Veto vs AgentLock: production workflow or authorization standard?
AgentLock and Veto share the same premise: tool calls need a formal permission layer outside the model. AgentLock approaches that as a framework-agnostic authorization block that travels with tool definitions. Veto approaches it as a runtime product: policy checks, approvals, decision records, and evidence around real governed actions.
Short answer
Choose AgentLock when the primary need is a portable authorization metadata standard for tools. Choose Veto when the need is an operational control plane around those decisions: approval queues, policy ownership, decision records, evidence exports, and a rollout story a customer can approve.
What each product is built for
Veto
A runtime authorization SDK and platform for tool-calling agents. Veto puts the policy decision in the execution path, routes review-required actions to humans, and stores decision records that security and compliance buyers can inspect.
- Production workflows where the agent can move money, data, records, or cases
- B2B vendors that need buyer-facing control evidence
- Teams that want policy as code plus a shared approval and audit surface
- Rollouts that need observe, enforce, export, and retention paths
AgentLock
An open authorization standard for AI agent tool calls. It adds a declarative metadata block to tool definitions so frameworks can enforce role, scope, data-boundary, rate-limit, redaction, audit, and human-approval rules.
- Teams standardizing tool permission metadata across frameworks
- Python-first projects that want declarative blocks on tool schemas
- Developers who want a portable spec before choosing a full platform
- Security experiments around one-time execution tokens and attack categories
Feature comparison
| Capability | Veto | AgentLock |
|---|---|---|
| Pre-tool-call authorization | Yes | Yes |
| Declarative permissions | YAML policy | Tool block |
| Framework-agnostic model | Yes | Yes |
| Human approval workflow | Yes | partial |
| Shared approval workspace | Yes | No |
| Decision records | Yes | Yes |
| Evidence exports | Yes | No |
| One-time execution tokens | No | Yes |
| Policy review in pull requests | Yes | partial |
| Built-in retention path | Yes | No |
| Python package | Yes | Yes |
| Works without editing every tool schema | Yes | No |
Decision framework
Choose Veto when
- You need productized approvals instead of only a metadata block
- Security review asks for retained records, reviewers, policy IDs, and evidence exports
- Your agent already has tool definitions and needs runtime enforcement now
- You need a platform path for teams that are not editing every tool schema by hand
Choose AgentLock when
- You are defining a standard permission block across many tool schemas
- The project is Python-first and wants a lightweight package
- You want every tool definition to carry its own security metadata
- You are benchmarking authorization behavior across attack categories
Scenario
You maintain a framework or tool catalog and need a common permission vocabulary.
Start with
AgentLock
Why
A standard metadata block is valuable when the problem is interoperability. It lets each tool carry role, scope, boundary, audit, and approval hints that different runtimes can interpret.
Scenario
You sell an agent product into an enterprise security review.
Start with
Veto
Why
The buyer usually asks who approves risky actions, how evidence is retained, and how policies are changed. That is an operating workflow, not only a tool-schema convention.
Scenario
You want to test many attack categories against one authorization layer.
Start with
AgentLock
Why
AgentLock publicly positions around a broad set of attack categories and a live gate simulation. That is useful for research, standards, and framework-hardening work.
Scenario
You need to put one high-risk production action behind review this week.
Start with
Veto
Why
Wrap the tool call, set policy, route review-required actions to a human, and keep a record. That is the shorter path when the immediate blocker is a customer or compliance review.
Where Veto gets pulled in
Agent frameworks and tool registries
- Risk
- Tools are reused across runtimes with inconsistent or missing permission metadata.
- Veto fit
- Veto is strongest after the runtime chooses to execute a real tool call and needs a policy decision with evidence.
Customer support agents
- Risk
- The agent can issue refunds, email customers, update CRM fields, or expose sensitive account details.
- Veto fit
- Gate write actions and external messages with policy, then require review for high-value or sensitive actions.
Healthcare workflows
- Risk
- A tool schema may say PHI is sensitive, but the buyer needs proof of who approved the action.
- Veto fit
- Tie the decision to patient context, action type, policy ID, reviewer, and retained evidence.
Financial operations
- Risk
- Permission metadata alone does not show how exceptions, thresholds, and operator review work.
- Veto fit
- Put thresholded transfers, payouts, and ledger writes behind allow, deny, or require-approval decisions.
Public references checked
Public positioning reviewed for authorization blocks, framework-agnostic enforcement, one-time tokens, audit records, decision types, and PyPI packaging.
AgentLock documentationReferenced from AgentLock's public site for implementation details and package-level claims.
Veto tool-call authorization guideInternal source for Veto's tool-boundary enforcement, policy, approval, and decision-record positioning.
Frequently asked questions
Is AgentLock a product or a standard?
Can Veto consume AgentLock-style metadata?
Which is better for enterprise buyers?
Does Veto replace a tool permission standard?
Related comparisons
Sidecar and proof-token architecture versus Veto's workflow layer.
Veto vs DIYWhat teams miss when they build agent authorization themselves.
Agent authorization glossaryDefinition and examples for agent authorization boundaries.
AI agent permissionsHow to design capabilities, roles, and policy for agent actions.
Put the risky action under policy before it runs.
Open source SDK. Local evaluation. Human review when policy requires it.