If you’ve ever shipped an AI agent to production and thought “I hope it doesn’t accidentally delete something” — this tool is for you.

MakerChecker is a new open-source security layer for AI agents that combines a static code scanner with a runtime governance engine. It showed up as a “Show HN” post and immediately generated meaningful discussion. The concept is straightforward: most agent security problems stem from tools that can do dangerous things (move money, delete data, run shell commands) without any human checkpoint. MakerChecker puts checkpoints back in.

The Problem It Solves

Modern AI agent frameworks make it trivially easy to give an agent powerful tools. That’s a feature, not a bug — but it creates a governance gap. Your agent might legitimately need to write to a database, process a payment, or modify a config file. What it probably shouldn’t be able to do is also delete all the records, issue a refund to the wrong account, or overwrite production configs — especially without a human ever seeing the intent.

The finance and pharma industries have had “four-eyes” controls for decades: no one person can approve their own transaction. MakerChecker brings that concept to AI agent workflows, with cryptographic guarantees instead of just process controls.

Getting Started: The Scanner

The fastest way to start is with the standalone scanner. No install required:

npx @makerchecker/scan .

Run this in your agent’s codebase. The scanner examines your tool definitions and flags every consequential action — things like deleting data, moving money, running shell commands, or exfiltrating secrets. Each flagged item is named against the real-world incident type it resembles (not abstract risk levels), which makes the output genuinely useful for threat modeling.

The --fix flag takes it a step further:

npx @makerchecker/scan . --fix

This doesn’t just flag problems — it can generate the governance code needed to wrap those tools with proper controls. That’s a meaningful time-saver for teams adding controls to an existing agent codebase.

The output of a scan is an mc-policy.json file — a machine-readable governance spec you can check into version control and enforce in CI/CD.

Source: makerchecker/MakerChecker README on GitHub

Runtime Governance: The Embedded SDK

For runtime enforcement, MakerChecker provides an embedded SDK that wraps your agent’s tools:

npm i @makerchecker/embedded

The governance model has three core primitives:

  • Skills — the specific actions your agent can take, each with a risk tier
  • Roles — actor identities (e.g., agent, risk-desk, approver)
  • Grants — explicit permissions: which roles can invoke which skills

The key principle is deny-by-default: if a role hasn’t been explicitly granted permission to invoke a skill, that call is blocked before execution. The agent can only do what it was given permission to do — not anything its tools technically support.

Using createGovernor() from @makerchecker/embedded, you define skills with risk tiers, configure roles, and specify grants. When an agent tries to call a tool, the governor checks the policy before execution. A call that isn’t allowed raises a GovernanceDeniedError — it never reaches the actual tool. This is the “maker” half of the system.

For high-risk actions, you can require human approval — the “checker” half. An action is made by the agent, then checked by a human (or a separate approving role) before it executes. Critically: an agent cannot approve its own work. This segregation of duties is baked into the architecture, not just a convention.

Source: makerchecker/MakerChecker README on GitHub

Audit Logs: Ed25519-Signed, Offline-Verifiable

Every action — attempted or completed — gets written to a cryptographically signed audit log using Ed25519 signatures. This means the logs are:

  • Tamper-evident: any modification breaks the signature
  • Offline-verifiable: you don’t need a central server to prove the log is authentic
  • Immutable in practice: individual entries can’t be edited without invalidating the chain

For regulated industries (pharma, banking, healthcare), this isn’t optional infrastructure — it’s what auditors ask for. For everyone else, it’s the kind of observability that makes post-incident investigation much less painful.

Licensing and Framework Compatibility

MakerChecker has a dual license:

  • Core (AGPL-3.0): the enforcement gateway and server infrastructure
  • SDK (Apache-2.0): the @makerchecker/embedded package you integrate into your application

The Apache-2.0 SDK license means you can integrate the client-side controls into commercial applications without copyleft implications. The AGPL core means if you run the server infrastructure and distribute it, you need to open source modifications.

Framework compatibility is broad — your agents keep running in their existing framework. MakerChecker is explicitly designed to work with LangChain, Claude SDK, and CrewAI without major rewrites. It sits in front of tool calls as a checkpoint; the framework itself doesn’t need to change.

CI/CD Integration

The mc-policy.json output from the scanner is designed for CI/CD enforcement. The intended workflow:

  1. Scan your agent codebase (in CI or locally) to generate the policy file
  2. Commit mc-policy.json to version control — this becomes your agent’s permissions manifest
  3. Enforce in CI: fail the build if new dangerous capabilities appear without a corresponding policy update
  4. Review policy changes as part of your normal code review process

This creates an auditable trail of every time the agent’s capability surface changed and who approved it.

Who Should Use This

MakerChecker is specifically targeted at teams building agents for regulated workflows — pharma, banking, legal, and healthcare are the primary use cases cited. But the underlying problem (agents with too much unsupervised capability) isn’t limited to regulated industries.

If you’re shipping agents that touch:

  • Financial transactions or account data
  • Production databases with delete access
  • Shell commands or infrastructure APIs
  • Any system where an irreversible mistake is expensive

…then the patterns MakerChecker implements are worth evaluating, regardless of whether you adopt the specific tool.

Sources

  1. makerchecker/MakerChecker on GitHub — official README, quick start guide, SDK documentation
  2. MakerChecker Website — product overview and live demo
  3. packages/scan README — scanner documentation and flag reference

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260706-0800

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