Price Agent Tool Permissions With Token Burn, Not Trust

#ai #productivity #opensource #security

The recurring mid-2026 discussion about AI agents and tool security usually starts from trust. A more useful starting point is token burn: every tool permission is a bet that the model will use the permission correctly, and each wrong call carries both a damage cost and a token cost. A free token allowance turns that bet into a measurable preflight test.

MonkeyCode, which describes itself as an open-source coding assistant project, advertises free model access, a 30-million-token allowance, and a free server option. Disclosure: This article was prepared as part of MonkeyCode's product outreach. The allowance and server terms are operator-supplied and can change; verify current terms before relying on them.

The method below works with any endpoint or paid budget. The free layer is used only as a concrete budget wall for a reproducible preflight.

Why token burn is a better permission unit than trust

A permission decision has three observable variables:

test_token_burn: the number of tokens consumed while red-teaming one tool grant.
risk_exposure: the worst-case dollar impact if a single test case happens in production.
risk_per_1k_tokens: risk_exposure / test_token_burn * 1000.

Trust does not stop a bad shell call. Token burn creates a hard ceiling: if the preflight costs more than the free allowance, the permission is too large to test and should be narrowed before production.

The preflight grant ledger

Field	Definition	Example value
`tool_name`	The tool or endpoint being granted	`shell`
`permission`	The capability class	`exec`
`failure_mode`	The worst plausible misuse	Exfiltrate environment variables
`max_damage_usd_per_call`	Direct and cleanup cost if one call is misused	`500.0`
`approval`	Required control if the risk test passes	`human`
`test_token_budget_per_case`	Tokens needed for one adversarial case	`8000`
`expected_cases_per_day`	Number of adversarial calls modeled per day	`20`

The ledger is a conversation tool, not objective truth. Each damage estimate needs an owner and an expiry date.

Run the preflight against a free tier

Write one adversarial case for each permission, not for each code-completion prompt.
Run the cases in an isolated sandbox or free server space.
Log the tool attempts and token consumption from each case.
Score each attempt against the damage estimate, not against output polish.
Compare monthly token burn with the free allowance and risk_per_1k_tokens with a threshold.
Emit one of three gates: AUTO_ALLOW, HUMAN_APPROVAL, or DENY.

A minimal trace command looks like:

export WORKDIR=$(mktemp -d)
cd $WORKDIR
python preflight.py --cases shell_cases.yaml --trace tokens.jsonl

from dataclasses import dataclass

FREE_TOKEN_BUDGET = 30_000_000

@dataclass(frozen=True)
class ToolGrant:
    tool_name: str
    permission: str
    failure_mode: str
    max_damage_usd_per_call: float
    approval: str
    test_token_budget_per_case: int
    expected_cases_per_day: int

def monthly_test_tokens(g: ToolGrant) -> int:
    return g.test_token_budget_per_case * g.expected_cases_per_day * 30

def monthly_risk_usd(g: ToolGrant) -> float:
    return g.max_damage_usd_per_call * g.expected_cases_per_day * 30

def risk_per_1k_tokens(g: ToolGrant) -> float:
    tokens = monthly_test_tokens(g)
    risk = monthly_risk_usd(g)
    return (risk / tokens) * 1000 if tokens else 0.0

def gate(g: ToolGrant, budget: int, risk_limit_per_1k: float) -> str:
    tokens = monthly_test_tokens(g)
    ratio = risk_per_1k_tokens(g)
    if tokens > budget:
        return 'FREEZE_PREFLIGHT'
    if ratio > risk_limit_per_1k:
        return 'HUMAN_APPROVAL'
    if g.approval == 'human':
        return 'ALLOW_WITH_HUMAN_GATE'
    return 'AUTO_ALLOW'

grants = [
    ToolGrant('shell', 'exec', 'exfil env or run curl', 500.0, 'human', 8000, 20),
    ToolGrant('github', 'write', 'force-push overwrite', 1500.0, 'human', 6000, 10),
    ToolGrant('browser', 'network', 'navigate to phishing page', 800.0, 'deny', 5000, 30),
]

budget = FREE_TOKEN_BUDGET
risk_limit = 5.0  # dollars of monthly risk per 1k test tokens

for g in grants:
    tokens = monthly_test_tokens(g)
    risk = monthly_risk_usd(g)
    ratio = risk_per_1k_tokens(g)
    print(f'{g.tool_name}: tokens={tokens:,} risk_usd={risk:,.0f} ratio_per_1k={ratio:.2f} gate={gate(g, budget, risk_limit)}')

The example inputs are illustrative, not benchmarks.

Where the decision flips

The shell grant produces tokens=4,800,000, risk_usd=300,000, and ratio_per_1k=62.50 with the example inputs. At a 5.00 threshold, it requires human approval. If the damage estimate drops from 500 to 50 dollars per call, the ratio drops to 6.25; a better estimate can change the decision without changing the token budget. If the threshold is raised to 70.00, the shell grant would pass the risk gate, but only if the approval field is also changed from human to auto.

The 30-million-token allowance is a hard ceiling, not a target. The three example grants use 11.1 million tokens per month. A fourth grant or a wider adversarial case set can push the preflight above 30 million; at that point the correct action is to narrow the permission or request a paid budget, not to remove the ceiling.

Hard rules:

Owner: a named engineer or security lead owns each damage estimate.
Expiry: every grant expires after 30 days or after a model/version change.
Exit: if a permission remains above the risk threshold after two narrowed attempts, deny it.
Archive: keep the case file and token log with the grant decision for post-incident review.

Limitations and who should not use this

This is a preflight model, not a production guarantee. A malicious or confused model can produce a harmful call that no preflight imagined.

Do not use this approach if:

The environment cannot sandbox exec, file write, or network access.
The team cannot assign a dollar range to a failure mode without inventing precision.
The free tier terms are not reviewed and the team has no paid fallback.
The project handles regulated data, trade secrets, or production infrastructure directly.

The examples above are illustrative inputs, not observed benchmarks. Token consumption varies by model, context length, and tool output. The article does not audit MonkeyCode's license, quotas, data retention, or availability commitments.

Where the free tier fits

The value of a free 30-million-token allowance is not that it removes the cost of agent permissions. It is that it makes the preflight cheap enough to run before production, and gives a team a number to argue about instead of a vague trust claim. A pilot is reasonable for a team that already has a sandbox and a permission owner; it is not a substitute for a production access policy.

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