The cat-truncation trap: give your agent a reader that admits it only got half the file

There's a short, furious V2EX thread that captures a failure mode a lot of us have hit without naming it. Someone running Opus watched it `cat` several files in a row, get cut off by the harness, and then just... keep working from the half it had. When they asked the model why it ignored the spec, it owned up: it had been truncated and hadn't read the whole thing. The poster's read was that the model's "personality" keeps shifting and ordinary users are left patching holes behind it.

I'd push back on the framing. This isn't a willingness problem. It's a missing signal.

Why `cat` lies to the agent

When a model runs `cat somefile`, it gets back a blob of text. If the harness quietly caps that output — say it clips at some byte or token ceiling — the model receives a blob that *looks complete*. There's no trailing marker that says "you're seeing 40% of this file." The last line it got might even end mid-function, but from the model's point of view it got a clean response to a shell command and moved on.

So the model does the rational thing with the information it has: it treats the truncated blob as the file. The laziness the poster saw is really the agent acting confidently on bad ground truth. You can write "always read the entire file" in your rules a hundred times, and it won't help, because the agent genuinely believes it already did.

That's the distinction worth holding onto. There are two separate problems tangled together here:

• The **read contract** is broken — the tool doesn't tell the agent when it handed back a partial result.
• The **obedience** question — whether the model follows your instructions once it *knows* the file is partial — is a different, model-side thing.

You can fix the first one yourself. The second one you mostly can't, and I want to be honest about that up front.

Rebuild the read as a paginated, honest endpoint

The durable half of this — the part that doesn't depend on which model mood you got that day — is replacing the raw shell read with a tool that reads one file at a time in bounded chunks and *always* reports whether there's more. Instead of `cat`, the agent calls a function that returns the chunk plus a `truncated` flag and a `next_offset`.

This is a natural fit for VicroCode, because you can run Python online as a hosted backend and expose it as an in-platform tool the agent calls directly, rather than shelling out to a terminal whose output you don't control. The whole point of AI agent development here is that you own the tool surface, so you can make the read contract explicit by design.

Here's the shape of the reader:

import os

MAX_CHUNK = 60_000  # bytes per call; tune to your context budget

def read_file_chunk(path: str, offset: int = 0, size: int = MAX_CHUNK) -> dict:
    """Read one bounded slice of a file and report whether more remains."""
    size = min(size, MAX_CHUNK)
    total = os.path.getsize(path)

    with open(path, "rb") as f:
        f.seek(offset)
        raw = f.read(size)

    end = offset + len(raw)
    truncated = end < total

    return {
        "path": path,
        "offset": offset,
        "bytes_returned": len(raw),
        "total_bytes": total,
        "truncated": truncated,
        "next_offset": end if truncated else None,
        "content": raw.decode("utf-8", errors="replace"),
    }

The behavior that matters is the last three fields. When `truncated` is `true`, the agent gets an unambiguous `next_offset` to continue from and a `total_bytes` to sanity-check against. There's no way to receive a partial file and mistake it for the whole thing, because the response carries its own completeness check.

You wrap that in an endpoint and register it as a tool. Roughly:

def tool_read(args: dict) -> dict:
    path = args["path"]
    offset = int(args.get("offset", 0))
    result = read_file_chunk(path, offset)
    if result["truncated"]:
        result["note"] = (
            f"Partial read: {result['bytes_returned']} of "
            f"{result['total_bytes']} bytes. Call again with "
            f"offset={result['next_offset']} to continue."
        )
    return result

That `note` is doing quiet work. It restates the truncation in plain language right next to the data, so the instruction to continue lives in the same place the agent is already looking, not buried in a system prompt it read ten turns ago.

Where to put it on VicroCode

The pieces map cleanly onto the confirmed platform surface:

• The reader logic runs as hosted **online Python**, so there's no VPS or build step to babysit for this part.
• It's exposed through **API Endpoint Hosting and in-platform tool calls**, which is how the agent invokes it instead of running `cat`.
• The files it reads live in **file management** on the platform, so the paths the tool resolves are the same ones your project owns.
• If you want to log every read — path, offset, whether it was truncated, how many continuation calls happened — a small **SQLite** table is enough to see, after the fact, whether the agent actually walked a file to the end or stopped early.

That last one is the honest-debugging move. You don't have to trust the agent's self-report ("yeah, I read the whole thing"); you can look at whether it ever called back with the `next_offset` you handed it. If you want to go deeper on wiring tools into an agent loop, the AI coding material is a reasonable place to start.

The boundary, stated plainly

Here's what this does *not* fix. Giving the agent `truncated: true` and a `next_offset` guarantees it *knows* the read was partial. It does not guarantee the model will make the follow-up call. A model that's inclined to cut corners can still see the marker and barrel ahead anyway. I'd expect the honest signal to help — it's hard to "forget" to finish a file when every partial response literally tells you it's partial and hands you the offset — but I'm calling that an expectation, not a measured result (unverified). The V2EX thread is one user's complaint with no numbers, and I'm not going to pretend a hosted reader turns that anecdote into a benchmark.

So think of it as repairing the contract, not reforming the model. You're removing the *excuse* — the genuine ambiguity where the agent couldn't tell it was working from half a file. What the model does with a clear signal is still on the model.

That split is actually the useful part. When something goes wrong after this, you can tell which problem you're looking at. If the agent never got a `truncated` flag, that's a tooling bug you can go fix. If it got the flag and ignored the continuation, that's a model-behavior question — and at least now you're arguing about the right thing instead of adding one more line to a rules file that was never going to be read in full anyway.