AWS Hydro: the compiler as a safety net for AI agents

The team behind Hydro, the open source framework in Rust for distributed systems, has identified a growing problem: AI agents generate more and more code and human reviews cannot keep up. Shadaj Laddad, the team's research lead at AWS, explained in an interview with The New Stack that a single change generated by agents can become a 5000-line pull request, difficult to split and review. His proposal is clear: move part of the verification to automatic tools outside the agent's "trust zone", such as the compiler.

The idea is not new, but it becomes urgent with the irruption of agents. Laddad compares this moment with historical milestones: C prevented programmers from manipulating function calls in assembly; Java eliminated manual memory management. Now, agents make necessary a next level of abstraction that guarantees certain properties of the software without depending on human review. Hydro applies this principle to distributed systems, detecting at compile time errors such as race conditions or failures in idempotency (that a retried request is not executed twice).

Why is it relevant for an SMB?

Although Hydro is designed for teams building high-concurrency and low-latency services, the underlying debate affects any company that starts using AI agents to program. In an SMB, the IT team is usually small and cannot afford to review line by line everything an agent generates. In addition, agents not only write the code, but also the unit tests, which creates a dangerous circle of trust: if the agent makes a mistake, its tests could validate the error.

Laddad's proposal is to establish "domain-specific safety barriers" that agents share. For example, idempotency is a recurring pattern in design reviews; turning it into an automatic check would prevent an agent from introducing a subtle failure when retrying an operation. This is especially critical in integrations with ERP, payment gateways or reservation systems, where a poorly managed retry can duplicate charges or orders.

Limits and risks of relying only on the compiler

Laddad himself acknowledges that compilers have limits. As we published in August, AI-generated Rust code can compile without errors and still be incorrect. Therefore, automatic checks are necessary but not sufficient. The key is to combine them with a layered verification strategy: static analysis, realistic integration tests and human reviews focused on critical points.

In addition, Hydro is a project in an early phase. Laddad warns that users should expect rough edges and a learning curve. It is not a tool ready for production in any context, but an exploration of where agent-assisted development should go.

What we recommend from ForgeNEX

For an SMB that already uses AI agents in its development flow, these are the steps we suggest:

• Define a clear "trust zone": agents can generate code, but critical checks must run outside their control. Tools such as linters, static analyzers and compilers with strict checks are allies.
• Do not delegate validation to the tests written by the agent itself: require integration tests written by humans or generated with independent criteria. In distributed systems, test failure and retry scenarios.
• Start with recurring patterns: identify errors that repeat in your domain (idempotency, transaction handling, concurrency control) and automate their detection. You can start with simple rules in your CI/CD pipeline.
• Review the generated code focusing on the boundaries: do not review everything equally; prioritize the areas where the agent interacts with external services, databases or payment APIs.
• Stay up to date with projects like Hydro and Infinity: both are open source and the result of collaboration between AWS and UC Berkeley. Even if you do not adopt them today, they mark the direction of the tools to come.

The conversation about coding agents also connects with other trends we have analyzed, such as GitHub's shielding for the era of agents or the opening of Android to third-party agents. In all cases, the question is the same: how do we maintain control when AI acts autonomously?

At ForgeNEX we believe that the answer involves integrating automatic verification into the software lifecycle, without giving up human oversight at risk points. If you are starting with AI agents in your team, we can help you design a trust pipeline that adapts to your size and sector.

Source: The New Stack. Analysis and adaptation: ForgeNEX.