An AI agent can complete a task correctly in a test environment. It finds the right information, calls the appropriate tool, and returns the expected result. Then the same agent encounters a slightly different request in production. The API is slower, the user leaves out a parameter, or a tool returns an unexpected response.<\/p>\n
The model may interpret that response incorrectly and make another decision based on the wrong assumption. The second action fails, and the agent may continue trying to recover without understanding what actually went wrong. Nothing about the underlying model necessarily changed. The environment did.<\/strong><\/p>\n This is the reliability problem with AI agents. A conventional LLM application generally follows a relatively short path from input to model to output. An agent has to do considerably more: interpret the request, create a plan, select a tool, execute an action, interpret the result, decide what to do next, and eventually determine whether the task was actually completed.[1]<\/a><\/p>\n Every additional step introduces another opportunity for failure.<\/p>\n Consider an agent responsible for processing a customer support request. It needs to understand the customer’s problem, retrieve the relevant account information, determine what action is required, call an internal tool, interpret the response, verify the result, and communicate the outcome.<\/p>\n If the final response sounds correct but the agent updated the wrong account along the way, the task was not successful. The language may be perfectly reasonable while the underlying operation is completely wrong.<\/p>\n This distinction is important because agent reliability is an end-to-end property<\/strong>. The quality of the language model is only one part of the system. A strong model can still sit inside an unreliable workflow, especially when several dependent actions have to succeed in sequence.<\/p>\n The problem becomes more apparent as the number of dependent steps increases. Suppose a five-step workflow has a 95% success rate at each individual step. If every step must succeed, the probability of completing the entire workflow is approximately. The calculation is simplified because real systems include retries, dependencies, and recovery mechanisms, but it illustrates a basic engineering reality: small failures at individual stages can compound into a much larger end-to-end reliability problem.<\/strong>[2]<\/a><\/p>\n Production makes that problem harder.<\/p>\n Users provide incomplete inputs. APIs time out. External information changes. Tools return unexpected values. Permissions expire. A model encounters an ambiguous result and has to decide whether to continue, retry, or stop.<\/p>\n And unlike a chatbot that simply generates text, an agent may have permission to take action. It could update a record, create a ticket, send a message, or modify another system. That changes the engineering requirement.<\/p>\n The question is no longer only whether the model can produce the right answer. It becomes whether the entire system can recognise uncertainty, handle failures, limit risky actions, and still complete tasks consistently.<\/p>\n The goal is not an agent that never fails. The goal is an agent whose failures are That is the difference between an agent that works in a demonstration and one that can be engineered for production.<\/p>\n An agent rarely fails because of one dramatic mistake. More often, the problem begins with a small decision that looks reasonable at the time.<\/p>\n Imagine an agent asked to reschedule a meeting. It understands the request correctly but selects the wrong calendar tool. The tool returns a valid response, so the agent assumes the operation worked as intended. It then continues with an incorrect view of the calendar and eventually produces a confident confirmation.<\/p>\n The final response may look normal, but the workflow was not.<\/p>\n This is what makes agent failures difficult to diagnose. The visible error can appear several steps after the original mistake, while everything in between may look like normal execution. By the time the failure becomes obvious, the original cause may already be several decisions behind it.<\/p>\n An agent moves through a chain of decisions rather than producing one response. It has to interpret the request, determine what needs to happen, select an appropriate tool, provide the required parameters, interpret the result, and decide what action should come next.<\/p>\n Each stage introduces a different type of failure.<\/p>\n Planning can fail when the agent misunderstands the task or creates a sequence of actions that cannot actually reach the requested outcome. Tool selection can fail when several tools have overlapping capabilities and the agent chooses the wrong one. Tool execution introduces another class of problems: parameters can be invalid, APIs can time out, services can become unavailable, or a tool can return an unexpected response.<\/p>\n The harder cases occur when the tool works correctly but the agent misunderstands the result<\/strong>.<\/p>\n Consider an inventory system that reports that a product is unavailable at one location but available at another. The API has returned valid information, yet the agent interprets the response as meaning the product is unavailable everywhere. Nothing has technically crashed; the failure happened in the reasoning layer.<\/p>\n This is why agent monitoring cannot stop at traditional system errors. Engineers also need to understand what the agent believed happened and how that belief influenced its next action.<\/p>\n Once an incorrect assumption enters the agent’s working state, later decisions can build on it. A wrong interpretation can lead to another incorrect action, which can produce a new result that appears valid and further reinforces the original mistake.[3]<\/a><\/p>\n Not every failure requires the same response. A failed search can usually be retried. A temporary API timeout may justify another attempt. An ambiguous result may require clarification from the user. But an action involving sensitive information, financial transactions, or an irreversible system change may need human approval instead.<\/p>\n A reliable agent therefore needs clear boundaries around when to retry, when to change strategy, and when to stop<\/strong>.<\/p>\n The system should not only know what to do when everything works. It should also know what to do when something goes wrong\u2014and recognise when continuing would create a bigger problem.<\/p>\n Once the failure points are clear, the next question is how to design an agent so that those failures do not easily cascade.<\/p>\n The answer is not to remove autonomy altogether. An agent needs enough freedom to reason, choose tools, and adapt to unexpected situations. But that freedom has to exist inside a system of explicit boundaries.<\/p>\n A production agent should not have unlimited access to every tool, every piece of data, or every possible action. Its permissions should reflect what the task actually requires.[4]<\/a> Limiting access does not eliminate incorrect decisions, but it can significantly reduce the consequences when they happen.<\/p>\n Consider an agent responsible for handling customer support tickets. It may need to read customer information, search a knowledge base, and create or update a support ticket. It probably does not need unrestricted access to the company’s financial systems or the ability to delete customer records.<\/p>\n The same principle applies to tools. Instead of exposing dozens of capabilities and expecting the model to choose correctly every time, production systems can provide a smaller set of task-specific tools with clearly defined inputs, outputs, and permissions. This makes the agent’s decision space easier to reason about and easier to monitor.<\/p>\n Verification is another important part of controlled execution.<\/p>\n A common mistake is to treat a successful tool call as proof that the intended action occurred. A successful API response only confirms that the request was accepted or processed; it does not necessarily confirm that the system reached the state the agent intended.<\/p>\n If an agent submits an update to another system, it may therefore need to verify the resulting state before continuing. For example, an agent changing a customer’s subscription could check the account again after the update rather than assuming that a successful response means the change was applied correctly.<\/p>\n For higher-impact operations, the system can go one step further and require explicit approval before execution.[4]<\/a> This is particularly useful when an action is difficult to reverse or could have consequences outside the agent itself.<\/p>\n The important principle is that an agent should not blindly trust its own previous action<\/strong>.<\/p>\n Failures are inevitable, so recovery should not be left entirely to the model. A temporary network failure may justify a retry, while a malformed tool response may require a different strategy. Repeated failures may indicate that the agent should stop and escalate rather than continuing indefinitely.<\/p>\n This is where mechanisms such as timeouts, retry limits, fallbacks, validation checks, and human escalation become part of the agent architecture. The objective is not to make the agent incapable of making mistakes. It is to ensure that one mistake does not turn into an uncontrolled sequence of actions.[5]<\/a><\/p>\n A reliable production agent therefore operates within a controlled execution loop where it reasons about the task, takes an action, observes what happened, validates the result, and then decides whether it should continue or stop.<\/p>\n That control layer is what turns an autonomous model into an engineered system.<\/p>\n An agent can look impressive in a demo and still fail when exposed to real users.<\/p>\n The problem is that evaluating an agent like a traditional language model is often not enough. A model can be tested on whether it generates a correct answer, but an agent needs to be evaluated on whether it can complete a task correctly across multiple steps<\/strong>.<\/p>\n That means testing more than the final response.<\/p>\n Consider an agent responsible for resolving a support request. A conventional evaluation might check whether its final response is accurate and relevant. A production evaluation needs to look deeper: did the agent identify the correct task, select the appropriate tool, provide the right parameters, interpret the tool’s response correctly, and verify the result before telling the user that the task was complete?<\/p>\n A failure at any of these stages can make the overall task unsuccessful, even when the final response sounds convincing.<\/p>\n This is why agent evaluation should measure task completion and execution quality<\/strong>, not just language quality.[6]<\/a><\/p>\n Real users also rarely behave like carefully prepared test cases. They leave information out, change their minds halfway through a task, provide contradictory instructions, or ask for something that the available tools cannot safely perform. External systems can introduce their own uncertainty when services become unavailable or return unexpected data.<\/p>\n These situations need to be part of evaluation rather than treated as unusual exceptions.<\/p>\n A useful test set should therefore include both normal tasks and deliberately difficult scenarios: incomplete inputs, ambiguous requests, tool failures, unexpected responses, repeated failures, and situations where the agent should stop instead of continuing.<\/p>\n The goal is not simply to measure how often the agent succeeds. It is also to understand how it behaves when it cannot succeed<\/strong>.<\/p>\n This distinction matters because two agents can have similar task-success rates while behaving very differently when something goes wrong. One might recognise the failure, recover, and ask for clarification. Another might continue making increasingly uncertain decisions while still producing confident responses.<\/p>\nA Good Answer Is Not the Same as a Successful Task<\/h3>\n
\nvisible, bounded, recoverable, and measurable.<\/strong><\/p>\n<\/div>\nWhere Agent Failures Actually Happen<\/h2>\n
\nassumption<\/div>\n
\nchoice<\/div>\n
\ninterpretation<\/div>\n
\naction<\/div>\n<\/div>\n<\/div>\nDesigning Agents Around Controlled Execution<\/h2>\n
Evaluation: Testing the Agent, Not Just the Model<\/h2>\n