Software Correctness on Autopilot

OVERVIEWWhat we do

Workers IO agents help teams build critical software with significantly higher correctness. By applying formal and semi-formal methods, we rigorously verify code behavior — making our approach ideal for designing and delivering reliable, safe, and high-integrity systems.

These approaches have ensured reliability in safety-critical systems for decades and are credited by large cloud providers for their operational stability. Unlike general software testing, our use of mathematical proofs addresses the most critical behaviors with a degree of confidence that testing alone cannot provide.

THE PROBLEMWhy software fails

Software failures typically result from the accumulation of minor, tolerated imperfections rather than a single dramatic error. These issues often converge at critical moments.

Tolerated failure

Minor defects are often tolerated because they seem insignificant on their own. Over time and across components, these risks accumulate and can result in major outages.

Creation is easy; correctness is hard

LLMs have reduced the cost of code generation to near zero, enabling rapid development. However, ensuring correctness remains expensive. We are entering a world of running software that no human has read. Critical systems in aviation, finance, and healthcare demand software that is reliable under pressure and at scale. The main challenge is no longer writing code, but defining and proving the essential properties that must always hold.

No natural constraints

Physical systems are constrained by the laws of physics, while software operates within an effectively unlimited state space. To maintain reliability, software behavior must be governed by explicit invariants, contracts, and types. Without these controls, systems can evolve beyond our ability to understand and manage them — a problem that AI-generated code dramatically accelerates.

WHY NOWLLMs are reshaping software itself

LLMs are not just a faster way to write software. They are reshaping the economics and architecture of software itself. When rewriting and understanding large codebases becomes cheap, the incentive to rely on deep dependency trees collapses. Legacy code loses its moat — entire codebases can be studied, rewritten, and upgraded in situations where humans would have given up long ago. The same shift is visible at the product layer — teams are embedding AI at progressively deeper levels in their SaaS, moving from simple input boxes toward fully generated, per-customer applications.

As the human factor in programming diminishes, selection pressure shifts toward languages that favor formal verifiability, type safety, and performance — properties often harder for humans to learn but well-suited to LLMs, which thrive in environments with verifiable rewards. Even Rust, despite its impressive type system, was designed around human cognitive constraints. What an optimal target language for LLM-generated, formally verified code looks like remains an open and exciting question.

In this landscape, formal verification is not optional — it is essential. The catch is that unknown unknowns remain unknown, and verification can only prove what you specify. But the alternative — unverified AI-generated code at scale — is far worse.

IMPACTCost of software failure

Software failure is a business and human-life risk, not just a technical risk. In July 2024, CrowdStrike’s update caused multi-day global disruptions; one airline alone reported $380M in lost revenue over five days, and the human lives lost during that incident are non-zero.

Such failures will become more common with LLMs writing most of the code without strict guardrails that can be verified with a high degree of accuracy.

Calculate your failure cost

If you have not estimated each category, you may be underestimating the total impact.

APPROACHVerification vs traditional testing

Verification takes a fundamentally different approach. Instead of sampling a few paths, it mathematically explores all possible execution paths against the rules and invariants you define. You don’t just gain confidence — you obtain a strong assurance, or a precise counterexample showing exactly where and how it fails.

HOW IT WORKSDelivering correctness

We capture required software behavior as executable specifications at different layers of your codebase.

TRANSPARENCYOpen challenges

We believe in being transparent about the limits of what verification can do today.

VISIONWhere we are going

Software engineering is changing rapidly. We believe there is a future in which the primary artifact is not code but specifications — succinct, version-controlled statements of intent that capture what the software must do, independent of how it is implemented. When that happens, software correctness and verification will become the central discipline.

We are in a phase where the cost of software creation is approaching zero, and the next challenge is ensuring software correctness. We are building the tools necessary for agents to generate code and verify the desired behavior with high rigor. When we achieve this, we will be able to deliver software more quickly and with greater impact, solving the tough problems that matter to people’s lives.

FAQFrequently asked questions