AI Agents Are Creating a New Challenge for Software Companies: Proving They Can Be Trusted

Photo By: Mushvig Niftaliyev

Consider a refund-processing agent that receives a customer request, updates the account, and closes the case. The API calls succeed, but the refund amount is wrong. The failure wasn’t in the payment endpoint. It was in how the agent interpreted the request before submitting it.

Nothing necessarily broke at the API level. The problem was the decision connecting one action to the next.

That distinction matters as businesses move beyond AI assistants that generate information to systems that can execute tasks. When software can interact with databases, initiate transactions, or make changes across applications, a mistake can have consequences beyond an inaccurate response.

The challenge for companies is no longer simply demonstrating that AI agents can complete tasks. It is establishing whether those systems can operate reliably, particularly when their behavior changes between deployments.

Enterprise AI Has a Reliability Problem

Developers have long relied on automated testing to identify bugs before software reaches users. AI agents complicate that process because their decisions can depend on changing instructions, external information, available tools, and the surrounding environment.

A workflow that succeeds during development may behave differently when an API returns unexpected information or a user presents a request the system has never encountered.

Research suggests that this is already a major concern for teams deploying agents.

A 2026 study published at the International Conference on Machine Learning examined 20 production-agent case studies and surveyed practitioners working with deployed systems. Reliability emerged as the leading development challenge, while human evaluation remained a common part of assessing agent performance.

The research shows that even with the advances of autonomous software, fully automating the assessment of correct behavior remains difficult.

That uncertainty has business implications. Organizations may rely on agents to reduce manual workloads, but failures can create additional work through investigations, corrections, customer complaints, and operational disruptions.

An agent that completes 99 routine transactions successfully may still create unacceptable risk if the remaining failure involves an unauthorized payment or an irreversible database change. The relevant measure is not only how frequently the system succeeds, but also what happens when it fails.

Turning Agent Failures Into Repeatable Tests

On October 28, BotGauge AI, announced a red-teaming platform creating to help engineering teams investigate potentially unsafe agent behavior and turn discovered vulnerabilities into recurring evaluations.

The company, which is an autonomous QA partner powered by agentic AI, says the platform includes more than 200 adversarial and real-life attack strategies covering scenarios such as prompt injection, policy bypass, tool misuse, and multi-turn manipulation.

Instead of halting operations when an agent yields an unexpected output, the system records the relevant prompts, model outputs, tool calls, which allows an AI model to interact with external systems, APIs, databases, or code execution environments, and execution steps linked to that behavior.

Engineers can then convert meaningful findings into evaluations intended to run against subsequent software releases.

“Agents don’t fail the way traditional software fails. A small change to a model, prompt, or tool can send an agent down a path no one anticipated,” said Pramin Pradeep, Co-Founder and CEO of BotGauge, in the launch announcement.

In a hypothetical customer-support workflow, engineers might investigate an unauthorized refund by reviewing the agent’s instructions, tool calls, and transaction history, then testing whether a subsequent release produces the same result.

BotGauge has described the platform’s capabilities, but its announcement does not provide independent comparative results demonstrating how frequently the system discovers vulnerabilities or prevents recurring incidents.

Still, the approach embodies an important engineering question: Can teams convert unexpected agent behavior into evidence they can use during the next deployment?

Why Reliability Cannot Be Measured by Accuracy Alone

An agent’s ability to complete a task does not necessarily establish whether it will do so consistently or safely.

In a separate 2026 paper, researchers examining AI-agent reliability proposed evaluating systems across four dimensions: consistency, robustness, predictability, and safety. Their analysis of 15 models across two benchmarks found that recent improvements in capability produced relatively modest gains in reliability.

This matters because conventional performance measurements may miss the severity of occasional failures.

A system might achieve a high task-completion rate while behaving inconsistently under slightly different conditions. It might also complete its assigned objective while violating a business rule.

Continuous assessment offers one way to investigate those problems as applications evolve. Changes to prompts, models, tools, or workflows can be tested against previously identified risks rather than relying exclusively on a one-time assessment.

“The goal with the launch is not to simply produce better, high-quality AI Agent evaluation and testing, but to create a feedback loop where every discovered flaw strengthens the system and raises the bar for the next release,” Pradeep said.

That ambition does not eliminate the need for human intervention and management. Automated evaluators can produce incorrect judgments, and engineering teams still need to determine which failures have meaningful operational or security consequences.

Companies also need safeguards that testing alone cannot provide. Such as restricted permissions, approval requirements for consequential actions, and procedures to stop or reverse unsafe operations.

For businesses adopting autonomous systems, these measures increasingly belong in the same conversation as productivity and implementation costs.

For engineering teams, the practical value of continuous evaluation depends on what happens after a failure is detected. Can they reconstruct the agent’s decisions? Identify which safeguards were ineffective? Check whether a correction survives the next model or software update?

BotGauge is positioning its new platform around that workflow. Whether it delivers consistent results will depend on the quality of its evaluations and how teams use the findings.

A completed task is one result. An explanation of how the agent reached it, and what could go wrong on the next run, is another.