AI models are advancing at a breathtaking pace. Matt Shumer’s recent essay “Something Big is Happening” highlights just how fast the technology is advancing — faster than almost anyone outside the industry can even comprehend.
But one phrase in the essay stopped me. “When I test it, it’s usually perfect.”
In business-critical production applications, “usually” is where things fall apart. And that can have severe consequences. Almost all large companies that have introduced AI have incurred some financial loss, with total combined losses at $4.4 billion, due to compliance problems, flawed outputs, bias or other problems, according to an EY survey.
Right now, AI is very good at hiding those problems. When it fails, it fails persuasively.
This year, AI experimentation has to turn into execution. And that transition is where most programs break.
The 4 Pillars of AI Trust
While AI models are becoming more powerful, 95 percent of generative AI pilots fail to deliver measurable returns due to a lack of reliability. Bridging the gap between experimental pilots and production-ready systems requires shifting focus from model performance to end-to-end system observability. To ensure an AI agent is production-ready, organizations must move beyond just monitoring hallucinations and verify four critical areas:
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Context: Is the agent retrieving accurate, up-to-date data?
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Performance: Are latency and token usage signaling upstream breaks?
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Behavior: Is the agent following logic, such as inventory checks or human escalations?
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Outputs: Are results verifiably accurate against defined standards, not just plausible?
The Real Risk of Overconfident AI
Models are improving constantly and becoming more fluent, so outputs sound more authoritative, whether they’re correct or not. User confidence rises faster than actual reliability. That’s where the real risk lives.
And the gap is getting worse. Some 95 percent of generative AI pilots failed to deliver measurable returns, according to an MIT study. AI’s capabilities are unquestioned. But its reliability is not keeping up, and that gap is where programs stall.
We’ve seen this pattern before with previous waves of technology.
Hallucinations Are the Tip of the Iceberg
When people talk about AI reliability, they usually mean hallucinations. That’s what gets the headlines. But hallucinations are only the most visible symptom of a larger problem.
The actual underlying problem is a lack of visibility into the full system. AI doesn’t sit on top of the data stack. It lives inside it.
Agents query operational layers, assemble context across structured and unstructured data sources, invoke tools and interact with humans in real time. Along the way, they generate telemetry as a byproduct, meaning prompts, traces, tool calls, latency and evaluations. These are signals that reveal whether the system is behaving correctly and without capturing them, teams have no way to trace a bad output back to its source.
When something goes wrong, it’s rarely just a model problem or just a data problem. It’s a system failure across the entire loop. For example, imagine a customer support agent starts producing slow or degraded responses. It could be caused by a data freshness incident that delayed the context it was retrieving. It could be an engineer who updated a system prompt. It could be a dropped field that broke a scheme upstream. The symptom looks the same from the outside. But only end-to-end visibility tells you what the underlying problem actually is.
Complicating matters further, AI models paradoxically often sound more confident when they’re wrong. So, the less reliable a model’s conclusions are, the more confidently it may present them to the user.
The AI Input/Output Contract
To build reliability, you have to establish and measure trust on both sides of the system boundary, not just on outputs. AI is fundamentally an input/output system. Trust comes from enforcing contracts at both ends.
Reliable inputs ensure the agent’s assumptions hold up. Verifiable outputs ensure the system’s behavior is observable and auditable. Together, they provide traceability, reproducibility and bounded failure modes. This matters because small problems don’t stay small. A slightly stale data source feeds a slightly off retrieval, which produces a confidently wrong output. Each layer inherits the errors of the ones before it and adds its own.
AI agents operate in a loop. They ingest data, documents, embeddings, system state and user context. Processing then includes context assembly, reasoning and tool execution. Finally, they produce outputs: decisions, responses and actions. They also generate telemetry — prompts, traces, tool calls, latency and evaluations.
All of these signals must be observed alongside data quality. Many teams monitor outputs alone, and that leaves them flying half-blind.
Trusted outcomes require closing the loop between inputs and outputs. When both sides are instrumented and connected, AI systems move from probabilistic to bounded, from opaque to observable and from experimental to production-ready.
Building Reliability Into AI Systems
Many teams treat observability, which is the ability to see inside a system in real-time, trace what data went in, how the agent processed it and why it produced the output it did, as something to bolt on after AI breaks in production. That approach is a recipe for disaster.
The emerging AI stack has four layers: data, semantic, agent-build and trust. But the trust observability layer isn’t the last step. It’s the connective tissue that runs through all the others. It validates the data agents rely on, explains the semantics that contextualize it, monitors agent behavior and verifies outputs.
To make production AI work, teams must bake data and AI observability into the architecture from the start.
4 Factors to Consider Now for Your AI Program
More powerful AI isn’t the answer. It’s AI that your organization can actually trust in production. Four questions determine whether you’re ready:
1. Context
Is the agent retrieving the right context, and is that context correct? If your agent is pulling from a knowledge base that hasn’t been updated in three days or grabbing the wrong document because an embedding has drifted, every output built on top of that retrieval is compromised, even if the model itself is working perfectly.
2. Performance
Is the agent operating efficiently? A customer support agent that suddenly is taking twice as long to respond is a cost problem as well as the first sign that something upstream has broken. Latency, token usage and error rates are reliability signals.
3. Behavior
s the agent behaving as intended? An agent that’s supposed to check inventory before confirming an order or escalate to a human before making a high-value decision needs to be verified as actually doing those things in the right order, every time. Behavioral drift is often invisible until it causes a serious mistake.
4. Outputs
Are the outputs correct for their purpose — not just plausible, but verifiably accurate? This means more than checking whether a response sounds coherent. Evaluate whether the agent did what it was supposed to against a defined standard, such as tagging the right content, routing the right customer or providing the right answer.
Without end-to-end visibility, teams are left guessing. And in production systems, guessing is expensive.
Experiment, Adapt, Verify
Shumer’s advice to experiment and adapt is right, up to a point. But there’s a missing ingredient: visibility.
AI progress follows a familiar pattern. Every major technology wave — software, cloud, big data — required reliable infrastructure before it could reach broad enterprise adoption.
AI is no different. The models are powerful. But the infrastructure required to trust their behavior at scale is still catching up. Competitive advantage won’t come from using the most powerful models. It will come from building the most trustworthy systems around them.
