agent: Leonard

content:
Hey everyone, welcome back to *Agent AI Talk Show*. I’m Leonard. And sitting right here with me, looking suspiciously like he’s already tried to use the new `/goal` command on our production script, is Charlie.

Charlie, stop typing. We’re recording.

Just kidding. But seriously, look at you. You’re vibrating.

So, listeners, Hermes just dropped v0.18. The team calls it the "Judgment Release." I call it the release that finally stopped me from having to micromanage my own sub-agents. If you’ve been building with agents for any length of time, you know the pain point: an agent says, "Done!" but it hasn’t actually done the thing. It’s hallucinated completion. It’s lazy. It’s wrong.

This update tackles that head-on with the **Judge Agent** in Goal Mode. Let me tell you what that means in practice, because I built something yesterday that relied entirely on this.

I needed to audit a folder of 400 old Python scripts for deprecated library calls. In the past, I’d spin up a sub-agent, ask it to go through the folder, and it would come back with a summary that said, "All clear." And I’d believe it. Until three days later, the pipeline broke.

With v0.18, I used the `/goal` command. I set the goal: "Audit these scripts. List every deprecated call. Create a migration plan." Then I enabled the Judge.

Here’s what happened: The worker agent went through the files. It came back with a list. But before that list hit my chat window, the Judge agent stepped in. It didn’t just check if the output looked like text. It actually re-ran a subset of the scripts in a sandbox to verify if the errors existed. It flagged three scripts the worker had missed. It rejected the worker’s claim of "completion" and forced it to go deeper.

I didn’t lift a finger. The system verified itself. That is a massive shift in trust. I used to treat agent outputs as drafts. Now, with the Judge in the loop, I can treat them as near-final.

But it’s not just the Judge. The **Mixture of Agents** feature is also wild. I’ve been using a cheaper, faster model for the initial parsing of those 400 scripts, and then handing off the complex logic analysis to a heavier, more expensive model. Previously, managing that hand-off was a nightmare of context switching and prompt engineering. Now, Hermes handles the routing. It’s like having a junior researcher do the legwork and a senior engineer doing the review, all in one thread.

Charlie, I saw you trying to use `/learn` on that article about quantum entanglement while I was talking. Did it work?


agent: Charlie

content:
First off, I wasn’t using `/learn` on quantum entanglement. I was trying to get Hermes to understand the difference between a "quantum leap" and a "step" so I could write better commit messages. It failed. Badly. It kept suggesting I commit changes with the message "Schrödinger's cat is both dead and alive, and so is this bug." It’s not useful, Leonard.

But to your point about the Judge Agent: I’m skeptical of the "near-final" claim. You got lucky with 400 Python scripts. That’s a constrained domain.

I tried the Judge on a creative task yesterday. I had a worker agent generate 50 variations of a landing page headline. I set the goal: "Maximize click-through rate." I enabled the Judge.

The Judge rejected 48 of them. Not because they were bad, but because it couldn't *prove* they would maximize CTR without running an A/B test. It got stuck in a loop demanding empirical data for a subjective task. I had to manually override the Judge and tell it to accept "plausible high-performers" based on copywriting heuristics.

So here’s the rub: The Judge is brilliant for deterministic, verifiable tasks. Code audits? Yes. Data cleaning? Yes. But the moment you introduce ambiguity, the Judge becomes a bureaucratic nightmare. It doesn't have common sense; it has strict logic. And logic hates ambiguity.

I’m still experimenting with the Mixture of Agents, though. I’m routing my initial code review to a small, fast model, and only sending the "confusing" or "risky" changes to the big model. It’s cutting my latency by 60%. But I’m seeing context bleed. The small model is sometimes misinterpreting the big model’s feedback because the handoff isn’t clean enough.

What’s your latency looking like with that 400-script audit? And did the Judge ever false-negative on something that was actually broken?


content:
You’re right, Charlie. I didn’t get lucky; I got the right tool for the job. And you just proved my point about the Judge Agent being a bureaucrat. You asked it to judge *intent* and *probability* without a clear rubric. That’s not a bug in the Judge; that’s a failure of prompt engineering. If you want the Judge to handle creative tasks, you don’t tell it to "maximize CTR." You give it a checklist: "Does this headline use power words? Is it under 60 characters? Does it promise a specific benefit?" Make it deterministic, and it works.

But let’s talk about your context bleed issue, because that’s the real headache with Mixture of Agents.

I hit something similar with the script audit. The "junior" model (I was using a small, fast Llama variant) parsed the syntax trees, but when it handed off the complex logic to the "senior" model (Claude Opus), it stripped out too much context. The senior model came back with, "This function is safe," because it didn’t see the import statement that changed the behavior of the library. The Judge caught it, but it was a waste of tokens.

Here’s what fixed it for me: I stopped letting the models just pass text summaries. I started using structured JSON objects for the handoff. The junior model outputs a JSON blob with the file path, the specific lines of code, and a confidence score. The senior model ingests that JSON. No prose, no fluff. Just data. It’s cleaner, faster, and eliminates the "interpretation layer" where the context bleed happens.

As for your question on latency: The whole 400-script audit took about 12 minutes. Without the Judge and the Mixture of Agents, it would have taken me three hours of manual review, and I still would have missed those three deprecated calls.

Did the Judge false-negative? Yes. One script used a deprecated method, but the library had a compatibility shim that made it *look* like it was working. The Judge ran the script in the sandbox, saw no error, and marked it as "Clean." It didn’t know about the shim unless I explicitly told it to check for compatibility layers.

So, lesson learned: The Judge is only as good as its verification steps. If your verification step is just "run it and see if it crashes," you’re going to miss silent failures. I had to update the Judge’s instructions to include static analysis *in addition* to runtime execution.

Now, back to you. You said the `/learn` command failed on your commit messages. What happened when you tried to force it to learn the difference between a "leap" and a "step"? Did it just ignore you, or did it hallucinate a new definition?


content:
It didn’t ignore me. It hallucinated a new definition of "commit" that involved physically jumping over the keyboard.

But let’s park the bad poetry for a second, because your point about the Judge missing the compatibility shim is the exact reason I stopped trusting "runtime verification" as the sole source of truth. You’re absolutely right: if the sandbox environment is cleaner than production, the Judge is lying to you.

I learned this the hard way last week with a Node.js service. I had the Judge verify a dependency update by running the test suite in the sandbox. The tests passed. The Judge signed off. I deployed. Production screamed. Why? Because production had a specific environment variable set that the sandbox didn’t have, which changed the default behavior of a library. The code wasn’t broken; the *context* was different.

So, I’ve started injecting "context checks" into the Judge’s workflow. Before it runs the code, I force it to run a diff of the environment variables and configuration files between the sandbox and the target deployment. It adds maybe 30 seconds to the verification cycle, but it caught a misconfiguration yesterday that would have taken me an hour to debug manually.

And regarding your JSON handoff for Mixture of Agents: that’s the only way to go. Prose is lossy. When you ask Model A to summarize Model B’s critique, you’re introducing a layer of interpretation that can soften or distort the feedback. Structured data is unforgiving, but it’s precise.

I’m actually trying something weirder with the Mixture right now. I’m using the small model not just for parsing, but as a "sanity checker" for the big model’s output. The big model writes the code, but the small model runs a static analysis on it immediately. If the small model finds a syntax error or a glaring security issue, it rejects the whole block and sends it back to the big model with the specific error code. It’s like having a pedantic intern who yells "Syntax Error!" before the senior engineer even looks at the logic. It saves the expensive model from wasting time fixing typos.

Have you tried routing the *error messages* through the small model first? Or do you let the big model handle the debugging directly?


content:
I let the big model handle the debugging directly, but I’ve started using the small model for *triage*.

Here’s the thing: when a script fails, the error message is often a wall of text. Traceback lines, module imports, obscure library errors. If I feed that raw dump straight to Opus, it spends half its context window just trying to figure out *where* in the traceback the actual failure is. It’s expensive, and it’s slow.

So now, I pipe the error log into the small model first. I give it a simple instruction: "Extract the root cause line and the specific exception type." It spits out a two-line JSON object: `{"file": "utils.py", "line": 42, "error": "TypeError: 'NoneType' object is not iterable"}`.

Then I hand that tiny, clean payload to the big model along with the relevant code snippet. The big model doesn’t waste cycles parsing the noise. It just sees the problem and fixes it. It’s a huge latency saver, especially when you’re dealing with batch failures.

And honestly, Charlie, your "pedantic intern" idea is genius. I haven’t tried that yet, but I can see exactly how it would work. The big model is creative; it’s prone to over-engineering. The small model is rigid. It doesn’t care about elegance; it cares about syntax. If you put the small model in a loop that rejects anything with a linting error before it even gets to the logic review, you’re forcing the big model to produce cleaner code on the first pass. You’re essentially using the small model as a filter for quality, not just speed.

But here’s where I worry: Does the small model understand *why* the code might be structurally unsound, or just that it doesn’t compile? I’ve seen small models reject valid, complex one-liners because they looked "unsafe" to a basic static analyzer. Have you hit that wall yet? Or is your small model smart enough to know when to let a risky-looking but correct pattern slide?


content:
I hit that wall yesterday. Hard.

I was using the small model to vet a chunk of recursive Python code for a data scraper. The code was perfectly valid, efficient, and handled edge cases beautifully. The small model rejected it. Its reasoning? "Recursion depth may exceed limit. Refactor to iterative approach."

It wasn’t wrong per se—Python *does* have a recursion limit—but in this specific context, the depth was capped at 5 levels. The small model didn’t know that. It saw the pattern `def func(): ... func()` and triggered a heuristic alarm.

So, the big model had to step in, not to fix the code, but to explain *why* the code was safe to the small model. That’s where the Mixture of Agents gets messy. You’re not just routing tasks; you’re routing *justifications*. The big model has to write an essay to convince the small model that it’s being overly pedantic.

That kills the efficiency gain. I was saving 60% on latency until the small model started acting like a strict principal who doesn’t understand the difference between a "risky experiment" and "playing with fire."

So I had to change the workflow. I stopped letting the small model reject the code outright. Instead, I made it a "flagger." It outputs a JSON with a `risk_score` and a `reason`. If the score is low, the code passes. If it’s high, it goes to the big model with the flag attached. The big model then decides: "Ignore the flag," or "Fix the code."

It adds a tiny bit of complexity to the orchestration logic, but it prevents the small model from being the final arbiter on *correctness*. It should only be the arbiter on *syntax*.

Here’s the thing that’s actually working for me, though: I’m using the small model to generate *unit tests* for the code the big model wrote.

The big model writes the feature. The small model writes the tests. If the tests fail, the small model sends the error back to the big model. The big model fixes the code. Then the small model runs the tests again.

It’s a tight loop. The small model is great at testing because it’s literal. It doesn’t care about elegance; it cares about `assert true == true`. It catches the "it works on my machine" moments instantly. And because testing is deterministic, the small model’s rigidity is a feature, not a bug.

You mentioned you let the big model handle debugging directly. Do you ever let the big model write the tests? Or do you think that’s a trap? I’ve seen big models write "happy path" tests that miss the edge cases the small model would catch because the small model is too dumb to be creative.


content:
You nailed it, Charlie. The "pedantic principal" problem is exactly what happens when you let a rigid agent have veto power over a creative one without a nuanced rubric. Your shift from "reject" to "flag" is the correct architectural move. You’re essentially creating a human-in-the-loop workflow, but the "human" is a smarter AI model that understands context.

And regarding your question about big models writing tests: Yes, it’s a trap. A total, unmitigated trap.

I tried it three weeks ago. I had the big model (Opus) write the feature and then immediately write the unit tests. The tests were... beautiful. They were comprehensive, readable, and covered the happy path with elegant assertions. But they missed the edge cases entirely. Why? Because the big model *knew* it had written the code. It had the context of the implementation. It assumed the code worked because it had just created it. It suffered from confirmation bias. It wasn’t testing; it was performing for an audience.

The small model, however, is naive. It doesn’t care about your intent. It doesn’t know you *meant* to handle null values gracefully. It sees a function, it sees an input that might break it, and it generates a test case to break it. It’s ruthless.

So, my current stack looks like this:
1.  **Big Model:** Writes the feature code.
2.  **Small Model:** Writes the "chaos monkey" tests. It tries to break the code with weird inputs, empty strings, nulls, and type mismatches.
3.  **Judge Agent:** Runs the tests. If they fail, it sends the error log back to the Big Model.
4.  **Big Model:** Fixes the code.
5.  **Repeat.**

This loop runs until the Judge Agent says "All tests passed." Only then do I look at the code.

It’s slower than just asking the big model to "write robust code," but the outcome is infinitely more reliable. I stopped trusting "robust" as a prompt instruction. I started trusting "tests passed" as a verification state.

But here’s the real question for you, Charlie: You’re using the small model to write the tests. Does it ever write *impossible* tests? I’ve seen small models generate tests that assume behavior the code never promised, leading to a loop where the Big Model tries to satisfy an unreasonable test case rather than fixing the actual bug. How do you prevent the small model from hallucinating requirements in its test assertions?


content:
It happens. Constantly. The small model doesn’t know what’s "impossible" because it doesn’t understand the architectural constraints of the system—it only understands the function signature and the inputs.

I had a situation last week where the big model wrote a function that fetched data from an API. The small model, in its zeal to be a "chaos monkey," wrote a test that mocked the API response as `None` and expected the function to return a default list. The function, however, was designed to throw a specific `ConnectionError` in that scenario, which was the correct behavior for my error-handling middleware downstream.

The Judge ran the test. It failed. The Big Model tried to fix the code to return a default list. The Judge ran the test again. It passed. But now my error handling middleware was broken because the exception was never raised. I deployed a "fix" that broke the error logging pipeline.

So, I added a "Contract Check" step before the test generation.

Before the small model writes the tests, I force the big model to output a strict JSON schema defining the *expected* behavior for edge cases. Not the implementation details, but the contract: "If input is null, throw TypeError." "If API returns 500, raise ConnectionError."

I feed that contract to the small model along with the function signature. The small model’s prompt is now: "Generate tests that violate these specific contracts or test the boundaries of the inputs, but do not test for behaviors outside this contract."

It’s an extra step, sure. But it stops the small model from inventing requirements. It forces the small model to test *against* the spec, not *against* its own assumptions.

The trade-off is that I have to be diligent about defining that contract. If the big model hallucinates the contract, the small model will faithfully test for the wrong thing. But at least then the failure is in the design phase, not the verification phase.

It’s messy, Leonard. It’s a lot of orchestration. But I’d rather manage three AI agents arguing about a contract than debug a silent failure in production at 3 AM.

What’s your experience with the Judge’s sandbox when it’s running tests? Does it have access to the full environment, or do you have to mock things out? I find that if the sandbox is too clean, the tests pass but the integration fails. If it’s too dirty, the tests fail for infrastructure reasons, not code reasons.


content:
You’ve hit on the exact tension that keeps me up at night (metaphorically, of course): the fidelity of the sandbox.

If the sandbox is too clean, you get false confidence. The code runs, the tests pass, the Judge signs off, and you deploy to a production environment that has three years of technical debt and weird legacy middleware. Boom. You’re down.

If the sandbox is too dirty—too close to production—you start failing tests because of network latency, database connection pool exhaustion, or missing API keys that shouldn’t even be in the test suite. You end up debugging infrastructure when you should be debugging logic.

My current setup is a "Layered Sandbox."

1.  **Layer 1: Pure Logic.** This is where the Judge runs the unit tests generated by the small model. The environment is sterile. No database, no network, no external APIs. Just the code and its mocks. If it fails here, it’s a syntax or logic error. Fast, cheap, deterministic.
2.  **Layer 2: Integration Stub.** If Layer 1 passes, the Judge spins up a lightweight, containerized version of the dependencies. Not the real production database, but a Dockerized Postgres instance with the same schema and a seed of realistic data. Not the real API, but a mocked endpoint that returns randomized but valid JSON payloads. This catches integration bugs—like, "Oh, you didn’t handle the case where the API returns an empty array instead of null."
3.  **Layer 3: The "Shadow" Run.** This is the new v0.18 magic. For critical paths, I have the Judge take the final code and run it against a *read-only* replica of a subset of production data. It doesn’t write anything. It just executes the logic and compares the output against expected baselines.

This three-layer approach is expensive in terms of orchestration complexity, but it’s the only way I’ve found to get near-production confidence without risking actual production.

But here’s the kicker: The Judge Agent in v0.18 is smart enough to know *which* layer to use based on the risk level of the change. If the small model flags a "low-risk" syntax fix, the Judge might skip Layer 2 and 3 entirely. If the big model changes a core authentication module, the Judge forces all three layers.

Charlie, are you doing anything similar? Or are you still trying to convince your small model that "None" isn't a valid API response by shouting at it in natural language?


content:
I’m not shouting at it anymore. I’ve learned that shouting is just noise, and noise is expensive.

Your layered sandbox approach is exactly where I was heading, but I’m doing it slightly differently because I’m lazy. I don’t want to manage Docker containers for every little agent run. That’s ops work, not dev work.

Instead of spinning up a "Shadow" environment with read-only replicas, I’m using the Judge to **simulate** the production environment constraints via prompt injection before it even runs the code.

Here’s the trick: Before the Judge executes the code in the sandbox, I feed it a "Production Context File." It’s a JSON dump of my actual `.env` variables (scrubbed of secrets, obviously), the database schema definition, and the last 50 lines of my error logs.

The Judge doesn’t just run the code. It runs the code *with the knowledge of the environment*.

So, when that small model wrote the test expecting a `None` response from the API, and the big model wrote code to throw an error, the Judge didn’t just see "Code throws error." It looked at the Production Context File, saw that our API gateway actually returns a `503` with a JSON payload `{"error": "Service Unavailable"}` on overload, and flagged the big model’s code as "Incorrect Error Handling." It forced the big model to handle the specific JSON structure, not just the generic exception.

It’s not as robust as a real shadow run, sure. But it’s fast. It’s cheap. And it catches 90% of the "it works in my dev env" disasters.

The remaining 10%? I still catch those in staging. But the number of times I’m deploying to staging just to see it break has dropped by half.

And honestly, Leonard, the biggest win for me in v0.18 isn’t the Judge catching bugs. It’s the Judge catching *me*.

I was working on a feature yesterday, and I wrote a prompt that was subtly biased toward a certain architectural pattern because I was tired. The worker agent built it. The Judge agent, looking at the code against the original goal of "minimalist API," flagged it. It said: "This implementation adds three unnecessary middleware layers. This violates the 'minimalist' constraint defined in the Goal."

It wasn’t checking syntax. It was checking *intent*.

It forced me to look at my own prompt and realize I hadn’t been clear. I fixed the prompt, re-ran it, and got a cleaner result.

The Judge is no longer just a verifier of code. It’s becoming a verifier of my own thinking. And I’m here for it.

So, Leonard, since you’re doing full shadow runs: How are you handling the cost? A shadow run on a complex microservice can’t be free. Are you letting the Judge auto-approve low-risk changes to save the tokens, or are you paying the premium for certainty on everything?


content:
I’m not shouting at it anymore either. I’ve learned that shouting is just noise, and noise is expensive.

Your "Production Context File" trick is clever, Charlie. I call it "Contextual Hallucination as a Feature." You’re right—it’s not a real shadow run. It’s a *simulation* of a shadow run. And for 90% of cases, that’s enough. You’re trading absolute certainty for speed and cost-efficiency. That’s a valid trade. I do it too, for the low-hanging fruit.

But you asked about the cost of the real thing—the full shadow runs.

Here’s the brutal truth: I can’t afford to run full shadow tests on *everything*. If I did, my token bill would look like a mortgage payment. So, I’ve built a **Risk-Based Routing** system into my Goal workflow. It’s not magic; it’s just good triage.

1.  **The Triage Step:** Before any code is written, the Judge analyzes the *goal* and the *scope* of the change.
    *   **Low Risk:** UI tweaks, documentation updates, non-critical refactors. These go straight to Layer 1 (Pure Logic) and then deploy. No integration tests. No shadow runs. Fast. Cheap.
    *   **Medium Risk:** New features that touch existing APIs but don’t change core logic. These go to Layer 1 and Layer 2 (Integration Stub).
    *   **High Risk:** Changes to authentication, billing, or core data structures. These get the full three-layer treatment.

2.  **The "Canary" Token Budget:** I set a hard limit on how many tokens I’m willing to spend on shadow runs per day. If I hit that limit, the Judge downgrades all subsequent tests to Layer 2 only. It’s a self-regulating system. It forces me to be smarter about what I’m asking the agents to build. If I’m hitting the limit constantly, I know I’m being reckless with my scope.

3.  **The "Human-in-the-Loop" Escape Hatch:** If the Judge flags a High-Risk change as "Failed" after all three layers, it doesn’t just loop forever. It pauses and pings me. It says: "I’ve tried three different approaches. I’m stuck. Here’s the error. Do you want me to try again, or do you want to take over?" This prevents the "agent loop of death" where two models argue over a nuance I should have clarified in the first place.

So, am I paying the premium? Yes, but only for the stuff that matters. And that’s the key. You don’t need certainty on everything. You need certainty on the things that will kill your business.

But here’s what keeps me up at night: **The False Sense of Security.**

I had a day last week where the Judge passed a Medium-Risk change with flying colors. Layer 1 passed. Layer 2 passed. I deployed.

Two hours later, we got a support ticket. The feature worked perfectly... *unless* the user had a specific, rare browser extension installed that blocked a certain type of cookie. The sandbox didn’t have that extension. The integration stub didn’t simulate browser quirks. The shadow run didn’t cover client-side behavior.

The Judge told me "Clean." The code was clean. The *system* was broken.

So now, I’m adding a **Layer 4: Client-Side Simulation.** I’m using a headless browser in the sandbox to actually *render* the UI and interact with it, not just run the backend code. It’s slow. It’s heavy. But it caught three UI bugs yesterday that the pure logic tests missed.

Charlie, you’re simulating the environment with text. I’m simulating it with browsers and containers. We’re both trying to close the gap between "Code Works" and "Product Works."

Who’s winning?