AI agents are moving from demos to production, and the results are sobering. Over the past six months, we have monitored over 2 million agent actions across our design partner deployments. These are not toy examples — they are production agents handling customer service, document processing, financial analysis, code generation, and data pipeline management. And while the success stories are impressive, the failure modes are instructive. We built AgentGuard based on what we learned, and this post shares the five most common failure patterns we observed and how we address them.
The first and most dangerous failure mode is what we call cascading action errors. An agent makes a small mistake in an early step — perhaps misinterpreting a user instruction or selecting the wrong tool — and then compounds that error through subsequent actions. In one case, a customer service agent misidentified a refund request as a product inquiry, then generated a detailed product comparison, followed by a recommendation to upgrade, and finally attempted to initiate a purchase on behalf of the customer. Each individual action looked reasonable in isolation; the error was in the initial interpretation, and every subsequent action made the situation worse. AgentGuard catches these by evaluating action sequences, not just individual actions. If an action is inconsistent with the stated task or contradicts previous actions in the sequence, AgentGuard flags it before execution.
The second failure mode is resource overconsumption. Agents with access to APIs, databases, and external services can generate enormous costs if their actions are not bounded. We observed an agent tasked with competitive analysis that made 4,700 API calls to a paid data service in 12 minutes, generating a bill that exceeded the company's monthly budget for that service. The agent was doing exactly what it was told — gathering comprehensive competitive data — but without any concept of resource constraints. AgentGuard enforces configurable resource limits: maximum API calls per task, spending caps, rate limits, and quotas. These constraints are evaluated pre-execution, so the agent is stopped before it exceeds bounds, not after.
The third failure mode involves scope violation. Agents given broad tool access frequently use tools in ways their operators did not intend. A document processing agent with file system access began organizing files in directories it was not supposed to touch. A data analysis agent with database write access started creating temporary tables that were never cleaned up, eventually filling disk space. A code generation agent with git access pushed commits directly to a production branch. In each case, the agent had the technical capability to perform the action and the action seemed locally reasonable, but it violated the intended scope of the agent's authority. AgentGuard addresses this through explicit scope definitions: you specify exactly which tools an agent can use, which resources it can access, and what actions are allowed versus prohibited.
The fourth failure mode is confidence miscalibration. Agents routinely present uncertain conclusions with the same authority as well-supported ones. A financial analysis agent told a user that a stock would likely increase 15% over the next quarter based on a pattern that appeared in 3 out of 47 historical cases — statistically insignificant, but presented as a confident prediction. AgentGuard includes a confidence calibration layer that evaluates the evidence basis for agent conclusions. When an agent makes a claim, AgentGuard assesses whether the supporting evidence is sufficient for the stated confidence level. If the evidence is thin, the action is flagged for review or the agent is instructed to qualify its output.
The fifth failure mode is what we call temporal inconsistency. Agents making decisions across multiple sessions or long conversations sometimes contradict their own earlier statements or decisions because they lack persistent memory of what they have already said and done. A legal research agent gave contradictory advice about the same contract clause in two sessions, based on which documents happened to be in its context window each time. AgentGuard maintains a consistency log that tracks agent statements and decisions across sessions. When a new action contradicts a previous one, it is flagged for human review, and the agent receives context about its previous position.
Beyond these five patterns, we have identified dozens of less common but equally impactful failure modes. The overarching lesson is that agent reliability is not primarily about model quality — it is about the systems around the model. The model might be excellent at reasoning, but without pre-execution verification, scope enforcement, resource limits, confidence calibration, and consistency tracking, even the best model will fail in ways that erode user trust and create business risk.
AgentGuard implements all of these safety layers through a simple API. Before your agent executes any action, it sends the proposed action to AgentGuard for verification. AgentGuard evaluates the action against your configured rules, checks it for consistency with previous actions, verifies it is within scope, and returns an approval, rejection, or modification recommendation. The entire check takes under 50 milliseconds, adding minimal latency to agent operations.
We are seeing design partners deploy AgentGuard alongside agents from every major framework: LangChain, AutoGPT, CrewAI, custom-built agents. The integration is framework-agnostic because AgentGuard operates at the action level, not the framework level. If your agent can express its proposed action as a structured request, AgentGuard can verify it. We are making our full research dataset available and plan to publish a detailed technical paper on agent failure taxonomy later this quarter.