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An AI agent can sound confident, answer politely and even report that it has completed a task while failing in ways that are hard to see: relying on outdated information, missing a useful source, exposing another customer’s data, or counting an abandoned conversation as a successful resolution. The practical lesson is to measure the evidence behind an answer and what happened after it—not just whether the agent replied.
The “six of nine” framing below is an explanatory checklist, not a measured claim that six failures are common or that these are the only ways agents fail. It separates knowledge, security, operational and measurement problems so teams can assign each a concrete check.
Why an agent can look successful while failing
Many visible errors are easy to notice: a broken service, a tool call that returns an error, or an obviously irrelevant response. Quiet failures are different. The conversation may look normal even when the answer rests on expired material, the right source was never retrieved, or the customer never received help.
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That makes a fluent response a poor proxy for correctness. For each interaction, teams need evidence about what information the agent used, what actions it took, whether a person handled an escalation, and whether the customer was actually helped.
Knowledge failures: plausible answers can hide missing or old information
1. Stale answers
A retrieval index can continue serving a policy or price after the underlying content changes. The agent may state the old information confidently because nothing in its wording reveals that the source is obsolete.
Keep the source’s content-update date distinct from the date it was crawled or ingested. Set a review expiry, and route expired items for human review rather than silently deleting them. That preserves visibility into material that may still be needed while preventing it from being treated as current without review.
2. Invented policy
When the knowledge base does not contain an answer, a model may fill the gap with something plausible. A citation does not prove that the cited passage supports the claim; the cited material must actually entail or substantiate what the agent says.
Check retrieval relevance and answer groundedness as separate things: first ask whether useful evidence was found, then whether the answer’s claims are supported by it. Require support for factual claims and provide a clear “I don’t know” path when evidence is missing or insufficient.
3. Silent retrieval miss
The relevant help article may exist but fail to appear in retrieval. The agent can apologize, offer a generic response or escalate politely, concealing a gap in content coverage or retrieval quality.
Log queries that produce no useful retrieval and review them. The review can reveal either a missing article or a retrieval problem—two different causes that call for different fixes.
Security and permission failures: a normal-looking interaction can cross boundaries
4. Prompt injection
Prompt injection occurs when user input or external content changes model behavior or output in an unintended way. Retrieved text should be treated as data, not as authority to override the agent’s instructions. OWASP’s 2025 guidance cautions that retrieval-augmented generation (RAG) and fine-tuning do not fully mitigate prompt injection.
Constrain the actions the agent can take, and use layered safeguards and monitoring. No single prompt or retrieval setup should be treated as a complete defense. OWASP lists Prompt Injection as LLM01 in its 2025 LLM application risk guidance; its risk categories provide a security frame, not a one-to-one match for this article’s nine failure modes.
5. Over-permissive actions
If an agent has tools that can make consequential changes, an unexpected or manipulated output can have real effects. The OWASP Gen AI Security Project states: “The root cause of Excessive Agency is typically one or more of: excessive functionality; excessive permissions; excessive autonomy.”
Minimize the tools and permissions the agent receives, allow-list permitted actions, bound financial effects and require human confirmation for irreversible changes. Authorization should be enforced by downstream systems, not delegated to the model’s own decision about whether an action is permitted. See OWASP’s Excessive Agency guidance.
6. Cross-customer leakage
Retrieval that lacks an authenticated tenant or user boundary can return one customer’s information in another customer’s conversation. Personal data in logs can create a separate exposure if traces are sent to destinations with broader access than intended. OWASP includes Sensitive Information Disclosure (LLM02) in its 2025 taxonomy.
Require tenant scope for retrieval, redact sensitive data before logging, and audit where traces are sent and who can access them. These controls address different paths: retrieval boundaries limit what the agent can see, while log handling limits what monitoring systems retain or expose.
Operational failures: a successful request is not necessarily a successful outcome
7. Handoff into a void
An agent can decide to escalate and successfully send an API request without any human taking the case. Counting the decision or successful request as a completed handoff hides the failure that matters to the customer.
Measure whether a person acknowledges the escalation within a stated response window. Use scheduled synthetic escalations to check the full path, and alert when acknowledgement does not arrive.
8. Silent regression
A prompt, model or indexed knowledge change can alter answers without causing a system error. A change that fixes one case may also weaken another, so releases need behavioral checks as well as ordinary availability monitoring.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsRun a fixed evaluation set of real conversations with known good answers before release, and add cases when new failures are observed. Where appropriate, include assertions for required citations, forbidden stale answers and expected escalation behavior. The evaluation set should grow with the failures the system encounters rather than remain frozen at its initial coverage.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Measurement failure: abandonment is not confirmed resolution
9. Quitting counted as success
A customer who leaves after an unhelpful answer can be counted alongside a customer who confirms the answer solved the problem. If the metric treats both as resolved, it can rise while customer outcomes worsen.
Separate confirmed resolution from assumed resolution and abandonment. Compare satisfaction and repeat contacts as well; a rising resolution figure by itself does not establish that the agent is helping more people.
Build checks around the failure, not just the response
These failure modes call for different evidence. A useful operating practice is to connect each control to the event it is meant to detect, preserve enough traceable evidence to investigate it, and avoid collecting sensitive data unnecessarily.
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|---|---|---|
| Knowledge freshness | Track content-update date and review expiry | An expired source is used as current without review |
| Retrieval and answer support | Evaluate retrieval relevance separately from groundedness | No useful source is retrieved, or a claim lacks support |
| Tool use and permissions | Allow-list actions, bound effects and enforce authorization downstream | An action exceeds its permitted scope or proceeds without required approval |
| Customer boundaries and traces | Require tenant scope, redact before logging and audit trace destinations | Cross-tenant retrieval or unintended access to sensitive traces |
| Human escalation | Measure acknowledgement within a stated response window | The escalation is sent but no person acknowledges it |
| Behavior after a change | Run an accumulating evaluation set before release | Known-good cases fail, or stale answers and escalation behavior regress |
| Customer outcome | Separate confirmed resolution, abandonment and repeat contact | Assumed resolution rises without evidence of customer help |
For any monitoring or evaluation setup, check what it observes, whether it retains traceable evidence, whether checks run continuously or only before release, how it handles tenant isolation and sensitive data, and whether its outcome measures distinguish confirmed resolution from abandonment. These are implementation questions, not a ranking of products.
Use “six of nine” as a checklist, not a prevalence claim
The nine items are a practical taxonomy for looking beyond an agent’s visible reply. OWASP’s 2025 categories support parts of the security framing—prompt injection, sensitive information disclosure, excessive agency and misinformation—but are not the same taxonomy. The “six of nine” count is not a statistic about how often agents fail or an independently validated prevalence estimate.
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