Barclays: AI testing, telemetry and kill switches - QA Financial

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Barclays on AI testing, telemetry and kill switches

Michiel Willems

August 11, 2026

This is the first instalment in a two-part QA Financial series examining how UK bank Barclays is preparing, testing and validating autonomous AI agents for deployment inside one of the world’s most highly regulated banking environments.

Part one explores the testing, observability and governance disciplines shaping production-ready AI. Part two will examine why evaluation, production assurance and software quality are increasingly central to any mature AI strategy.

Banks have spent decades refining how they test software before it reaches production. Autonomous AI agents are now forcing them to rethink that discipline.

The challenge is no longer simply proving that a model produces the correct answer. Increasingly, it is about demonstrating that an AI system can be observed, governed, evaluated and, if necessary, stopped.

That shift is becoming increasingly visible across financial services. Earlier this year, the UK’s Financial Conduct Authority brought Barclays, UBS, Lloyds Banking Group and others into its AI Live Testing programme, signalling that regulators are looking beyond model accuracy towards governance, monitoring, human oversight and operational controls inside live environments.

Andy McMahon

Andy McMahon, Principal AI Engineer at Barclays, believes banks need to think the same way. Speaking on The Brave Technologist podcast last month, McMahon offered a rare glimpse into how one of Britain’s oldest banks is approaching autonomous AI inside one of the world’s most tightly controlled technology environments.

Yet rather than discussing ever more capable AI models, he repeatedly returned to subjects that software testing professionals know well: telemetry, observability, evaluation, permissions, production controls and software assurance.

It is a notable change in emphasis. Much of the conversation around agentic AI still revolves around capability, while McMahon’s focus was confidence.

Not confidence that an agent can complete a task in a demonstration, but confidence that it will continue behaving safely once deployed into production.

Observability before intelligence

Ask most organisations about AI agents and the conversation quickly turns to reasoning, planning and autonomy. McMahon starts somewhere else.

"I covered three different core concerns for productionizing agent systems," he explained. "So one was observability." That is not an accidental choice.

For decades, software teams have relied on logs, metrics and monitoring to understand how production systems behave. McMahon argued autonomous AI requires exactly the same engineering discipline, only at a much deeper level.

"So you know how do you capture telemetry metrics, logs, traces for your agent and agentic solutions," he shared.

Those few words may prove more significant than any discussion about larger models or more sophisticated reasoning.

Without telemetry, engineers cannot understand why an agent reached a particular decision. Without traces, they struggle to reconstruct failures. Without observability, testing effectively ends the moment software reaches production.

That represents an important shift. Testing no longer stops when software is released. Instead, production itself becomes part of the testing lifecycle, continuously generating evidence about how autonomous systems behave under real operating conditions.

That philosophy mirrors Barclays’ broader engineering direction. Last year, the bank described how it had embedded testing earlier into development while expanding observability across its engineering practices.

Global Lead for Performance Engineering Manik Sikka argued that observability and shift-left testing had become essential for identifying issues before software reached customers.

McMahon’s comments suggest those same principles are now being extended into autonomous AI.

Testing behaviour, not just functionality

Traditional software testing has largely focused on whether applications perform the functions they were designed to perform.

Autonomous systems introduce a different challenge. Engineers also need confidence that agents will continue operating within acceptable boundaries when conditions change.

For McMahon, safety is not an abstract discussion about existential AI risk. It is a practical engineering problem. "How do you make sure what you’re deploying safe and you can understand how it’s going to behave in the wild?"

That phrase, ‘behave in the wild’, may become one of the defining testing challenges of enterprise AI. Laboratory evaluations reveal how systems respond to expected scenarios. Production introduces unexpected ones.

That is why McMahon places equal emphasis on operational controls. "How can you employ...