Managing Agents – the risk no one is talking about (and how to mitigate it)

There was a time when software development was defined by late nights, half-erased whiteboards, and the quiet satisfaction of solving a problem that refused to yield. Today, the glow of that whiteboard has been replaced by something far more responsive: the AI coding agent. Ask a question, describe a feature, and within seconds, a solution appears, often coherent, sometimes elegant, and occasionally…completely absurd.

This shift has been nothing short of transformative. AI agents can scaffold applications, refactor codebases, and even suggest architectural decisions. But beneath this productivity boom lies a quieter, more insidious risk: the erosion of critical thinking. When developers stop questioning outputs and start accepting them as truth, the craft of software engineering begins to dull.

The Rise of Passive Development

AI tools are designed to be helpful, and they are exceptionally good at it. They autocomplete functions, generate boilerplate, and explain complex concepts with remarkable fluency. But their greatest strength, the ability to produce plausible answers quickly, is also their greatest weakness. Plausibility is not correctness.

Developers, particularly under time pressure, can easily fall into a pattern of passive acceptance. The agent suggests a solution; it compiles; it runs. Good enough. The critical step of interrogating the solution, asking why it works, whether it is optimal, or if it introduces hidden risks, gets skipped. Over time, that becomes habit.

When Agents Go Rogue (and Nobody Notices)

Consider a simple example: a developer asks an agent to write a function to determine whether a number is prime. The agent produces a solution that works perfectly for smaller numbers but fails for larger inputs because of an inefficient loop boundary. The developer tests a few examples, sees the expected outputs, and moves on.

Weeks later, the function becomes a performance bottleneck in production.

Or consider something more subtle. An agent is asked to implement authentication middleware. It produces code that appears sound, token parsing, validation checks, error handling, all neatly structured and professionally presented. But buried within the logic is a flaw: expired tokens are not properly rejected under certain conditions.

The developer, reassured by the polish and confidence of the generated code, integrates it without deep review. The vulnerability remains unnoticed until it doesn’t.

Then there are the genuinely peculiar moments. One developer recounts asking an agent to optimise a database query. The agent confidently rewrote it, replacing a straightforward join with a convoluted subquery that, while syntactically valid, increased execution time tenfold. The developer, assuming the agent “knew better”, deployed it.

Only later did they realise they had optimised their system…in reverse.

The Illusion of Authority

Part of the problem is psychological. AI agents present information with confidence. There is no hesitation, no “I’m not sure”, and no visible uncertainty unless explicitly prompted. This creates an illusion of authority.

Humans are wired to trust confident answers.

In traditional development, uncertainty is visible. A colleague might say, “I think this works, but we should double-check.” That hesitation naturally invites scrutiny. An AI agent, by contrast, delivers a fully formed solution with no such cues. The result? Developers lower their guard.

Skill Atrophy in Real Time

The danger is not limited to isolated mistakes. The deeper risk is long-term skill erosion.

When developers rely too heavily on agents, they outsource not just the work, but the thinking behind it. Debugging becomes harder because the underlying logic is not fully understood. Architectural decisions become shallower because they are guided by suggestions rather than reasoning.

Developers, and indeed humans in general, are wired to take the path of least resistance. It is all too easy to accept the recommendation of a highly capable agent, particularly when the answer is delivered so confidently.

Junior developers are especially vulnerable. Instead of wrestling with problems and building intuition, they may default to prompting an agent for answers. The struggle, often uncomfortable but essential, is skipped entirely.

It is the difference between learning to navigate a city and simply following GPS directions. You arrive at your destination either way, but only one approach leaves you with a mental map.

A Tool, Not a Substitute

None of this is an argument against AI in software development. Far from it. Used well, agents can amplify productivity, reduce repetitive work, and even spark creative solutions. The issue is not the tool. It is how we use it.

Healthy scepticism is essential. Every piece of generated code should be treated as a suggestion, not a verdict. Developers should ask:

• Does this actually solve the problem, or merely appear to?
• What assumptions is this code making?
• How would I explain this solution to someone else?

If those questions cannot be answered confidently, that is usually a signal to dig deeper.

Managing the Managers

Ironically, as AI agents become more capable, the role of the developer shifts from builder to manager. But managing agents requires a very different skill set, one that emphasises judgement, curiosity, and the willingness to challenge outputs. Blind trust is not management.

The best developers will be those who can collaborate with AI without becoming dependent on it. They will use agents to accelerate their thinking, not replace it. They will recognise when a solution is elegant, and when it is elegantly wrong.

A Light-Hearted Warning

There is a certain humour in some of the mistakes agents make. The function that sorts numbers alphabetically. The “optimised” loop that runs forever. The API call that confidently references an endpoint that has never existed. These moments are amusing, until they are not.

Because behind each one is a human decision to accept rather than question.

An agent apologised to me the other day, stating: “My bad, I missed that requirement, let me fix that now,” after I pointed out missing functionality. I was dangerously close to accepting the revised change without further thought. It was only the discipline drilled into me over several decades that caused me to pause and review it properly before proceeding.

Bring Back the V Model?

Those old enough to remember the widespread adoption of the V Model during the 1990s will recall one of its core strengths: forcing teams to think about testing and acceptance criteria early, often before development was even complete. That discipline feels increasingly relevant today.

As highlighted earlier, an AI agent confidently presenting a solution will often pass the initial “sniff test”. Developers think through a handful of simple scenarios, see that the code appears reasonable, and allow the change through.

The problem is that complex edge cases are rarely obvious at first glance.

One of the best ways to mitigate this risk is to have detailed testing scenarios already prepared. Strong testing discipline forces developers to challenge assumptions, validate behaviour thoroughly, and think beyond the happy path, precisely the areas where overconfidence in AI-generated outputs becomes dangerous.

Software development has always been as much about thinking as it is about coding. AI agents are changing how we write code, but they should not change how we think about it.

The danger is not that agents will replace developers. It is that developers, lulled by convenience, replace their own judgement. And that is a bug no tool can fix.

I urge organisations to start thinking seriously about mitigation strategies before this becomes a far larger problem in production software than many currently realise.

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