The Real Cost of 'Free' AI: A Budgeted Patch Loop for Agent-Written Code

When a developer watches an agent patch stream past, two separate contests are running on the screen. The first is code correctness. The second is the cost of the compute the agent burned to claim that correctness. Teams with free model access make a recognizable mistake: they let the iteration continue because every individual request feels free. The total grows anyway, but the cost is invisible until the far larger expense of integrating a broken patch appears.

The fix is not a better model. The fix is a fixed cost. Any loop you run for an agent patch — free model or paid — should run against a pre-assigned budget with an explicit stopping condition. Free resources are still resources. They are easier to allocate than money, but they are not unlimited, and treating them as unlimited quietly trains your pipeline to generate unusable work.

The free-tier trap: generating instead of verifying

A free model endpoint is a generator, not a verifier. It can produce a test, a bug report, or a mocked benchmark in seconds. The bottleneck is never generation; the bottleneck is verification, which costs compute, wall-clock time, and human attention. When generation is artificially cheap, teams overproduce unverified artifacts and hide the real cost in review meetings.

My workflow inverts that: generation is budgeted and verification is cheap and repeatable. I run a patch-loop where the free model proposes tests for a C++ change, and a separate free server runs the verification channel. The server is the gate. The model is just a proposal engine. They are different resources, and mixing them up is where the waste begins.

The budgeted patch loop

For each agent-written patch I apply three steps, each with a fixed upper limit written into the script before the agent is allowed to start.

1. Build window. The patch is applied to a clean checkout and must compile within a fixed time limit. Failure to build terminates the loop regardless of how promising the diff looks.
2. Generation budget. The free model may spend at most a fixed token allowance generating tests for the patch. When the allowance is exhausted, generation stops permanently.
3. Verification channel. The generated tests run on the free server, in a container that resets between runs, so the environment is identical every time.

The script below is the actual loop I keep in a repository for a small C++ utility library. The shell is deliberately boring; the discipline is in the numbers you choose and never revise mid-run.

#!/usr/bin/env bash
set -euo pipefail

PATCH_FILE="$1"
BUILD_SECONDS=240                 # step 1: hard build limit
GENERATION_TOKENS=40000           # step 2: hard generation limit
SERVER_HOST="localhost:8787"     # step 3: verification channel

start=$(date +%s)

# Step 1: apply, then compile under a watchdog
if ! timeout "$BUILD_SECONDS" sh -c 'git apply "$1" && cmake --build build' _ "$PATCH_FILE"; then
  echo "[loop] build failed or exceeded ${BUILD_SECONDS}s"
  exit 10
fi

# Step 2: let the free model draft tests until the budget is spent
TOTAL_TOKENS=0
while (( TOTAL_TOKENS < GENERATION_TOKENS )); do
  RESPONSE=$(curl -s -X POST "$SERVER_HOST/generate" \
    -d @- <<'JSON'
{"patch": "'"$PATCH_FILE"'", "max_tokens": 8000}
JSON
  )
  SELECTED=$(jq -r '.test_code' <<< "$RESPONSE")
  USED=$(jq -r '.usage.total_tokens' <<< "$RESPONSE")
  TOTAL_TOKENS=$(( TOTAL_TOKENS + USED ))

  if ! echo "$SELECTED" | tee tests/generated_$(date +%s).cpp; then
    break
  fi

  sleep 2   # respect the server between generations
done

# Step 3: run the verification channel on the free server
curl -s -X POST "$SERVER_HOST/verify" -d @tests/generated_*.cpp

elapsed=$(($(date +%s) - start))
echo "[loop] finished in ${elapsed}s with ${TOTAL_TOKENS} generation tokens spent"

Run it as ./budget_loop.sh agent_patch.diff. The three numbers at the top are the contract. Change them between loops, not during one.

The script is a template, not a finished tool. You will need a local endpoint that forwards to your model provider and a verification server that compiles and executes the generated tests in a sandbox. The parts I leave abstract are where your own constraints belong.

Reading the loop's output

The loop produces three possible outcomes, and each maps to a different decision.

Outcome	Meaning	Decision
Build timeout	The patch does not fit the codebase	Reject without further spending
Generation budget spent, few tests pass	The change is poorly understood by the model	Reject or re-specify the task
All generated tests pass on the server	The patch behaves under the proposed contract	Send to human review with the test list

The third outcome is the only one that earns human attention. Everything else is terminated before a developer spends ten minutes reading a diff that will never compile.

This is the loop I run daily, and the infrastructure is deliberately inexpensive. MonkeyCode's current free tier, as of this writing, includes free model access and a free server option, which is where the verification channel in the script lives. The economics matter less than the habit: a bounded loop that stops when the budget ends, not when the agent gets bored.

Disclosure: This article was prepared as part of MonkeyCode's product outreach.

Why the budget must be a wall, not a suggestion

Incentives inside an agent loop are asymmetric. The agent's objective is to feel done, so it will consume as much compute as the environment allows. A budget that lives only in a pull request comment is a suggestion. A budget enforced by timeout and an integer counter is a wall. My experience is that the second behavior is what makes free resources usable in the first place: the free server keeps the environment reproducible, and the token counter keeps the model from wandering.

You should also record the outcome of every loop. Keep a small JSON trace per patch, because the trace is what lets you change the budget later with evidence instead of intuition.

Limitations

The loop has narrow applicability. It works when the target behavior can be expressed as executable tests within a small number of tokens. It fails for patches that are primarily architectural, where the important properties are structural and a generated test cannot observe them. It also fails when your verification sandbox is slower than your generation rate, because the server queue becomes the real bottleneck and the token budget stops being the constraint.

If you ship to embedded devices, the verification channel cannot possibly run in a free shared server. If your codebase depends on proprietary databases or licensed toolchains, a generic sandbox will not reproduce the behavior that matters. This workflow is for portable, self-contained projects, and it is honest about that.

Free resources earn their name only when they are allocated deliberately. Set the budget, keep the trace, and let the loop decide what reaches a human.