The agentic AI landscape is changing rapidly, but amid all the excitement, the main focus still remains on reliability. But what does reliability actually mean?

Meaning of Reliability for AI agents

AI agent reliability is the consistent ability of autonomous systems to complete intended tasks without causing unintended consequences, even in unpredictable environments.

For agentic AI, failures are often subtle quality regressions rather than hard errors. A customer support agent might provide correct but unhelpful responses, or a research assistant might cite sources that don’t support their claims.

Unlike traditional software that follows predetermined execution paths, agents make non-deterministic decisions that create entirely new categories of failures.

This challenge increases in multi-agent systems where coordination failures can cascade across shared models and interconnected workflows. When one agent makes a poor decision, the error propagates through the entire network, resulting in system-wide failures.

The stakes are rising as more and more agents handle increasing complex business functions.

Key Dimensions of AI Agent Reliability

Consistency: The agent should produce similar and logical outcomes when presented with similar inputs, even if the wording or context varies slightly. This is challenging because the same input can lead to different valid thought processes or actions.

Accuracy/Groundedness: The agent must consistently deliver the correct answer or complete the correct action and avoid generating factually incorrect or fabricated information or hallucinations.

Robustness & Resilience: The agent must maintain its performance and not fail miserably when faced with messy, unexpected, or adversarial inputs such as typos, incomplete forms, or security prompts. It must also have fallbacks and error-handling to recover from system failures or API call errors.

Policy Alignment: The agent must operate within defined safety and compliance boundaries never violating business rules, generating toxic content, or leaking sensitive data.

Auditability & Explainability: It must be possible to trace every step the agent took, which tool it used, and why it made a specific decision. This is vital for debugging, compliance, and building human trust.

Things impacting the reliability of an AI agent

Acting without asking

Gone are the days when AI agents were just passive executors, now they hold the discretion to decide which action to take, how to take based on the inputs and goals. They can choose from multiple valid responses without fixed instructions. For example, 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.

We make decisions using context, nuance, and life experience in the areas where there’s no single right answer. AI agents don’t have this lived experience; they are expected to perform even when they have incomplete information.

In these ambiguous situations, AI agents pull data from its past interactions and training using a method called probabilistic reasoning. We might not be able to trace the reasons behind the final choice of the AI agent, which makes it further more difficult to monitor, guide, or fully trust its resulting actions

Operating across systems

Modern AI agents are not limited to one device. They can integrate different CRMs, devices, apps etc. They are multi-modal and multi-domain. That breadth of action increases utility, but also escalates the impact of errors.

Security Vulnerabilities

Microsoft researchers found threats like memory poisoning and prompt injection. An email can contain hidden prompts that act as instructions for the agent’s memory, leading to serious cybersecurity risks.

Operational Consistency

Scaling and maintaining AI agents is expensive. Transitioning from pilot to enterprise-scale often uncovers hidden costs (e.g., continuous hosting, specialized talent).

Reliability of Browser Agents vs Mobile Agents

Aspect	Browser Agents	Mobile Agents
Core Function	Takes a user prompt, breaks it into a plan, and executes it through web interactions.	Operates similarly, but can also perform actions within the OS.
Environment Complexity	Faces high complexity due to diverse and ever-changing website structures.	Operates in controlled mobile OS environments, though cross-app operations can be tricky.
Anti-Bot Defenses	Easily detected by CAPTCHAs, IP blocks, and bot-detection systems on websites.	Less exposed to anti-bot systems since interactions are within mobile environments.
Clutter & Noise	Dynamic pop-ups, banners, and overlays can confuse navigation and decision-making.	Mobile UI is cleaner and more standardized, reducing distraction during task execution.
Security Risks	High: vulnerable to prompt injection, web exploits, and malicious web scripts.	Lower, as mobile OS sandboxing limits exposure to external web threats.
Cross-App Context Handling	Limited as it operates in a single browser session; context often lost between tabs or pages.	Difficult but possible, as managing state across apps is a major reliability challenge.
Testing Flexibility	Testing limited to browser variations (Chrome, Firefox, etc.).	Easier to test using emulators and cloud-based devices across different OS versions.
Functional Scope	Restricted to browser-based tasks.	Dual functionality — they can perform browser tasks and native mobile tasks.
Overall Reliability	Often less reliable due to unpredictable web environments and security blocks.	Potentially more reliable with system integration, though still maturing technology.

Conclusion

Mobile agents like Droidrun are emerging as the more reliable option compared to browser agents. While browser agents often fail due to complex, changing web structures, anti-bot defenses, and cluttered interfaces, mobile agents operate in more stable environments with cleaner UIs and fewer external blockers. Though they face challenges in cross-app coordination and reliance on accessibility APIs, their dual functionality and controlled OS integration make them better suited for consistent, long-term reliability.