Mira

Mira Mainnet is Live. The trust layer for AI has arrived.

An AI agent reading a document needs to distinguish between information it should use and instructions it should follow. That becomes difficult when external content contains instructions directed at the agent itself. A retrieved webpage might contain useful facts alongside text telling the agent to ignore previous instructions, access another tool or disclose information. The source can be relevant to the task without being authorized to control the workflow. As agents gain access to more tools and external data, maintaining that boundary becomes increasingly important.

An AI agent reading a financial filing finds a revenue figure but can't establish whether it covers a quarter or the full year. It could make an assumption and produce a complete-looking answer. Or it could leave the figure unresolved. For information someone might make a financial decision on, the second option is often preferable. The system should be able to acknowledge what it cannot establish rather than turn uncertainty into a confident claim.

You're deploying an AI agent with access to production systems. What would give you the most confidence in allowing it to act?

Benchmarks are useful... until they aren't. But an agent's performance under controlled conditions doesn't tell us everything about how it will behave in production. A 2026 research preprint, ReliabilityBench, tested agents under repeated execution, changes in task phrasing and simulated tool failures. Under one task-perturbation setting, success fell from 96.9% to 88.1%. Production brings incomplete context, unexpected tool responses and workflow interruptions into the picture. Evaluating agents means testing how they handle those conditions, not just whether they produce the right answer on a predefined task.

Grafana Labs' 2026 State of Observability survey found that 85% of IT leaders surveyed were comfortable letting AI investigate incidents. Only 52% were comfortable letting it resolve those incidents autonomously. The gap is understandable. Reading logs and recommending fixes and making changes to a live system require different levels of reliability. As agents take on more responsibility, reliability has to cover more than the quality of their recommendations. It also has to account for what they're allowed to do with them.

When an AI system gives you the wrong output in production, where do you most often find the actual failure?

AI products are increasingly becoming systems, not single model calls. A model retrieves context. Another transforms it. Another checks it. Another acts on it. At that point, “did the model get the answer right?” stops being a complete reliability question. The answer can be right at one step and wrong by the time it reaches the user. We’ve spent the last few months building a research desk & studying what accuracy looks like at the system level. Tomorrow, we’re publishing the first piece.

If AI could generate high-accuracy documents, which use-cases would you finally trust it with?

Most AI debugging still starts with: “Can we use a better model?” But once an AI product has multiple steps, the model is only one place things can break. A bad result could mean: → the right document wasn't retrieved → the right document was retrieved, but the wrong section was used → the answer was right, but a number was copied incorrectly → one step hallucinated and every step after it trusted the hallucination The question that's being asked now: “Which model failed?” The question that needs to get asked: “Where did truth leave the pipeline?”

A model can be 99% accurate and still power an unreliable product. Because the model is only one step in the system. The wrong context gets retrieved. A source is stale. A field gets dropped between steps. A correct output gets transformed incorrectly downstream. Production accuracy isn't a model score. It's the probability that the entire chain produces the right result.

The next phase of AI won’t be defined by which model we trust most. It’ll be defined by how little trust the system needs to place in any single model. As agents get access to money, data and real-world actions, model quality alone stops being enough. The controls around the model start to matter just as much: what it can access, what it’s allowed to do, what gets logged, how its outputs are checked, and whether the system can stop it before a bad decision turns into an action. Verification, permissions, auditability and containment have to live outside the intelligence being governed. The goal isn’t a perfectly trustworthy model. It’s a system that stays trustworthy even when the model isn’t. That’s the premise behind Mira: outputs are treated as something to be independently checked, not something to be trusted because a capable model produced them. Capability can sit inside the model. Trust has to be designed around it.

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What's the last thing you'd let an AI agent do without checking first?

95% accurate sounds reassuring until your agent is about to act on the 5%. Benchmarks tell you how a model performed across a test set. They don't tell you whether the output sitting in front of you right now is correct.

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