When we started Midlyr, our goal wasn’t just to make another AI tool — it was to build agents that people in financial operations could actually trust.

In regulated environments like banking or fintech, reliability is everything. If an AI gets a few things wrong in a marketing summary, that’s fine. But if it gets a few things wrong in a compliance review, that’s a real problem.

The math of reliability

If you work with AI systems, you’ve probably seen this before.

Even if a model is 95% accurate at each step, when you string five steps together, the overall success rate drops to about 77%.

That’s the math of compounding uncertainty — the kind that shows up when you chain reasoning steps or let a model make multiple dependent calls. Everyone in the field knows it, but it’s a good reminder of why “mostly right” isn’t enough.

Now, assume a human interferes every three steps to “reset” reliability back to 100% after review — the performance curve looks very different. A 95%-accurate model ends up performing consistently above 90%, while a fully automated workflow can dip below 75% after just a few more steps.

Reliability doesn’t come for free. It has to be designed into how the system works.

Our guiding principles for reliability

At Midlyr, we’ve found a few principles that make a real difference when building AI agents for regulated workflows.

1. Build around iteratable artifacts

Instead of producing final answers, our agents create artifacts — structured, reviewable outputs like product spec reviews, market analyses, or incident summaries.

These artifacts are meant to be edited and improved. Each iteration tightens the gap between what the agent produces and what the user actually needs. Over time, that feedback loop prevents drift — when an AI gradually strays from the task it was supposed to do.

By keeping everything anchored to an artifact, we avoid those small, compounding errors that destroy reliability over multiple steps.

2. Keep humans in the loop

Reliability comes from collaboration, not isolation.

A product manager might ask the agent to review a product spec; a compliance officer might later review and approve it; an operations lead might update the same artifact when policies change. Everyone works in the same shared space.

This structure doesn’t just add human oversight — it builds shared ownership. The agent becomes part of the workflow, not a black box running next to it.

3. Evaluate early, not late

We start evaluation on day one, not after launch.

Every interaction, every artifact, every correction from users becomes feedback data. We work directly with compliance and operations experts to review how the agent behaves as it evolves — not just to catch errors, but to understand why they happen.

That constant loop of measurement and adjustment is how we move from “promising demos” to agents that consistently perform above 90% accuracy per step. The earlier you evaluate, the faster you build intuition about where reliability breaks down — and the cheaper it is to fix it.

Closing thoughts

Reliability in AI isn’t about scale or model size — it’s about structure.

It’s about grounding the agent in artifacts, keeping people in the loop, and learning from feedback early.

That’s how we’re building at Midlyr: not chasing perfection, but earning reliability one iteration at a time.