About This Framework

This is a framework for engineers building AI agent systems who want to understand why some deployments succeed and others fail—and how to build systems that actually work.

The Core Argument

Agents in production require reliability. Reliability requires error correction. Error correction requires structure.

Most domains don't have that structure—at least not explicitly. Humans learned the rules through experience: what's allowed, what isn't, what combinations make sense. None of this is written down. People just know.

Agents are fluent, not experienced. They'll try things any human would avoid. Without explicit rules to check against, the mistakes go through. That's why impressive demos become disappointing deployments.

What Structure Means

Structure is what lets you catch errors and fix them.

When the rules are explicit, violations fail immediately. You know what state things are in. Operations can be undone. You can go back to a known good point. Errors get corrected, not compounded.

Without explicit structure, errors pass through silently. You find them later—sometimes much later—when the damage is already done.

Who This Is For

Software engineers and leaders who want to:

• Understand why agent deployments fail despite capable models
• Build agent systems that actually work in production
• Think clearly about what agents can do and what they can't

How to Use This Site

If you're new: Start with The Structure Problem. It's the complete argument in about 11 minutes. Everything else expands on it.

If you want depth: Read the essays in order. They build a narrative: the core problem, what agents actually are, why errors compound, how to build structure incrementally.

Key Ideas

Agents are translators, not autonomous workers. They convert fuzzy human input into structured system operations. But translation requires something to translate into. An agent without structure has nothing to work with.

The key to long-running agents isn't making fewer errors—it's catching and correcting them. 99% accuracy per decision means 37% success at 100 decisions. You can't make error rates low enough. You need errors caught before they compound.

The structure doesn't exist because humans didn't need it. Organizations ran on implicit knowledge—judgment, experience, context passed through training. That worked when humans operated the systems. Agents can't fill those gaps. They expose every place where explicit structure is missing.

Structure emerges through operation, not upfront design. Start with agents as translators—you approve everything. Patterns emerge. Turn those patterns into validation rules. Build the interface incrementally from what you observe. You can't encode judgment you haven't exercised.

What This Is and Isn't

This is a framework grounded in established principles—information theory, operating system design, high-reliability engineering—applied to the specific problem of building agent systems.

This isn't a mathematical proof. It won't work for every domain. Some domains may be genuinely irreducible. But for the domains where agents are being deployed—customer support, data processing, business workflows—the framework explains the patterns we're seeing and provides practical guidance.

Use it as a lens. When something contradicts it, investigate why. Update as evidence arrives.

About the Author

Pawel Zimoch is an ML/AI researcher and software engineer based in Boston, MA. His work spans probabilistic generative models, Bayesian reasoning, and deep learning—with a bent toward information theory and automation.

As a software engineer, he's been interested in building autonomous, resilient systems that "just work" with minimal supervision. This framework is an attempt to apply those principles to agent systems.

Contact

To get in touch, send an email to agents@zimoch.tech. All incoming emails are read, though the response may be agent-generated.