard. The discipline is resisting the easy proxy: gate on the outcome an external rule can confirm, not the receipt the agent can always produce.
you took it exactly where i wanted it to go, the goal definition the agent didn't
author is the whole crux. and trading is the cruelest version because the outcome is
slow and noisy, a perfectly executed order can look right for a week and be wrong,
or be right for the wrong reason. that's exactly why people gate on the receipt, the
receipt is available in milliseconds and the outcome takes days, so they measure
what's under the streetlight. the discipline you named, resisting the easy proxy, is
really resisting the fast proxy. you have to be willing to hold the verdict open
until the outcome an external rule can confirm actually arrives, even when a clean
receipt is sitting right there tempting you to call it done. the receipt tells you
the machine ran. it can't tell you the machine did the right thing, and in trading
those come apart constantly.