AI Agents: The Challenge of Reliability and How to Solve It

Business leaders across industries are witnessing a significant shift in artificial intelligence (AI); from simple tools like chatbots and image generators to a new generation known as agentic AI. Unlike traditional systems that require detailed instructions, these advanced agents can autonomously plan, reason, and execute complex, multi-step tasks.

As highlighted by McKinsey, this marks a move from static, knowledge-based tools to dynamic AI systems capable of managing entire workflows, like marketing, finance, and logistics, with minimal oversight.

The productivity potential is immense. Agentic AI can automate sophisticated knowledge work, operate continuously, and deliver adaptive, personalized outcomes. While the autonomy of agentic AI unlocks new possibilities, it also requires scrutinization for implementation.

Organizations must decide where and how to integrate these agents, ensuring the right balance of human oversight to minimize errors and maintain reliability. The real challenge lies not in autonomy itself, but in designing processes that align AI decisions with organizational goals and compliance standards.

This shift calls for deliberate leadership. Executives must evaluate where AI agents can truly add value, when human oversight is non-negotiable, and how to structure guardrails that keep autonomy aligned with business intent. This blog unpacks the reliability challenges of agentic AI and offers a path toward responsible, scalable integration.

“What started as AI chatbots and image generators are becoming, AI “agents”- AI systems that can execute a series of tasks without being given specific instructions. No one knows exactly how all this will play out, but details aside, leaders are considering the very real possibility of wide-scale organizational disruption. It’s an exciting time. ”

Reid Blackman

CEO Virtue

AI Agents and the Rise of Machine-Led Decisions

Agentic AI serves the original purpose of AI, which is autonomous decision-making.  Organizations are now delegating intent to AI agents, tasking them with goals rather than step-by-step instructions. These agents interpret objectives, weigh trade-offs, and choose actions based on context, data, and learned behavior. It’s not just software execution, what we are seeing today is machine-led decision-making in action.

Once intent is delegated, AI agents must navigate a range of situations to fulfill their objectives. The scope of decision-making expands significantly. The agents may have to act in ambiguity, draw insights from multiple systems, and operate without clear human instruction. There arises a growing question of reliability through all of these situations.

1. Decision Latitude: AI Agents that Act Without Asking

AI agents are no longer passive executors they hold the discretion to decide what action to take, when to take it, and how to proceed based on evolving inputs and goals. This decision latitude means they can dynamically choose from multiple valid options, without fixed instructions or human validation. For instance, AI agents can make decisions on whether to refund a customer, escalate a complaint, or wait for more data, based on a combination of real-time sentiment analysis, historical interactions, customer value, and company policy.

These choices are made without explicit rules or human approval, reflecting the agent’s ability to weigh trade-offs and optimize for multiple objectives like cost, satisfaction, and resolution speed, all of which are hallmarks of true decision latitude.

What makes this powerful is also what makes it risky: agents can override standard protocols in pursuit of outcomes they interpret as optimal. Their logic is not rule-based but goal-driven, often abstracting human preferences into weighted decisions. There is great flexibility in decision making, but it also opens the door to decisions that are inappropriate, misaligned, or made with incomplete context.

2. Autonomy in Ambiguity: When Agents Operate Without All the Facts

Human decision-making often relies on context, nuance, and lived experience especially in the gray areas where there’s no single right answer. AI agents, however, are expected to act even when information is incomplete, ambiguous, or evolving.

In such moments, agents draw on past interactions, training data, and probabilistic reasoning to interpret intent and decide on a course of action. Without explicit instructions, they infer goals, estimate likely outcomes, and generate responses based on patterns and not certainty. When agents reason through ambiguity, their decisions often escape human understanding which makes it difficult to monitor, guide, or trust the outcomes.

3. Cross-System Autonomy: How Agents Operate Across Systems

Modern AI agents aren’t confined to a single task or dataset. They can integrate across CRMs, ERPs, and cloud platforms, perform data lookups, automate workflows, and trigger business logic. They are multi-modal and multi-domain. That breadth of action increases utility, but also escalates the impact of errors.

With data quality and goal misalignment, even well-tuned agents can act irresponsibly. Flawed training data or outdated inputs can quietly degrade agent performance. For instance, if a sales agent misinterprets regional tax rules in Europe because it was trained on US-centric data, it could trigger compliance violations. Moreover, model drift, where agents gradually become less accurate as data changes, makes this issue a moving target.

As their action space grows, so does the risk of unintended consequences. An agent that’s set loose on CRM data might send a personalized email blast at the wrong time, to the wrong people, with false promises, simply because its training didn’t account for the company’s latest product rollout. Indeed, the more freedom agents are given across systems, the more coordination complexity and audit demands balloon.

4. Human Ownership in Agentic Decision-Making

Even as AI agents execute decisions independently, accountability remains with humans. The responsibility for those decisions, which include ethical, legal, or reputational, still sits with the organization, not the algorithm. This creates a growing operational challenge: how do you maintain meaningful human oversight when agents are acting faster, broader, and with greater discretion than ever before?

A clear example of this tension emerged when Amazon’s scrapped AI hiring tool, which systematically disadvantaged female applicants due to biased training data. This isn’t hypothetical, it’s happening at scale.

In traditional teams, accountability follows action. But in agentic systems, where action is automated and often opaque, oversight must be rethought. Organizations must find new ways to embed human judgment without turning autonomy into micromanagement. That balance between scale and control is not just a technical task; it’s a design problem at the heart of enterprise AI strategy.

Indeed, the more power we give our AI agents, the more governance we owe them. Granting AI the power to act doesn’t mean it understands what’s right. It’s still up to businesses to apply human judgment, because these systems can make decisions, but they don’t yet grasp the consequences.

Training your corporate team in agentic AI is essential for safe and effective integration. It builds AI literacy, teaching employees how to use, monitor, and collaborate with AI agents while understanding their limitations. Focus areas should include prompt engineering, ethical use, data governance, and compliance with regulations.

Role-specific training across departments IT, legal, marketing, and operations ensures responsible deployment. With proper training, your team can confidently leverage AI agents to boost productivity, minimize risks, and support informed, compliant decision-making in daily workflows.

Key Impacts of Machine Agency on Reliability

Challenges Arising from AI Decisions: Why Reliability Is at Stake

AI agents' challenges continue to rise with a rich set of reliability concerns. Key issues include:

• Hallucinations and Accuracy: Generative models often produce outputs that are syntactically fluent but factually wrong. These “hallucinations” can misinform decision-makers. For instance, LLM will confidently fabricate details when it lacks knowledge. In business applications, such errors have real costs.
  
  Analysts warn that AI must be made more “reliable and predictable.” A survey found 61% of companies experienced accuracy issues with their AI tools, and only 17% rated their in-house models as “excellent.” In high-stakes domains (healthcare, finance, legal), even small inaccuracies can violate regulations or damage trust.
• Bias and Fairness: AI agents learn from data reflecting historical biases. Unchecked, they can perpetuate or exacerbate discrimination. For example, an automated hiring agent might favor certain demographics if training data were skewed.
  
  Companies worry that a biased AI agent could lead to legal liabilities. According to a Deloitte survey, over 60% of European respondents expressed concern about the misuse of personal data and AI fairness, and these risks loom especially large in regulated industries. Ensuring fairness requires rigorous data governance and bias mitigation during development.
  
  Further, AI agents are often “black boxes.” McKinsey notes that AI today still “lacks greater transparency and explainability,” which is critical to improve safety and reduce bias. Without clear explanations for their outputs, agents are hard to debug or trust, especially in regulated settings.

• Security Vulnerabilities: Agents can introduce new attack surfaces. According to Darkreading, Microsoft researchers identify novel threats like memory poisoning and prompt injection. In a proof-of-concept, an AI email assistant was “poisoned” via a specially crafted email. The agent incorporated the malicious instruction into its internal memory, then began forwarding sensitive correspondence to an attacker.
  
  In practice, any AI agent with the ability to store or retrieve information must be hardened against such attacks. Reports warn that flawed agents could act as “malicious insiders,” leaking data or taking harmful actions under adversarial influence. Security teams now recommend continuous monitoring and red-team testing of agent behavior to catch these hidden failure modes.

• Opacity and Explainability: Most generative models are black boxes. Even when an agent acts, it often can’t justify its reasoning in human terms. Businesses struggle to trust decisions they cannot audit. In enterprise settings, if AI flags a loan for fraud, stakeholders need to know why, but most systems still can’t explain themselves. This lack of transparency erodes confidence.
  
  Even internally, many organizations report a “trust gap” on AI decisions: for example, only 62% of executives (and 52% of employees) are confident in their company’s ability to deploy AI responsibly. Without clear explanations or provenance, even a technically successful agent might be viewed as too risky.

• Data Quality and Context Gaps: AI agents are only as reliable as their data. Poor or outdated training data can cause repeated failures or skewed outputs. Enterprises face the “garbage in, garbage out” problem at scale: corrupted data sources can quietly undermine an agent’s recommendations. In addition, agents trained on public data may not adapt well to specific corporate environments. For example, an agent lacking context on a company’s internal processes could suggest impractical workflows.
• Operational Consistency: Scaling and maintaining AI agents is expensive. Gartner reports that >90% of CIOs find data preparation and compute costs “limit their ability to get value from AI.” CIOs frequently underestimate AI costs by up to 1,000 % error in their cost calculations; proof-of-concept phases alone can range from $300k–$2.9 M due to compute, data, and maintenance needs.
  
  Transitioning from pilot to enterprise-scale often uncovers hidden costs (e.g., continuous hosting, specialized talent). Besides, AI agents may behave differently day-to-day. Unlike software with fixed logic, a generative agent could produce different valid responses to the same input depending on subtle context or randomness in generation. This nondeterminism complicates validation. In mission-critical applications, stakeholders want consistency. For example, if a finance agent inconsistently interprets policies, audits become difficult.

• Autonomy vs. Oversight: Businesses must calibrate control. HBR authors caution that “too much autonomy” in AI agents can endanger brand, reputation, and even financial stability, while too little autonomy means forgoing potential benefits. Striking this balance, like when to let the agent decide and when to insert a human, is a strategic, context-dependent decision.
  
• Irreversibility and Cascade Failures: When autonomous agents act, the consequences can ripple. An AI mislabeling a supplier’s risk rating might cause a contract termination. An agent mishandling one critical email may kick off a cascade of automated reactions across procurement, legal, and finance.
  The scariest part? These cascades may be undetected until the damage is done, especially if agents talk to each other (as they increasingly do in multi-agent environments).
  Unlike rule-based automation that halts at failure, agents push forward and may double down on bad decisions without oversight.

In summary, trusting an AI agent requires careful mitigation of errors, biases, and security risks. Without safeguards, agentic tools risk more than just annoying mistakes: they can undercut a business’s compliance, safety, and reputation. Organizations must treat reliability as a paramount design goal, not an afterthought.

Enterprise vs Consumer Deployments: Different Stakes

The implications of AI agent reliability vary greatly between consumer and enterprise contexts. In business settings, agent failures can carry far higher stakes. The table below compares key differences:

In practice, enterprises tend to confine AI to “low-stakes” roles until trust is built. Companies often use AI as an “intern” doing assistive tasks (summarizing text, sorting emails) rather than letting it make final decisions. In contrast, consumers already use AI in informal ways without oversight (e.g., home assistants, image filters).

In everyday products (smart home speakers, personal assistants, etc.), reliability impacts user satisfaction. In autonomous vehicles (see case study), faults can mean life or death. The Tesla Autopilot incidents (below) show how quickly consumer trust can vanish if an AI agent fails even a single time in public.

Deloitte surveys reflect this gap: Deloitte finds that only about 57% of people using generative AI in Europe trust its outputs generally, and that drops to 33% among nonusers. Trust is task-dependent: 70% of Europeans trust AI to summarize a news article, but only 50% trust it to write one. In the enterprise, the pattern is even starker: if the task is high-stakes (granting a loan, making a diagnosis), trust plummets.

Finally, while consumers can “vote with their feet” (stop using a bad chatbot), enterprises cannot risk that. A failed enterprise agent could cause regulatory breaches or millions in losses. Thus, enterprises invest heavily in reliability: they budget for testing, oversight teams, and often delay deployment until confidence is earned. This contrasts with consumer AI, where deployment cycles are fast and often driven by innovation hype rather than measured risk management.

Managing AI Governance and Risk Management

Given the high stakes, robust governance and risk management are essential for safe AI agent deployment. Leading experts and institutions recommend a layered approach:

• AI TRiSM (Trust, Risk, Security Management): Gartner’s emerging AI governance framework explicitly lists “reliability” as a core requirement for AI systems. Organizations are establishing AI “audit” teams to continuously test models, simulate adversarial scenarios, and ensure consistent performance over time.
• Regulatory Compliance: Around the world, governments are establishing AI regulations. The EU’s AI Act (effective 2024) is the most comprehensive, classifying many enterprise AI applications as “high-risk.” It mandates lifecycle risk management, high accuracy standards, data governance, transparency, and human oversight for critical systems. For example, EU law requires that high-risk AI (credit scoring, medical diagnostics, etc.) maintain logs of decisions and allow human intervention.
  
  U.S. regulators are also moving: initiatives like NIST’s AI Risk Management Framework (AI RMF) provide voluntary guidelines for identifying and mitigating AI risk. In practice, enterprises must stay abreast of these rules. Even where no law yet exists, best practice is to self-impose similar standards to build trust with customers and avoid future liabilities.
• Structured Risk Frameworks: Organizations should incorporate AI risk into their existing governance. AI governance must integrate with corporate risk management processes. This means creating clear policies and controls: who is responsible for monitoring an agent’s performance, how incidents are reported, etc.
  
  Developing metrics (such as error rates or bias audits) tied to business goals is critical. The NIST AI RMF suggests assessing factors like robustness, safety, privacy, and ethics at each development phase.
• Data Governance and Quality Controls: Many failures start with data. As one risk analyst notes, “garbage in, garbage out” applies acutely to AI. Enterprises should audit training data: ensure it is accurate, relevant to the domain, and free of sensitive or outdated information. Techniques like Retrieval-Augmented Generation (RAG) can improve reliability by grounding an agent on vetted corporate data.
  
  For example, using an internal FAQ database ensures an AI assistant answers customer queries based on the latest policies. Cross-functional teams (including IT, legal, and business units) must collaborate on data labeling, annotation, and review to prevent bias.
• Transparency and Explainability: While full transparency may be impossible for black-box models, partial solutions can help. Deploying agents with audit trails, e.g., logging all inputs and outputs, allows engineers to trace decisions. Some companies build “explainable AI” layers or require models to output confidence scores.
  
  Importantly, organizations are emphasizing transparency as a trust-builder: one survey found that 66% of AI users prioritize data security, while 59% prioritize proven accuracy, and 57% want to understand AI’s decision-making. To meet these needs, firms are creating AI ethics committees and requiring documentation (e.g., system design docs, model cards) to accompany any deployment.
• Independent Audits and Assurance: Some organizations engage third-party auditors to validate AI safety. For instance, the non-profit ForHumanity provides impartial audits of AI systems against risk management standards. Such external reviews can uncover blind spots. Even within a firm, separating design and audit teams (so the builders aren’t the only ones checking work) helps maintain objectivity. Given that many AI components come from external vendors or open models, due diligence on third-party models and libraries is part of good governance.

In practice, implementing these measures is nontrivial. By aligning AI deployment with formal oversight and risk management, businesses can significantly reduce reliability issues. Ultimately, governance is a business enabler: it allows organizations to leverage agents’ power while keeping their promises of fairness, safety, and trust.

Case Study: Tesla Autopilot and Autonomy

Tesla’s Autopilot (semi-autonomous driving) illustrates the high stakes of AI agent reliability. U.S. regulators found it was involved in at least 13 fatal crashes in scenarios where attentive drivers could have intervened. The National Highway Traffic Safety Administration called Tesla an “industry outlier,” noting the system lacked standard safeguards Investigators concluded Tesla “let Autopilot operate in situations it wasn’t designed to” without requiring driver attention. This high-profile failure underscores how even leading tech brands face severe consequences when an AI agent behaves unpredictably.

• Regulatory Critique: The NHTSA report highlighted that Tesla’s design did not enforce driver engagement. It specifically found Tesla “let Autopilot operate in situations it wasn’t designed to,” a lapse that contributed to unavoidable crashes.
• Brand and Safety Fallout: Tesla has faced federal probes, lawsuits, and loss of consumer trust as a result. Even after over-the-air safety patches, consumer watchdogs (e.g., Consumer Reports) warn that misuse of the system continues, demonstrating how hard it is to “fix” an AI policy problem after launch.
• Lesson for AI Leaders: Tesla’s experience is a cautionary tale. It shows that deploying an AI agent without exhaustive testing of edge cases and robust fail-safes can lead to tragedy. Companies are learning that clear communication of an agent’s limits and rigorous safety measures (e.g., monitoring driver attention) are non-negotiable for consumer AI.

Conclusion

AI agents are set to redefine enterprise productivity, automating complex tasks once handled exclusively by people. But this leap from rule-based tools to generative, autonomous systems introduces serious reliability concerns, hallucinations, misjudgments, and security vulnerabilities that can carry real operational and reputational consequences. Trust in AI agents cannot be assumed; it must be earned through rigorous design, oversight, and human-in-the-loop governance.

For enterprises, especially those in regulated industries, the cost of ignoring these risks is steep. Many companies now invest heavily in explainable AI, agent security, and ethical oversight. Another crucial step: investing in team readiness. Business leaders should prioritize agentic AI training for their workforce, ensuring both technical and non-technical teams understand how to deploy, manage, and evaluate AI systems responsibly.

A standout partner in this space is Edstellar, a global, instructor-led training platform offering over 2,000 tailored programs across technical, management, leadership, behavioral, compliance, and social impact disciplines. With tools like Skill Matrix, Stellar AI, and comprehensive skill management software, Edstellar helps organizations upskill at scale.

Companies that prioritize education alongside innovation will be best positioned to harness agentic AI safely and effectively, transforming risk into competitive advantage.